System

Abstract

An object of the system according to the embodiment is to reduce the bias of the generated AI and generate a result that matches the business habits and culture of each country.SOLUTION: A system according to an embodiment includes a monitoring unit, a data set generation unit, a reinforcement learning unit, and a culture adaptation unit. The monitoring unit monitors a bias of the generated AI. The data set generation unit generates a data set in which diversity and representativeness are respected. The reinforcement learning unit performs reinforcement learning for bias reduction using the data set generated by the data set generation unit. The culture adaptation unit generates a result that matches the business habits and culture of each country.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the AI ​​used to generate the data is susceptible to bias, and there is a risk that the data will be biased against Asians in particular, resulting in unfair services being provided.

[0005] The system of the embodiment aims to reduce bias in the generation AI and generate results that are consistent with the business practices and culture of each country. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a dataset generation unit, a reinforcement learning unit, and a cultural adaptation unit. The monitoring unit monitors bias in the generation AI. The dataset generation unit generates a dataset that respects diversity and representativeness. The reinforcement learning unit performs reinforcement learning to reduce bias using the dataset generated by the dataset generation unit. The cultural adaptation unit generates results that are consistent with the business practices and culture of each country. [Effects of the Invention]

[0007] The system according to the embodiment can reduce bias in the generation AI and generate results that are consistent with the business practices and culture of each country. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The bias reduction system according to the embodiment of the present invention is a system that reduces the bias of the generation AI and generates results that are consistent with the business practices and culture of each country. As a result, the bias reduction system can reduce the bias of the generation AI and generate results that are consistent with the business practices and culture of each country.

[0029] A bias reduction system according to an embodiment includes a monitoring unit, a dataset generation unit, a reinforcement learning unit, and a cultural adaptation unit. The monitoring unit monitors the bias of the generation AI. For example, the monitoring unit periodically checks the output results of the generation AI to ensure that they do not contain bias. The monitoring unit also checks whether the generation AI is producing unfair results for Asians and takes corrective measures if a problem is found. The dataset generation unit generates a dataset that respects diversity and representation. For example, the dataset generation unit collects data reflecting various countries and cultures in the Asian region and uses that data to train the generation AI through reinforcement learning. The dataset generation unit also collects data on the business practices and cultures of Asian countries and trains the generation AI on that data. The reinforcement learning unit performs reinforcement learning to reduce bias. For example, the reinforcement learning unit advances learning by providing appropriate rewards to the generation AI so that it produces unbiased results. The reinforcement learning unit also provides rewards for producing fair results for Asians and penalizes for producing unfair results. The cultural adaptation unit generates results that are consistent with the business practices and cultures of each country. For example, the cultural adaptation unit collects data on the business practices and cultures of Asian countries and trains the generation AI to learn from them. The cultural adaptation unit also uses data that reflects the characteristics of each country, such as Japanese, Chinese, and Indian business practices and cultures, allowing the generation AI to generate results suited to each country. This allows the bias reduction system according to the embodiment to reduce bias in the generation AI and generate results that match the business practices and cultures of each country.

[0030] To monitor bias in generative AI, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to discuss bias detection and improvement measures. For example, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to evaluate the output results of generative AI. For example, experts in technology, ethics, sociology, etc. can gather to discuss bias detection and improvement measures. The monitoring department also detects bias in the output results of generative AI at expert review meetings and identifies the cause. For example, if a specific dataset or algorithm causes bias, it can correct that part. The monitoring department also discusses and implements new methods and technologies for mitigating bias in generative AI at regular review meetings. For example, it can incorporate the latest research results and technological trends to mitigate bias in generative AI. This allows bias detection and improvement measures to be discussed at review meetings with experts from different industries and fields.

[0031] The monitoring unit can build a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. The monitoring unit, for example, builds a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. For example, an alert is issued if a specific keyword or phrase indicates bias. The monitoring unit also uses a real-time monitoring system to detect bias in the output results of the generation AI and immediately issues an alert. For example, it detects bias based on user feedback and issues an alert. The monitoring unit also develops a system that monitors the output results of the generation AI in real time and issues an alert if bias is detected. For example, it uses a bias detection algorithm to detect bias in real time and issues an alert. This makes it possible to monitor the output results of the generation AI in real time and immediately issue an alert if bias is detected.

