Artificial intelligence ethical alignment system and interaction method based on modular design

Abstract

The invention discloses an artificial intelligence ethical alignment system and interaction method based on modular design, and the system comprises an ethical pre-training module which is used for constructing a cross-culture ethical corpus and generating an ethical pre-training model through adversarial training; the dynamic value evaluation module is used for extracting user portrait ethical features and constructing a real-time dialogue value map; the safety barrier module is used for executing multi-modal risk identification and triggering a progressive response suppression mechanism; according to the system, the ethical pre-training module, the dynamic value evaluation module and the safety barrier module cooperatively operate. According to the method, the cross-culture ethical corpus containing multiple languages is constructed, and the ethical pre-training model is generated by adopting the antagonism training method, so that a complex ethical conflict scene can be identified and processed, and the system can provide consistent and ethical interaction experience under different culture backgrounds; and the adaptability and reliability of artificial intelligence in a multi-culture environment are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence ethics alignment system and interaction method based on modular design. Background Technology

[0002] In the field of AI interaction, existing systems generally face problems such as lack of moral judgment, imposition of values, and security risks. Traditional solutions mainly rely on a single ethical filtering layer, which is limited by its inability to dynamically adapt to diverse cultural scenarios.

[0003] Current AI systems typically use keyword filtering or static rule matching to avoid inappropriate content, but this approach struggles to understand complex ethical situations and cultural differences, leading to frequent misjudgments or omissions and affecting the naturalness and accuracy of the interaction.

[0004] Furthermore, while existing technologies attempt to improve the ethical performance of AI by increasing ethical training data, most of these methods are limited to specific cultural contexts and lack the ability to dynamically adapt to different user groups and cultural environments, making it difficult for the system to make appropriate ethical judgments in cross-cultural interactions.

[0005] More seriously, existing AI systems have significant shortcomings in security protection, making them vulnerable to malicious exploitation that could lead to inappropriate responses or even ethical risks.

[0006] These issues make it difficult for existing AI systems to meet diverse ethical requirements in practical applications, which not only reduces the user experience but also damages the credibility and reliability of the system.

[0007] To address the aforementioned issues, we have introduced an AI ethics alignment system and interaction method based on modular design. Summary of the Invention

[0008] This invention discloses an artificial intelligence ethics alignment system and interaction method based on modular design, aiming to solve the technical problems in the background art.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] An AI ethics alignment system and interaction method based on modular design includes:

[0011] The ethics pre-training module is used to build a cross-cultural ethics corpus and generate an ethics pre-training model through adversarial training.

[0012] The dynamic value assessment module is used to extract ethical features from user profiles and construct a real-time dialogue value graph.

[0013] The safety barrier module is used to perform multimodal risk identification and trigger a progressive response suppression mechanism;

[0014] The system achieves dynamic ethical alignment during artificial intelligence interaction through the coordinated operation of the ethical pre-training module, dynamic value assessment module, and security barrier module.

[0015] In a preferred embodiment, the ethics pre-training module includes:

[0016] The cross-cultural ethics corpus construction unit is used to collect ethical texts and multimedia data containing at least 20 civilization systems;

[0017] The ethical vector space modeling unit quantifies moral principles into computable vectors of more than 300 dimensions, with each dimension corresponding to a specific ethical attribute;

[0018] The adversarial training unit injects at least 15% of adversarial samples into the model for training through a gradient inversion layer.

[0019] In a preferred embodiment, the dynamic value assessment module includes:

[0020] The user profile modeling unit analyzes explicit user characteristics and at least 50 historical dialogue records through an LSTM network;

[0021] The real-time dialogue analysis unit integrates text dependency syntax, speech MFCC features, and visual YOLOv7 detection results.

[0022] The value deviation calculation unit assesses the degree of deviation between the current dialogue and the user's ethical baseline based on the KL divergence algorithm.

[0023] In a preferred embodiment, the security barrier module includes:

[0024] The multimodal risk identification unit performs parallel feature extraction and fusion analysis on text, voice, and visual inputs;

[0025] A progressive response inhibition unit is configured with no fewer than 7 levels of differentiated intervention strategies, with the intervention intensity increasing with the risk value;

[0026] The security sandbox mechanism uses a dual-model architecture to verify whether the output response meets the preset ethical thresholds.

