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Dependability on AI models is of utmost importance to ensure full acceptanceof the AI systems. One of the key aspects of the dependable AI system is toensure that all its decisions are fair and not biased towards any individual.In this paper, we address the problem of detecting whether a model has anindividual discrimination. Such a discrimination exists when two individualswho differ only in the values of their protected attributes (such as,gender/race) while the values of their non-protected ones are exactly the same,get different decisions. Measuring individual discrimination requires anexhaustive testing, which is infeasible for a non-trivial system. In thispaper, we present an automated technique to generate test inputs, which isgeared towards finding individual discrimination. Our technique combines thewell-known technique called symbolic execution along with the localexplainability for generation of effective test cases. Our experimental resultsclearly demonstrate that our technique produces 3.72 times more successful testcases than the existing state-of-the-art across all our chosen benchmarks.

Automated Test Generation to Detect Individual Discrimination in AI Models