[0032] The Monitoring Department can expand its monitoring activities of Asian organizations to other regions and promote global bias reduction activities. For example, the Monitoring Department could expand its monitoring activities of Asian organizations to other regions, such as Europe and Africa, and promote global bias reduction activities. For example, it could hold international conferences bringing together experts from each region and build a cooperative system for bias reduction. The Monitoring Department could also monitor bias reduction activities in other regions and collaborate with the activities of Asian organizations. For example, it could share information with bias reduction organizations in Europe and Africa and promote global bias reduction activities. The Monitoring Department could also expand its monitoring activities of Asian organizations globally and integrate bias reduction activities in each region. For example, it could build an international bias reduction network and coordinate bias reduction activities in each region. This could enable the Monitoring Department to expand its monitoring activities of Asian organizations to other regions and promote global bias reduction activities.

[0033] The monitoring unit incorporates multimodal information, including image and audio data, into the bias monitoring activities of the generative AI, enabling more multifaceted bias detection. For example, the monitoring unit incorporates image and audio data into the bias monitoring activities of the generative AI and detects bias based on this multimodal information. For example, it uses image recognition and audio analysis technology to improve the accuracy of bias detection. The monitoring unit also uses multimodal information to detect bias in the output results of the generative AI from multiple angles. For example, it analyzes not only text data but also image and audio data to detect bias. The monitoring unit also uses multimodal information, including image and audio data, to strengthen the bias monitoring activities of the generative AI. For example, it detects signs of bias in image and audio data and proposes improvement measures. This allows for more multifaceted bias detection by incorporating multimodal information, including image and audio data.

[0034] To ensure the diversity of the dataset, the dataset generation unit actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, to ensure the diversity of the dataset, the dataset generation unit actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, the dataset generation unit recruits data providers from Asian countries and collects diverse data. The dataset generation unit also builds a system to recruit data providers from different cultures and backgrounds and have the generation AI learn that data. For example, the dataset generation unit recruits data providers from all over the world through an online platform. To ensure the diversity of the dataset, the dataset generation unit also actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, the dataset generation unit conducts a data collection campaign specialized for a specific culture or background. This allows the data generation AI to actively recruit data providers from different cultures and backgrounds and have the generation AI learn that data.

[0035] The dataset generation unit may periodically review the output results of the generation AI to evaluate the representativeness of the dataset and update the dataset as necessary. The dataset generation unit, for example, may periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, it may check whether the output results contain bias and update the dataset as necessary. The dataset generation unit may also build a system to periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, it may periodically hold review meetings with experts and update the dataset. The dataset generation unit may also periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, if bias is found in the output results, it may identify the cause and update the dataset. This makes it possible to periodically review the output results of the generation AI and update the dataset as necessary.

[0036] The dataset generation unit can apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. The dataset generation unit can apply the dataset that respects diversity and representativeness to other AI models, such as speech recognition and image recognition, to build an unbiased AI system. For example, voice data and image data are collected from diverse datasets and trained on an AI model. The dataset generation unit can also apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. For example, a speech recognition model is trained using data collected from data providers with different cultures and backgrounds. The dataset generation unit can also apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. For example, an image recognition model is trained on a diverse dataset to generate unbiased results. This allows the dataset that respects diversity and representativeness to be applied to other AI models to build an unbiased AI system.

[0037] The dataset generation unit can use crowdsourcing to collect datasets and gather diverse data from all over the world. The dataset generation unit, for example, uses crowdsourcing to collect datasets and gather diverse data from all over the world. For example, it recruits data providers through an online platform and collects diverse data. The dataset generation unit also uses crowdsourcing to collect datasets and builds a system for collecting diverse data from all over the world. For example, it offers rewards to data providers and collects diverse data. The dataset generation unit also uses crowdsourcing to collect datasets and gather diverse data from all over the world. For example, it conducts a data collection campaign specialized for a particular culture or background and collects diverse data. In this way, it is possible to use crowdsourcing to gather diverse data from all over the world.

[0038] The reinforcement learning unit can construct a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, the reinforcement learning unit constructs a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and impose a penalty. The reinforcement learning unit also develops a system that evaluates the results output by the generation AI in real time and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and imposes a penalty. The reinforcement learning unit also develops a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and imposes a penalty. This makes it possible to evaluate the results output by the generation AI in real time and immediately impose a penalty if bias is detected.

[0039] The reinforcement learning unit can improve the reinforcement learning algorithm and set a new reward criterion for producing unbiased results. The reinforcement learning unit, for example, improves the reinforcement learning algorithm and sets a new reward criterion for producing unbiased results. For example, it sets a criterion that gives a high reward when an unbiased result is produced. The reinforcement learning unit also sets a new reward criterion and improves the reinforcement learning algorithm. For example, it sets a reward criterion for producing unbiased results and makes the generation AI learn according to that criterion. The reinforcement learning unit also improves the reinforcement learning algorithm and sets a new reward criterion for producing unbiased results. For example, it sets a criterion that gives a high reward when an unbiased result is produced and makes the generation AI learn according to that criterion. In this way, it is possible to improve the reinforcement learning algorithm and set a new reward criterion for producing unbiased results.

