User-Type AI Ethicality Evaluation for Psychological Consultation
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Solution Overview
Problem
Existing AI-based psychological consultation technologies lack methods for evaluating the ethicality of their output data, which is crucial for providing user-friendly responses and explanations, especially considering user characteristics.
Innovation Solution
An electronic device with a processor that measures the ethicality of AI model outputs based on user types, using explainable AI to classify users and evaluate the ethicality of AI models through criteria such as disposition, virtue, personality, cognitive faculty, and personal environments, and assess the AI's interpretability, transparency, responsibility, bias, and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-based psychological consultation technology is implemented as a black box, then development efficiency and accessibility are improved, but ethicality evaluation and user trust are worsened
Solution Approach 1:
The patent segments the AI model evaluation process into multiple dimensions including disposition, virtue, personality, cognitive faculty, and personal environments. Each dimension is evaluated separately using specific criteria, allowing systematic assessment of ethicality while maintaining the overall black box approach for development efficiency.
Solution Approach 2:
The patent introduces an intermediary evaluation framework that mediates between the black box AI model and users. This framework includes evaluation criteria and user type classifications that serve as intermediaries to assess ethicality without requiring transparency into the model's internal operations.
2Reliability
If AI model outputs are evaluated according to user types, then user satisfaction and ethicality are improved, but system complexity is worsened
Solution Approach 1:
The patent applies local quality by tailoring evaluation criteria to specific user types. Different user types (e.g., those with different personality characteristics or personal environments) are evaluated using customized criteria, allowing targeted ethicality assessment without requiring complete system redesign.
Solution Approach 2:
The patent changes evaluation parameters based on user type characteristics. The evaluation framework adjusts its criteria dynamically according to user attributes such as disposition, virtue, personality, cognitive faculty, and personal environments, enabling adapted evaluation without proportional increase in system complexity.
3Reliability
If explainable AI is used to provide human-friendly explanations, then user trust and ethicality are improved, but computational resources and processing time are worsened
Solution Approach 1:
The patent applies partial action by providing explanations only for specific evaluation dimensions rather than complete model transparency. The system provides human-friendly explanations for key ethicality aspects (disposition, virtue, personality, cognitive faculty, personal environments) without requiring full computational transparency, balancing user trust with resource consumption.
Data Source
AI summary
An electronic device according to an embodiment of the present invention is configured by including: a memory for storing an evaluation criterion related to ethicality of an artificial intelligence model performing a consultation; and a processor that measures an ethicality degree of result data output by the artificial intelligence model according to the evaluation criterion, wherein the ethicality degree is measured according to types of users performing consultations with the artificial intelligence model.