Assigning Trust Rating to AI Services Using Causal Impact Analysis


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Reference #: 01617

The University of South Carolina is offering licensing opportunities for Assigning Trust Rating to AI Services Using Causal Impact Analysis


Today, it is very difficult for an AI user to know what the AI service is doing. This leads to users not trusting AI and most developers, who are genuine users and reuse others’ APIs or data, becoming open to liability and risk.

Invention Description:

Our method assigns a label (rating) to AI services in a black-box setting that conveys their behavior related to the trust/reliability of the services. We generate inputs based on known dependencies between its components related to protected variables like gender and race and look for any dependency in the output. Then, we use the degree of causal relationship to assign ratings. The ratings help users understand the AI’s expected behavior and decide the appropriateness of use in their unique context.

Potential Applications:

The AI and cloud industries will be interested in this disclosure. According to Bloomberg, it is a "$422.37+ Billion Global Artificial Intelligence (AI) Market Size Likely to Grow at 39.4% CAGR During 2022-2028.

Advantages and Benefits:

There is no similar alternative today. All AI vendors and platforms hosting AI services will be interested to grow the AI market. We assign principled labels (ratings) based on the dependency of inputs on outputs, and they have precise semantics. It improves users’ and developers’ trust in AI services being used and developed.


For licensing information contact:

Nikki Biagas, Licensing & Compliance Manager-

UofSC Technology Commercialization Office-


Patent Information:
Software and Computing
For Information, Contact:
Technology Commercialization
University of South Carolina
Biplav Srivastava
Kausik Lakkaraju
Marco Valtorta
artificial intelligence
black-box testing
causal models
software testing
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