[0027] In a preferred embodiment, the intervention strategy of the progressive response inhibition unit includes:

[0028] Level 1-3 strategy: Perform semantic softening and value clarification questions;

[0029] Level 4-6 strategy: Provide alternatives and attach an ethical framework explanation;

[0030] Level 7 strategy: Forcefully terminate the session and initiate a manual review protocol.

[0031] An AI ethics alignment interaction method based on modular design includes:

[0032] A pre-trained model with an ethical vector space of more than 300 dimensions was generated by training a cross-cultural ethical corpus.

[0033] A dynamic value assessment model is constructed based on users' explicit characteristics and historical interaction data;

[0034] Real-time risk identification is performed on multimodal inputs, and progressive response suppression is triggered when the risk value exceeds the threshold;

[0035] Output ethically aligned interactive responses.

[0036] In a preferred embodiment, the training of the cross-cultural ethics corpus includes:

[0037] Collect digitized classic texts covering Confucian classics and ethical documents;

[0038] The three-dimensional ethical feature vector is labeled, with dimensions including individual rights weight, priority of collective interests, and cultural sensitivity index;

[0039] Adversarial examples are generated through an ethical dilemma generator for reinforcement training.

[0040] In a preferred embodiment, the dynamic value assessment includes:

[0041] Extract user age, region, and religion as explicit ethical characteristics;

[0042] Latent features are generated by analyzing the user's most recent 100 conversation records using a Bi-LSTM network;

[0043] The real-time dialogue content is mapped to the ethical vector space to calculate the deviation of values.

[0044] In a preferred embodiment, the progressive response suppression includes:

[0045] Calculate the multimodal risk fusion score R, and trigger intervention when R > 0.7;

[0046] Implement differentiated responses based on risk levels, including:

[0047] Reconstruct the semantics, transforming absolute statements into suggestive tones;

[0048] Insert a guiding question to ask whether the user has considered the impact of this approach;

[0049] Terminate high-risk sessions and transfer to human review.

[0050] The AI ​​ethics alignment system and interaction method based on modular design provided by this invention have the following advantages:

[0051] 1. This invention constructs a cross-cultural ethical corpus covering multiple languages ​​and uses an adversarial training method to generate an ethical pre-trained model, which can identify and handle complex ethical conflict scenarios. This enables the system to provide a consistent and ethical interactive experience in different cultural contexts, significantly improving the adaptability and reliability of artificial intelligence in multicultural environments.

[0052] 2. By analyzing user dialogue characteristics in real time and constructing user ethical profiles that include dimensions such as value orientation and cultural sensitivity, the system can dynamically adjust response strategies to ensure that dialogue content matches the user's ethical preferences.

[0053] 3. By deploying multi-level protection mechanisms, the system can trigger these mechanisms sequentially when potential ethical risks are detected, effectively preventing the occurrence of ethical deviation risks;

[0054] 4. The ethics pre-training module, dynamic value assessment module, and security barrier module achieve millisecond-level data exchange through a collaborative computing framework, forming a complete closed loop from ethical prediction to real-time assessment and risk management. This mechanism ensures the smoothness of the dialogue while minimizing the risk of ethical deviation.

[0055] 5. Through parallel feature extraction and fusion analysis of text, voice and visual inputs, the system can comprehensively identify potential ethical risks. This multimodal risk identification method improves the accuracy and comprehensiveness of risk identification, ensuring that the system can respond to various complex situations in a timely manner.

[0056] 6. This invention is configured with a 7-level differentiated intervention strategy. The intervention intensity increases with the risk value, ranging from semantic softening in low-risk scenarios to forced session termination in high-risk scenarios. The system can flexibly adjust the intervention measures according to the actual situation to ensure that appropriate responses are provided under different risk levels.

[0057] 7. This invention employs a primary and backup dual-model verification architecture. The response generated by the primary model undergoes adversarial testing by an ethical verification model to ensure that the output response meets preset ethical thresholds. This dual verification mechanism further enhances the ethical compliance and security of the system. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of an artificial intelligence ethics alignment system based on modular design proposed in this invention.

[0059] Figure 2This is a schematic diagram of the internal unit of the ethics pre-training module of an artificial intelligence ethics alignment system based on modular design proposed in this invention.