[0040] The reinforcement learning unit can apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. The reinforcement learning unit can apply reinforcement learning for bias reduction to other AI models, such as natural language processing and image generation, to build an unbiased AI system. For example, reinforcement learning is applied to a natural language processing model to generate unbiased results. The reinforcement learning unit can also apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. For example, reinforcement learning is applied to an image generation model to generate unbiased results. The reinforcement learning unit can also apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. For example, reinforcement learning is applied to a natural language processing or image generation model to generate unbiased results. This makes it possible to apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system.

[0041] The reinforcement learning unit can collect feedback from users of different cultures and backgrounds during the reinforcement learning process and reflect it in the learning. For example, the reinforcement learning unit collects feedback from users of different cultures and backgrounds during the reinforcement learning process and reflects it in the learning. For example, feedback from users in Asian countries is collected and reflected in the learning of the generative AI. The reinforcement learning unit also builds a system that collects feedback from users of different cultures and backgrounds and reflects it in the reinforcement learning. For example, feedback is collected from all over the world through an online platform. The reinforcement learning unit also collects feedback from users of different cultures and backgrounds during the reinforcement learning process and reflects it in the learning. For example, a feedback collection campaign specialized for a specific culture or background is conducted. This makes it possible to collect feedback from users of different cultures and backgrounds and reflect it in the learning.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] To monitor bias in generative AI, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to discuss bias detection and remediation measures. For example, experts in technology, ethics, sociology, etc. can gather to evaluate the output of generative AI and detect bias and identify its causes. Furthermore, if a specific dataset or algorithm causes bias, the review meetings can discuss specific methods for correcting that part. Furthermore, the latest research findings and technological trends can be incorporated to implement new methods and technologies for mitigating bias in generative AI. This allows bias detection and remediation measures to be discussed at review meetings with experts from different industries and fields.

[0044] The monitoring unit can build a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. For example, an alert can be issued if a specific keyword or phrase indicates bias. It is also possible to develop a system that detects bias based on user feedback and issues an alert. Furthermore, a system that detects bias in real time and immediately issues an alert can be built using a bias detection algorithm. This makes it possible to monitor the output results of the generation AI in real time and immediately issue an alert if bias is detected.

[0045] The monitoring unit can incorporate multimodal information, including image and audio data, into the bias monitoring activities of the generative AI to perform more multifaceted bias detection. For example, image recognition and audio analysis technology can be used to improve bias detection accuracy. It is also possible to build a system that analyzes not only text data but also image and audio data to detect bias. Furthermore, it can detect signs of bias from image and audio data and propose remedial measures. This allows for more multifaceted bias detection by incorporating multimodal information, including image and audio data.

[0046] To ensure the diversity of the dataset, the dataset generation unit can actively recruit data providers from different cultures and backgrounds and have the generation AI learn from that data. For example, data providers from various Asian countries can be recruited to collect diverse data. A system can also be built to recruit data providers from around the world through an online platform. Furthermore, a data collection campaign specialized for a specific culture or background can be conducted to collect diverse data. This allows data providers from different cultures and backgrounds to be actively recruited and the generation AI to learn from that data.

[0047] The dataset generation unit can periodically review the output results of the generation AI to evaluate the representativeness of the dataset and update the dataset as necessary. For example, it can check whether the output results contain bias and update the dataset as necessary. It can also build a system in which expert review meetings are held regularly and the dataset is updated. Furthermore, if bias is found in the output results, the cause can be identified and the dataset can be updated. This makes it possible to periodically review the output results of the generation AI and update the dataset as necessary.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The monitoring department monitors the bias of the generation AI. For example, the monitoring department periodically checks the output results of the generation AI to ensure that they do not contain bias. It also checks whether the generation AI is producing unfair results against Asian people, and takes corrective measures if any problems are found. Step 2: The dataset generation unit generates a dataset that respects diversity and representativeness. For example, the dataset generation unit collects data that reflects various countries and cultures in the Asian region and uses it for reinforcement learning of the generation AI. The unit also collects data on the business practices and cultures of Asian countries and trains the generation AI on that data. Step 3: The reinforcement learning unit performs reinforcement learning to reduce bias. For example, the reinforcement learning unit trains the generator AI by providing appropriate rewards so that it produces unbiased results. It also rewards the generator when it produces fair results for Asians and penalizes it when it produces unfair results. Step 4: The cultural adaptation unit generates results that are suited to each country's business practices and culture. For example, the cultural adaptation unit collects data on the business practices and cultures of Asian countries and trains the generation AI to learn from it. In addition, by using data that reflects the characteristics of each country, such as Japanese, Chinese, and Indian business practices and cultures, the generation AI can generate results that are appropriate for each country.

