Front-End UX Component Evaluation for Trustworthy AI Disclosure
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Solution Overview
Problem
Users struggle to comprehend and trust AI model decisions due to insufficient explanation in front-end user experiences, leading to potential bias and regulatory issues.
Innovation Solution
A computer-implemented method evaluates front-end user experience components for trustworthy AI factors like accuracy, explainability, and fairness, and modifies the UX to provide additional information if the initial disclosure does not meet a threshold, ensuring users understand AI model outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional information about trustworthy AI factors is provided in the front-end UX, then user trust and comprehension of AI decisions improve, but the complexity of the UX component increases
Solution Approach 1:
The patent segments trustworthy AI factor information into discrete, evaluable components within the UX. Each UX component is analyzed for specific trustworthy AI factors (accuracy, reliability, explainability, fairness) separately, allowing systematic presentation of trust-related information without overwhelming the user with undifferentiated complexity.
Solution Approach 2:
The system performs preliminary evaluation of UX components to determine trust scores before final presentation. By pre-evaluating whether information adequately conveys trustworthy AI factors and comparing against thresholds, the system prepares and structures trust-related information in advance, reducing the perceived complexity during actual user interaction.
2Reliability
If the UX component is evaluated to meet a threshold of disclosure, then trustworthiness is improved, but the time and resources required for evaluation increase
Solution Approach 1:
The patent implements a feedback mechanism where UX components are evaluated against trust score thresholds, and results are used to determine whether additional information is needed. This automated feedback loop enables efficient, systematic verification of trustworthy AI factor disclosure without requiring manual review of each component, reducing overall evaluation time and resources.
Data Source
AI summary
Described are techniques for a trustworthy artificial intelligence (AI) service. The techniques include identifying a user experience (UX) component in a front-end UX containing information that conveys a trustworthy AI factor. The techniques further include evaluating the information contained in the UX component to determine a trust score for the UX component that indicates a degree to which the information contained in the UX component conveys the trustworthy AI factor. The techniques further include determining, based on the trust score for the UX component, that the information contained in the UX component does not meet a threshold of disclosure of the trustworthy AI factor. The techniques further include obtaining an alternative UX component containing additional information that meets the threshold of disclosure of the trustworthy AI factor and providing the alternative UX component for incorporation into the front-end UX of the application.