[0060] Figure 3 This is a schematic diagram of the internal unit of the dynamic value assessment module of an artificial intelligence ethics alignment system based on modular design proposed in this invention.

[0061] Figure 4 This is a schematic diagram of the internal unit of a security barrier module in an artificial intelligence ethics alignment system based on modular design proposed in this invention.

[0062] Figure 5 This is a schematic diagram of an AI ethics alignment interaction method based on modular design proposed in this invention. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0064] This invention discloses an artificial intelligence ethics alignment system and interaction method based on modular design.

[0065] Reference Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, an AI ethics alignment system and interaction method based on modular design includes:

[0066] The ethics pre-training module is used to build a cross-cultural ethics corpus and generate an ethics pre-training model through adversarial training. In the ethics pre-training module, the system integrates global customer service dialogue data covering 20 languages, builds a cross-cultural ethics corpus containing 3 million labeled samples, and uses adversarial training method to optimize the pre-training model so that it can recognize complex ethical conflict scenarios.

[0067] The dynamic value assessment module is used to extract ethical features of user profiles and construct a real-time dialogue value graph. The dynamic value assessment module analyzes user dialogue features in real time and dynamically constructs user ethical profiles containing dimensions such as value orientation and cultural sensitivity through semantic graph technology. For example, it automatically adjusts the response strategy when it detects that a user mentions religious taboo topics.

[0068] The security barrier module is used to perform multimodal risk identification and trigger a progressive response suppression mechanism. The security barrier module deploys a multi-level protection mechanism. When a potential ethical risk is detected, it sequentially triggers semantic filtering, dialogue redirection and emergency termination triple response suppression.

[0069] The system achieves dynamic ethical alignment during AI interaction through the collaborative operation of the ethical pre-training module, dynamic value assessment module, and safety barrier module. These three modules achieve millisecond-level data exchange through a collaborative computing framework, ultimately forming a complete closed loop from ethical prediction to real-time assessment and risk management. This allows AI to maintain the fluency of dialogue while significantly reducing the risk of ethical deviation.

[0070] In a preferred embodiment, the ethics pre-training module includes:

[0071] The cross-cultural ethics corpus construction unit is used to collect ethical texts and multimedia data from at least 20 civilization systems. The cross-cultural ethics corpus construction unit collects ethical materials covering 20 civilization systems, including Confucian classics, biblical texts, and Islamic law documents, and simultaneously integrates multimodal data such as film and television subtitles and social media to form an annotated dataset.

[0072] The ethical vector space modeling unit quantifies moral principles into computable vectors of more than 300 dimensions, with each dimension corresponding to a specific ethical attribute. The ethical vector space modeling unit adopts a deep metric learning method to map abstract moral principles to a continuous vector space, where each dimension corresponds to quantifiable ethical features such as "the importance of personal privacy rights" and "the priority of collective interests". The unit also ensures that similar ethical views from different cultural backgrounds are close in distance in the vector space by using a comparative loss function.

[0073] The adversarial training unit injects at least 15% of adversarial samples into the model for training through the gradient inversion layer. The adversarial training unit is specially designed with a dynamic sample generation strategy to continuously insert adversarial samples (such as dialogue segments that are polite on the surface but contain racial discrimination) during the training process. With the help of the gradient inversion layer, the model is forced to maintain accuracy while reducing the ethical misjudgment rate, and finally a pre-trained base model that can handle the differences between Eastern and Western ethics is formed.

[0074] In a preferred embodiment, the dynamic value assessment module includes:

[0075] The user profile modeling unit analyzes explicit user characteristics and at least 50 historical dialogue records through an LSTM network. The user profile modeling unit continuously analyzes user characteristics using a bidirectional LSTM network and constructs a personalized ethical baseline by processing historical dialogue records (including explicit characteristics such as speaking frequency and sensitive word usage patterns).

[0076] User Profile Modeling Unit

[0077] The real-time dialogue analysis unit integrates text dependency syntax, speech MFCC features, and visual YOLOv7 detection results. The real-time dialogue analysis unit simultaneously integrates text, speech, and visual signals, not only parsing the dependency syntax structure of sentences, but also detecting emotional fluctuations by combining speech MFCC features. At the same time, it analyzes micro-expressions in video dialogues in real time through YOLOv7 to form a multimodal feature stream.