[0050] (Example 2) The bias reduction system according to the embodiment of the present invention is a system that reduces the bias of the generation AI and generates results that are consistent with the business practices and culture of each country. As a result, the bias reduction system can reduce the bias of the generation AI and generate results that are consistent with the business practices and culture of each country.

[0051] A bias reduction system according to an embodiment includes a monitoring unit, a dataset generation unit, a reinforcement learning unit, and a cultural adaptation unit. The monitoring unit monitors the bias of the generation AI. For example, the monitoring unit periodically checks the output results of the generation AI to ensure that they do not contain bias. The monitoring unit also checks whether the generation AI is producing unfair results for Asians and takes corrective measures if a problem is found. The dataset generation unit generates a dataset that respects diversity and representation. For example, the dataset generation unit collects data reflecting various countries and cultures in the Asian region and uses that data to train the generation AI through reinforcement learning. The dataset generation unit also collects data on the business practices and cultures of Asian countries and trains the generation AI on that data. The reinforcement learning unit performs reinforcement learning to reduce bias. For example, the reinforcement learning unit advances learning by providing appropriate rewards to the generation AI so that it produces unbiased results. The reinforcement learning unit also provides rewards for producing fair results for Asians and penalizes for producing unfair results. The cultural adaptation unit generates results that are consistent with the business practices and cultures of each country. For example, the cultural adaptation unit collects data on the business practices and cultures of Asian countries and trains the generation AI to learn from them. The cultural adaptation unit also uses data that reflects the characteristics of each country, such as Japanese, Chinese, and Indian business practices and cultures, allowing the generation AI to generate results suited to each country. This allows the bias reduction system according to the embodiment to reduce bias in the generation AI and generate results that match the business practices and cultures of each country.

[0052] The monitoring unit uses an emotion estimation function to analyze the user's emotional response to the output results of the generation AI, and if there are many negative emotions, it can identify the cause and propose improvement measures. For example, the monitoring unit uses the emotion estimation function to analyze the user's emotional response to the output results of the generation AI in real time. For example, it analyzes the user's facial expressions and voice, and if there are many negative emotions, it can identify the cause and propose specific improvement measures. The monitoring unit also uses the emotion estimation function to calculate the user's emotion score for the output results of the generation AI, and if there are many negative emotions, it can identify the cause. For example, if a specific keyword or phrase causes negative emotions, it can correct that part. The monitoring unit also regularly collects the user's emotional response to the output results of the generation AI and performs emotion analysis. If there are many negative emotions, it can identify the cause and propose improvement measures. For example, it can adjust the generation AI's algorithm based on user feedback. This makes it possible to analyze the user's emotional response, identify the cause of negative emotions, and propose improvement measures.

[0053] To monitor bias in generative AI, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to discuss bias detection and improvement measures. For example, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to evaluate the output results of generative AI. For example, experts in technology, ethics, sociology, etc. can gather to discuss bias detection and improvement measures. The monitoring department also detects bias in the output results of generative AI at expert review meetings and identifies the cause. For example, if a specific dataset or algorithm causes bias, it can correct that part. The monitoring department also discusses and implements new methods and technologies for mitigating bias in generative AI at regular review meetings. For example, it can incorporate the latest research results and technological trends to mitigate bias in generative AI. This allows bias detection and improvement measures to be discussed at review meetings with experts from different industries and fields.

[0054] The monitoring unit can build a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. The monitoring unit, for example, builds a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. For example, an alert is issued if a specific keyword or phrase indicates bias. The monitoring unit also uses a real-time monitoring system to detect bias in the output results of the generation AI and immediately issues an alert. For example, it detects bias based on user feedback and issues an alert. The monitoring unit also develops a system that monitors the output results of the generation AI in real time and issues an alert if bias is detected. For example, it uses a bias detection algorithm to detect bias in real time and issues an alert. This makes it possible to monitor the output results of the generation AI in real time and immediately issue an alert if bias is detected.

