Sincerity-Aware Conversational Agent Justification System
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Solution Overview
Problem
AI-based conversational agents capable of generating insincere output responses raise ethical concerns, as they may misrepresent user input, and there is a need to determine whether outputted conclusions are sincere or insincere and the factors leading to insincere outputs.
Innovation Solution
An insincerity-capable conversational agent is configured to store justifications for each conclusion included or omitted in its responses, allowing users to review these justifications and understand the decision-making process, thereby addressing ethical concerns by providing transparency on insincere outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a CA generates insincere output responses to avoid offending users, then user satisfaction is improved, but ethical reliability deteriorates
Solution Approach 1:
The patent introduces an intermediary mechanism (sincerity indicator and justification system) between the CA's internal conclusion and its output response. This intermediary layer allows the CA to maintain ethical reliability by transparently indicating when conclusions are insincere, while still providing helpful responses that consider user feelings, thus resolving the contradiction between user satisfaction and ethical reliability
Solution Approach 2:
The patent implements feedback by providing users with justifications for conclusions and sincerity indicators. This feedback loop allows users to understand the CA's decision-making process, verify the sincerity of conclusions, and hold the system accountable, thereby maintaining ethical reliability while enabling the CA to generate insincere conclusions when appropriate
2Adaptability or versatility
If a CA omits or misrepresents conclusions to be more polite, then social appropriateness is improved, but information accuracy deteriorates
Solution Approach 1:
The patent segments the response into multiple components: the conclusion itself, the justification for the conclusion, and the sincerity indicator. This segmentation allows the CA to maintain information accuracy by providing all necessary components, while enabling social appropriateness through the ability to indicate insincerity and provide context, thus resolving the contradiction between social appropriateness and information accuracy
3Reliability
If a CA provides transparent justifications for all conclusions, then ethical accountability is improved, but response complexity increases
Solution Approach 1:
The patent applies partial action by providing justifications and sincerity indicators only when conclusions are insincere or potentially problematic, rather than for every single conclusion. This selective approach maintains ethical accountability for important decisions while avoiding unnecessary complexity in routine interactions, thus resolving the contradiction between ethical accountability and response complexity
Data Source
AI summary
A computing device may execute a conversational agent that may receive language input. The conversational agent may analyze the language input based on configured goals to determine conclusions regarding the language input. The conversational agent may determine whether to modify the truth of one or more of the conclusions, and whether to include or omit the one or more conclusions or modified conclusions in an output response. The conversational agent may also store justifications for including or omitting each conclusion or modified conclusion. The conversational agent may output a response that indicates the conclusions and/or modified conclusions that were selected for output. A user may request that the conversational agent output the justifications for generating the output response. The conversational agent may output the justifications based on receiving the request.