Automated Test Generation to Detect Individual Discrimination in AI
  Models
  Dependability on AI models is of utmost importance to ensure full acceptance
of the AI systems. One of the key aspects of the dependable AI system is to
ensure 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 an
individual discrimination. Such a discrimination exists when two individuals
who 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 an
exhaustive testing, which is infeasible for a non-trivial system. In this
paper, we present an automated technique to generate test inputs, which is
geared towards finding individual discrimination. Our technique combines the
well-known technique called symbolic execution along with the local
explainability for generation of effective test cases. Our experimental results
clearly demonstrate that our technique produces 3.72 times more successful test
cases than the existing state-of-the-art across all our chosen benchmarks.