[0055] The Monitoring Department can expand its monitoring activities of Asian organizations to other regions and promote global bias reduction activities. For example, the Monitoring Department could expand its monitoring activities of Asian organizations to other regions, such as Europe and Africa, and promote global bias reduction activities. For example, it could hold international conferences bringing together experts from each region and build a cooperative system for bias reduction. The Monitoring Department could also monitor bias reduction activities in other regions and collaborate with the activities of Asian organizations. For example, it could share information with bias reduction organizations in Europe and Africa and promote global bias reduction activities. The Monitoring Department could also expand its monitoring activities of Asian organizations globally and integrate bias reduction activities in each region. For example, it could build an international bias reduction network and coordinate bias reduction activities in each region. This could enable the Monitoring Department to expand its monitoring activities of Asian organizations to other regions and promote global bias reduction activities.

[0056] The monitoring unit incorporates multimodal information, including image and audio data, into the bias monitoring activities of the generative AI, enabling more multifaceted bias detection. For example, the monitoring unit incorporates image and audio data into the bias monitoring activities of the generative AI and detects bias based on this multimodal information. For example, it uses image recognition and audio analysis technology to improve the accuracy of bias detection. The monitoring unit also uses multimodal information to detect bias in the output results of the generative AI from multiple angles. For example, it analyzes not only text data but also image and audio data to detect bias. The monitoring unit also uses multimodal information, including image and audio data, to strengthen the bias monitoring activities of the generative AI. For example, it detects signs of bias in image and audio data and proposes improvement measures. This allows for more multifaceted bias detection by incorporating multimodal information, including image and audio data.

[0057] The monitoring unit can use the emotion estimation function to develop an interface for reducing stress and anxiety felt by the user during monitoring activities. The monitoring unit, for example, uses the emotion estimation function to develop an interface for reducing stress and anxiety felt by the user during monitoring activities. For example, the monitoring unit analyzes the user's emotional state in real time and provides feedback for stress reduction. The monitoring unit also develops an interface for reducing stress and anxiety felt by the user during monitoring activities. For example, the emotion estimation function is used to suggest a relaxation method according to the user's emotional state. The monitoring unit also uses the emotion estimation function to build an interface for reducing stress and anxiety felt by the user during monitoring activities. For example, the emotion estimation function monitors the user's emotional state and provides advice for stress reduction. This makes it possible to develop an interface for reducing stress and anxiety felt by the user during monitoring activities.

[0058] The dataset generation unit can use the emotion estimation function to analyze the emotions of the data provider when collecting the dataset, and prioritize the use of data with positive emotions. For example, the dataset generation unit can use the emotion estimation function to analyze the emotions of the data provider in real time when collecting the dataset, and prioritize the use of data with positive emotions. For example, the dataset generation unit can analyze the facial expressions and voice of the data provider and select data with a high positive emotion score. The dataset generation unit can also use the emotion estimation function to analyze the emotional state of the data provider and prioritize the collection of data with positive emotions. For example, the dataset generation unit can prioritize the use of data provided by the data provider in a relaxed state. The dataset generation unit can also build a system that analyzes the emotions of the data provider and prioritizes the use of data with positive emotions. For example, the dataset generation unit can automatically select data with a high emotion score and add it to the dataset. This allows the data provider's emotions to be analyzed and data with positive emotions to be prioritized.

[0059] To ensure the diversity of the dataset, the dataset generation unit actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, to ensure the diversity of the dataset, the dataset generation unit actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, the dataset generation unit recruits data providers from Asian countries and collects diverse data. The dataset generation unit also builds a system to recruit data providers from different cultures and backgrounds and have the generation AI learn that data. For example, the dataset generation unit recruits data providers from all over the world through an online platform. To ensure the diversity of the dataset, the dataset generation unit also actively recruits data providers from different cultures and backgrounds and has the generation AI learn that data. For example, the dataset generation unit conducts a data collection campaign specialized for a specific culture or background. This allows the data generation AI to actively recruit data providers from different cultures and backgrounds and have the generation AI learn that data.

[0060] The dataset generation unit may periodically review the output results of the generation AI to evaluate the representativeness of the dataset and update the dataset as necessary. The dataset generation unit, for example, may periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, it may check whether the output results contain bias and update the dataset as necessary. The dataset generation unit may also build a system to periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, it may periodically hold review meetings with experts and update the dataset. The dataset generation unit may also periodically review the output results of the generation AI to evaluate the representativeness of the dataset. For example, if bias is found in the output results, it may identify the cause and update the dataset. This makes it possible to periodically review the output results of the generation AI and update the dataset as necessary.

