Automated Trust Credential Exchange for AI Risk Scoring
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Solution Overview
Problem
Establishing trust in AI and ML applications is a significant hurdle due to ethical issues, bias, transparency, and morality concerns, and assessing their risk profiles in real time is technically difficult.
Innovation Solution
A data processing service with a control layer and risk scoring module generates trust credentials for AI-driven applications by evaluating risk factors and using a determination engine to provide real-time risk scores and credentials based on standardized frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI and ML applications are widely adopted, then productivity and task performance are improved, but trust and reliability deteriorate due to ethical issues, bias, and transparency concerns
Solution Approach 1:
The patent introduces a trust credential as an intermediary element that mediates between the AI application and its users. The trust credential contains verified information about the AI system's behavior, risk factors, and ethical compliance, allowing users to assess trustworthiness without directly analyzing the complex AI system itself. This resolves the contradiction by providing a tangible trust indicator that enables widespread adoption while maintaining reliability through verified credentials.
Solution Approach 2:
The patent implements preliminary risk assessment and trust credential generation before the AI application is deployed or used. By evaluating risk factors, ethical compliance, and transparency characteristics in advance, the system establishes trust credentials that accompany the AI application. This preliminary action ensures that productivity gains from AI adoption are achieved while reliability is maintained through pre-verified trust information.
2Reliability
If real-time risk assessment is implemented, then trust and reliability are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The patent segments the trust assessment system into distinct modular components: a risk scoring module that evaluates specific risk factors, a determination engine that processes multiple data sources, and a trust credential generator that produces standardized outputs. This segmentation allows real-time risk assessment to be implemented through distributed, specialized components rather than a monolithic complex system, reducing overall device complexity while maintaining reliability.
Solution Approach 2:
The patent changes the parameters of risk assessment by focusing on a defined set of key risk factors and characteristics rather than analyzing all possible system attributes. The risk scoring module evaluates specific parameters such as ethical compliance, bias indicators, and transparency metrics, transforming the complex problem of real-time trust assessment into a manageable set of measurable parameters that can be processed efficiently.
3Measurement precision
If comprehensive risk factor evaluation is performed, then measurement precision of trust assessment is improved, but loss of time and processing duration worsen
Solution Approach 1:
The patent extracts and isolates the most critical risk factors and characteristics that contribute to trust assessment from the comprehensive set of all possible evaluation parameters. The risk scoring module focuses on evaluating specific extracted factors such as ethical compliance indicators, bias metrics, and transparency characteristics, rather than analyzing every possible system attribute. This extraction maintains measurement precision by concentrating on the most relevant factors while reducing assessment time by eliminating unnecessary evaluations.
Solution Approach 2:
The patent applies partial action by evaluating a selected subset of risk factors that are most relevant to trust assessment rather than performing exhaustive analysis of all possible parameters. The determination engine processes key risk factors with appropriate weighting, achieving sufficient measurement precision for practical trust assessment without the time cost of comprehensive evaluation of every conceivable system characteristic.
Data Source
AI summary
A method for generating a trust credential for an AI-driven application is presented. The method includes identifying one or more risk factors, receiving a request to generate a trust credential for an AI-driven application, and receiving the AI-driven application and associated data, wherein the AI-driven application has one or more subcomponents. The method includes applying a risk determination function to each of the one or more subcomponents of the AI-driven application and the associated data to generate a risk score for each of the one or more subcomponents. The method further includes applying a weighting function to the risk score of each subcomponent to generate a trust score for each of the one or more subcomponents, and generating the trust credential for the AI-driven application based on the trust scores of each of the one or more subcomponents.