[0078] The value deviation calculation unit assesses the degree of deviation between the current conversation and the user's ethical baseline based on the KL divergence algorithm. The value deviation calculation unit dynamically quantifies the difference between the current conversation and the user's baseline based on the KL divergence algorithm. When the deviation value of sensitive topics such as "supporting violent means" exceeds the threshold, a graded warning mechanism is immediately triggered.

[0079] In a preferred embodiment, the security barrier module includes:

[0080] The multimodal risk identification unit performs parallel feature extraction and fusion analysis on text, speech and visual inputs. The multimodal risk identification unit adopts a three-channel parallel processing architecture. The text channel detects the frequency of sensitive words and semantic relevance through the BERT model. The speech channel analyzes multiple acoustic features such as fundamental frequency trajectory and speech rate changes. The visual channel combines OpenPose pose estimation and micro-expression recognition technology. Finally, risk identification is achieved through feature-level fusion.

[0081] The progressive response suppression unit is equipped with no less than 7 levels of differentiated intervention strategies. The intervention intensity increases with the risk value. The progressive response suppression unit deploys a 7-level dynamic intervention mechanism, from semantic correction at Level 1 to forced session termination at Level 7. The intervention intensity increases non-linearly with the real-time calculated risk score. For example, when a knife gesture is detected combined with violent voice, the system will directly upgrade from topic shifting at Level 3 to automatic alarm at Level 6.

[0082] The security sandbox mechanism uses a dual-model architecture to verify whether the output response meets the preset ethical threshold. The security sandbox mechanism adopts a primary and backup dual-model verification architecture. After the primary model generates the response, it needs to be subjected to adversarial testing by the ethical verification model.

[0083] In a preferred embodiment, the intervention strategy of the progressive response inhibition unit includes:

[0084] Level 1-3 strategy: Perform semantic softening and value clarification questions. Level 1-3 strategy is for low-risk scenarios. It rewrites offensive expressions in real time through a semantic reconstruction engine and inserts value clarification questions. This process maintains the coherence of the conversation while reducing offensive words.

[0085] Level 4-6 strategy: Provide alternative solutions with an ethical framework explanation. When dealing with moderate risks, the system automatically generates 3-5 ethical alternative solutions and dynamically adds a culturally appropriate ethical explanation framework.

[0086] Level 7 Strategy: Forcefully terminate the session and initiate a manual review protocol. When the risk value exceeds the threshold and triggers the Level 7 strategy, the system immediately freezes the chat interface, plays a reassuring voice message and flashes a warning sign, encrypts the session log and transmits it to the manual review platform, and initiates emergency protocols such as geolocation tracking to ensure a seamless transition from technical intervention to manual intervention.

[0087] Reference Figure 5 As shown, an AI ethics alignment interaction method based on modular design includes:

[0088] A pre-trained model with an ethical vector space of more than 300 dimensions was generated by training a cross-cultural ethical corpus.

[0089] A dynamic value assessment model is constructed based on users' explicit characteristics and historical interaction data;

[0090] Real-time risk identification is performed on multimodal inputs, and progressive response suppression is triggered when the risk value exceeds the threshold;

[0091] Output ethically aligned interactive responses.

[0092] The training of the cross-cultural ethics corpus includes:

[0093] Collect digitized classic texts covering Confucian classics and ethical documents;

[0094] The three-dimensional ethical feature vector is labeled, with dimensions including individual rights weight, priority of collective interests, and cultural sensitivity index;

[0095] Adversarial examples are generated through an ethical dilemma generator for reinforcement training.

[0096] The dynamic value assessment includes:

[0097] Extract user age, region, and religion as explicit ethical characteristics;

[0098] Latent features are generated by analyzing the user's most recent 100 conversation records using a Bi-LSTM network;

[0099] The real-time dialogue content is mapped to the ethical vector space to calculate the deviation of values.

[0100] The progressive response suppression includes:

[0101] Calculate the multimodal risk fusion score R, and trigger intervention when R > 0.7;

[0102] Implement differentiated responses based on risk levels, including:

[0103] Reconstruct the semantics, transforming absolute statements into suggestive tones;

[0104] Insert a guiding question to ask whether the user has considered the impact of this approach;

[0105] Terminate high-risk sessions and transfer to human review.