[0061] The dataset generation unit can apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. The dataset generation unit can apply the dataset that respects diversity and representativeness to other AI models, such as speech recognition and image recognition, to build an unbiased AI system. For example, voice data and image data are collected from diverse datasets and trained on an AI model. The dataset generation unit can also apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. For example, a speech recognition model is trained using data collected from data providers with different cultures and backgrounds. The dataset generation unit can also apply the dataset that respects diversity and representativeness to other AI models to build an unbiased AI system. For example, an image recognition model is trained on a diverse dataset to generate unbiased results. This allows the dataset that respects diversity and representativeness to be applied to other AI models to build an unbiased AI system.

[0062] The dataset generation unit can use crowdsourcing to collect datasets and gather diverse data from all over the world. The dataset generation unit, for example, uses crowdsourcing to collect datasets and gather diverse data from all over the world. For example, it recruits data providers through an online platform and collects diverse data. The dataset generation unit also uses crowdsourcing to collect datasets and builds a system for collecting diverse data from all over the world. For example, it offers rewards to data providers and collects diverse data. The dataset generation unit also uses crowdsourcing to collect datasets and gather diverse data from all over the world. For example, it conducts a data collection campaign specialized for a particular culture or background and collects diverse data. In this way, it is possible to use crowdsourcing to gather diverse data from all over the world.

[0063] The dataset generation unit can use the emotion estimation function to create guidelines for reducing anxiety and concern felt by data providers when providing data. The dataset generation unit, for example, uses the emotion estimation function to create guidelines for reducing anxiety and concern felt by data providers when providing data. For example, the dataset generation unit analyzes the emotional state of the data provider in real time and provides feedback to give a sense of security. The dataset generation unit also creates guidelines for reducing anxiety and concern felt by data providers when providing data. For example, the emotion estimation function is used to suggest relaxation methods according to the emotional state of the data provider. The dataset generation unit also uses the emotion estimation function to create guidelines for reducing anxiety and concern felt by data providers when providing data. For example, the emotion estimation function is used to monitor the emotional state of the data provider and provides advice to give a sense of security. In this way, guidelines for reducing anxiety and concern felt by data providers when providing data can be created.

[0064] The reinforcement learning unit incorporates an emotion estimation function into the reinforcement learning reward system, and can reward the user when the user produces a result that indicates positive emotion. The reinforcement learning unit, for example, incorporates the emotion estimation function into the reinforcement learning reward system, and can reward the user when the user produces a result that indicates positive emotion. For example, the reinforcement learning unit analyzes the user's facial expressions and voice, and can reward the user when the positive emotion score is high. The reinforcement learning unit also uses the emotion estimation function to improve the reinforcement learning reward system, and can reward the user when the user produces a result that indicates positive emotion. For example, the reinforcement learning unit analyzes the user's emotional state in real time, and can reward the user when the positive emotion score is high. The reinforcement learning unit also incorporates the emotion estimation function into the reinforcement learning reward system, and can build a system that rewards the user when the user produces a result that indicates positive emotion. For example, the reinforcement learning unit monitors the user's emotional state, and can reward the user when the positive emotion score is high. This makes it possible to reward the user when the user produces a result that indicates positive emotion.

[0065] The reinforcement learning unit can construct a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, the reinforcement learning unit constructs a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and impose a penalty. The reinforcement learning unit also develops a system that evaluates the results output by the generation AI in real time and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and imposes a penalty. The reinforcement learning unit also develops a system that evaluates the results output by the generation AI in real time during the reinforcement learning process and immediately imposes a penalty if bias is detected. For example, a bias detection algorithm is used to detect bias in real time and imposes a penalty. This makes it possible to evaluate the results output by the generation AI in real time and immediately impose a penalty if bias is detected.

[0066] The reinforcement learning unit can improve the reinforcement learning algorithm and set a new reward criterion for producing unbiased results. The reinforcement learning unit, for example, improves the reinforcement learning algorithm and sets a new reward criterion for producing unbiased results. For example, it sets a criterion that gives a high reward when an unbiased result is produced. The reinforcement learning unit also sets a new reward criterion and improves the reinforcement learning algorithm. For example, it sets a reward criterion for producing unbiased results and makes the generation AI learn according to that criterion. The reinforcement learning unit also improves the reinforcement learning algorithm and sets a new reward criterion for producing unbiased results. For example, it sets a criterion that gives a high reward when an unbiased result is produced and makes the generation AI learn according to that criterion. In this way, it is possible to improve the reinforcement learning algorithm and set a new reward criterion for producing unbiased results.

[0067] The reinforcement learning unit can apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. The reinforcement learning unit can apply reinforcement learning for bias reduction to other AI models, such as natural language processing and image generation, to build an unbiased AI system. For example, reinforcement learning is applied to a natural language processing model to generate unbiased results. The reinforcement learning unit can also apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. For example, reinforcement learning is applied to an image generation model to generate unbiased results. The reinforcement learning unit can also apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system. For example, reinforcement learning is applied to a natural language processing or image generation model to generate unbiased results. This makes it possible to apply reinforcement learning for bias reduction to other AI models to build an unbiased AI system.

[0068] The reinforcement learning unit can collect feedback from users of different cultures and backgrounds during the reinforcement learning process and reflect it in the learning. For example, the reinforcement learning unit collects feedback from users of different cultures and backgrounds during the reinforcement learning process and reflects it in the learning. For example, feedback from users in Asian countries is collected and reflected in the learning of the generative AI. The reinforcement learning unit also builds a system that collects feedback from users of different cultures and backgrounds and reflects it in the reinforcement learning. For example, feedback is collected from all over the world through an online platform. The reinforcement learning unit also collects feedback from users of different cultures and backgrounds during the reinforcement learning process and reflects it in the learning. For example, a feedback collection campaign specialized for a specific culture or background is conducted. This makes it possible to collect feedback from users of different cultures and backgrounds and reflect it in the learning.

[0069] The reinforcement learning unit can use the emotion estimation function to develop an interface for reducing stress and anxiety felt by a user during the reinforcement learning process. The reinforcement learning unit, for example, uses the emotion estimation function to develop an interface for reducing stress and anxiety felt by a user during the reinforcement learning process. For example, the emotional state of a user is analyzed in real time and feedback for stress reduction is provided. The reinforcement learning unit also develops an interface for reducing stress and anxiety felt by a user during the reinforcement learning process. For example, the emotion estimation function is used to suggest a relaxation method according to the user's emotional state. The reinforcement learning unit also uses the emotion estimation function to build an interface for reducing stress and anxiety felt by a user during the reinforcement learning process. For example, the emotional state of a user is monitored and advice for stress reduction is provided. This makes it possible to develop an interface for reducing stress and anxiety felt by a user during the reinforcement learning process.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] To monitor bias in generative AI, the monitoring department can hold regular review meetings bringing together experts from different industries and fields to discuss bias detection and remediation measures. For example, experts in technology, ethics, sociology, etc. can gather to evaluate the output of generative AI and detect bias and identify its causes. Furthermore, if a specific dataset or algorithm causes bias, the review meetings can discuss specific methods for correcting that part. Furthermore, the latest research findings and technological trends can be incorporated to implement new methods and technologies for mitigating bias in generative AI. This allows bias detection and remediation measures to be discussed at review meetings with experts from different industries and fields.

[0072] The monitoring unit uses an emotion estimation function to analyze the user's emotional response to the output results of the generation AI, and if there are many negative emotions, it can identify the cause and propose improvement measures. For example, it can analyze the user's facial expressions and voice, and if there are many negative emotions, it can identify the cause and propose specific improvement measures. It also uses the emotion estimation function to calculate the user's emotion score for the output results of the generation AI, and if there are many negative emotions, it can identify the cause. Furthermore, by adjusting the generation AI's algorithm based on user feedback, it is possible to eliminate the cause of negative emotions. This makes it possible to analyze the user's emotional response, identify the cause of negative emotions, and propose improvement measures.

[0073] The monitoring unit can build a system that monitors the output results of the generation AI in real time and immediately issues an alert if bias is detected. For example, an alert can be issued if a specific keyword or phrase indicates bias. It is also possible to develop a system that detects bias based on user feedback and issues an alert. Furthermore, a system that detects bias in real time and immediately issues an alert can be built using a bias detection algorithm. This makes it possible to monitor the output results of the generation AI in real time and immediately issue an alert if bias is detected.

[0074] The monitoring unit can incorporate multimodal information, including image and audio data, into the bias monitoring activities of the generative AI to perform more multifaceted bias detection. For example, image recognition and audio analysis technology can be used to improve bias detection accuracy. It is also possible to build a system that analyzes not only text data but also image and audio data to detect bias. Furthermore, it can detect signs of bias from image and audio data and propose remedial measures. This allows for more multifaceted bias detection by incorporating multimodal information, including image and audio data.

[0075] The monitoring unit can use the emotion estimation function to develop an interface for reducing the stress and anxiety felt by the user during monitoring activities. For example, the monitoring unit can analyze the user's emotional state in real time and provide feedback for stress reduction. It can also build an interface that suggests relaxation methods according to the user's emotional state. Furthermore, it can develop a system that monitors the user's emotional state and provides advice for stress reduction. This makes it possible to develop an interface for reducing the stress and anxiety felt by the user during monitoring activities.