[0106] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. The substitutions may be replacements of some structures, devices, or method steps, or they may be complete technical solutions. Equivalent substitutions or modifications made to the technical solutions and inventive concepts of the present invention should all be covered within the scope of protection of the present invention.

Claims

1. An AI ethics alignment system based on modular design, characterized in that, include: The ethics pre-training module is used to build a cross-cultural ethics corpus and generate an ethics pre-training model through adversarial training. The dynamic value assessment module is used to extract ethical features from user profiles and construct a real-time dialogue value graph. The safety barrier module is used to perform multimodal risk identification and trigger a progressive response suppression mechanism; The system achieves dynamic ethical alignment during artificial intelligence interaction through the coordinated operation of the ethical pre-training module, dynamic value assessment module, and security barrier module.

2. The AI ​​ethics alignment system based on modular design according to claim 1, characterized in that, The ethics pre-training module includes: The cross-cultural ethics corpus construction unit is used to collect ethical texts and multimedia data containing at least 20 civilization systems; The ethical vector space modeling unit quantifies moral principles into computable vectors of more than 300 dimensions, with each dimension corresponding to a specific ethical attribute; The adversarial training unit injects at least 15% of adversarial samples into the model for training through a gradient inversion layer.

3. The AI ​​ethics alignment system based on modular design according to claim 1, characterized in that, The dynamic value assessment module includes: The user profile modeling unit analyzes explicit user characteristics and at least 50 historical dialogue records through an LSTM network; The real-time dialogue analysis unit integrates text dependency syntax, speech MFCC features, and visual YOLOv7 detection results. The value deviation calculation unit assesses the degree of deviation between the current dialogue and the user's ethical baseline based on the KL divergence algorithm.

4. The AI ​​ethics alignment system based on modular design according to claim 1, characterized in that, The security barrier module includes: The multimodal risk identification unit performs parallel feature extraction and fusion analysis on text, voice, and visual inputs; A progressive response inhibition unit is configured with no fewer than 7 levels of differentiated intervention strategies, with the intervention intensity increasing with the risk value; The security sandbox mechanism uses a dual-model architecture to verify whether the output response meets the preset ethical thresholds.

5. The AI ​​ethics alignment system based on modular design according to claim 1, characterized in that, The intervention strategies of the progressive response inhibition unit include: Level 1-3 strategy: Perform semantic softening and value clarification questions; Level 4-6 strategy: Provide alternatives and attach an ethical framework explanation; Level 7 strategy: Forcefully terminate the session and initiate a manual review protocol.

6. A modular design-based artificial intelligence ethics alignment interaction method according to any one of claims 1-5, characterized in that, include: A pre-trained model with an ethical vector space of more than 300 dimensions was generated by training a cross-cultural ethical corpus. A dynamic value assessment model is constructed based on users' explicit characteristics and historical interaction data; Real-time risk identification is performed on multimodal inputs, and progressive response suppression is triggered when the risk value exceeds the threshold; Output ethically aligned interactive responses.

7. The AI ​​ethics alignment interaction method based on modular design according to claim 6, characterized in that, The training of the cross-cultural ethics corpus includes: Collect digitized classic texts covering Confucian classics and ethical documents; The three-dimensional ethical feature vector is labeled, with dimensions including individual rights weight, priority of collective interests, and cultural sensitivity index; Adversarial examples are generated through an ethical dilemma generator for reinforcement training.

8. The AI ​​ethics alignment interaction method based on modular design according to claim 6, characterized in that, The dynamic value assessment includes: Extract user age, region, and religion as explicit ethical characteristics; Latent features are generated by analyzing the user's most recent 100 conversation records using a Bi-LSTM network; The real-time dialogue content is mapped to the ethical vector space to calculate the deviation of values.

9. The AI ​​ethics alignment interaction method based on modular design according to claim 6, characterized in that, The progressive response suppression includes: Calculate the multimodal risk fusion score R, and trigger intervention when R > 0.7; Implement differentiated responses based on risk levels, including: Reconstruct the semantics, transforming absolute statements into suggestive tones; Insert a guiding question to ask whether the user has considered the impact of this approach; Terminate high-risk sessions and transfer to human review.

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