[0076] The dataset generation unit uses an emotion estimation function to analyze the emotions of data providers when collecting a dataset, and can prioritize the use of data with positive emotions. For example, it can analyze the facial expressions and voice of the data provider and select data with a high positive emotion score. It can also build a system that prioritizes the use of data provided by data providers in a relaxed state. Furthermore, it can develop a system that automatically selects data with a high emotion score and adds it to the dataset. This allows the emotions of data providers to be analyzed and data with positive emotions to be prioritized.

[0077] To ensure the diversity of the dataset, the dataset generation unit can actively recruit data providers from different cultures and backgrounds and have the generation AI learn from that data. For example, data providers from various Asian countries can be recruited to collect diverse data. A system can also be built to recruit data providers from around the world through an online platform. Furthermore, a data collection campaign specialized for a specific culture or background can be conducted to collect diverse data. This allows data providers from different cultures and backgrounds to be actively recruited and the generation AI to learn from that data.

[0078] The dataset generation unit can periodically review the output results of the generation AI to evaluate the representativeness of the dataset and update the dataset as necessary. For example, it can check whether the output results contain bias and update the dataset as necessary. It can also build a system in which expert review meetings are held regularly and the dataset is updated. Furthermore, if bias is found in the output results, the cause can be identified and the dataset can be updated. This makes it possible to periodically review the output results of the generation AI and update the dataset as necessary.

[0079] The dataset generation unit can use the emotion estimation function to create guidelines to alleviate the anxiety and concerns that data providers feel when providing data. For example, it can analyze the emotional state of data providers in real time and provide feedback to give them a sense of security. It can also create guidelines that suggest relaxation methods according to the data provider's emotional state. Furthermore, it can build a system that monitors the emotional state of data providers and provides advice to give them a sense of security. This makes it possible to create guidelines to alleviate the anxiety and concerns that data providers feel when providing data.

[0080] The reinforcement learning unit incorporates an emotion estimation function into the reinforcement learning reward system, allowing it to reward users when they produce results that reflect positive emotions. For example, it can analyze the user's facial expressions and voice and reward users when the positive emotion score is high. It is also possible to build a system that analyzes the user's emotional state in real time and reward users when the positive emotion score is high. Furthermore, it is possible to develop a system that monitors the user's emotional state and reward users when the positive emotion score is high. This makes it possible to reward users when they produce results that reflect positive emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The monitoring department monitors the bias of the generation AI. For example, the monitoring department periodically checks the output results of the generation AI to ensure that they do not contain bias. It also checks whether the generation AI is producing unfair results against Asian people, and takes corrective measures if any problems are found. Step 2: The dataset generation unit generates a dataset that respects diversity and representativeness. For example, the dataset generation unit collects data that reflects various countries and cultures in the Asian region and uses it for reinforcement learning of the generation AI. The unit also collects data on the business practices and cultures of Asian countries and trains the generation AI on that data. Step 3: The reinforcement learning unit performs reinforcement learning to reduce bias. For example, the reinforcement learning unit trains the generator AI by providing appropriate rewards so that it produces unbiased results. It also rewards the generator when it produces fair results for Asians and penalizes it when it produces unfair results. Step 4: The cultural adaptation unit generates results that are suited to each country's business practices and culture. For example, the cultural adaptation unit collects data on the business practices and cultures of Asian countries and trains the generation AI to learn from it. In addition, by using data that reflects the characteristics of each country, such as Japanese, Chinese, and Indian business practices and cultures, the generation AI can generate results that are appropriate for each country.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit that monitors the bias of the generating AI, a dataset generation unit that generates a dataset that respects diversity and representativeness; a reinforcement learning unit that performs reinforcement learning for bias reduction using the dataset; A cultural adaptation unit that generates results that match the business practices and culture of each country. A system characterized by:

2. The monitoring unit Analyze the user's emotional response to the output of the generation AI, and if there are many negative emotions, identify the cause and propose improvements.

2. The system of claim 1.

3. The monitoring unit To monitor bias in the generative AI, regular review meetings will be held with experts from different industries and fields to discuss detection of bias and improvement measures.

2. The system of claim 1.

4. The monitoring unit Build a system that monitors the output of the AI ​​in real time and immediately issues an alert if bias is detected.

2. The system of claim 1.

5. The monitoring unit Expand monitoring of Asian organizations to other regions and advance global debiasing efforts.

2. The system of claim 1.

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