  The fairness characteristic is a critical attribute of trusted AI systems. A
plethora of research has proposed diverse methods for individual fairness
testing. However, they are suffering from three major limitations, i.e., low
efficiency, low effectiveness, and model-specificity. This work proposes ExpGA,
an explanationguided fairness testing approach through a genetic algorithm
(GA). ExpGA employs the explanation results generated by interpretable methods
to collect high-quality initial seeds, which are prone to derive discriminatory
samples by slightly modifying feature values. ExpGA then adopts GA to search
discriminatory sample candidates by optimizing a fitness value. Benefiting from
this combination of explanation results and GA, ExpGA is both efficient and
effective to detect discriminatory individuals. Moreover, ExpGA only requires
prediction probabilities of the tested model, resulting in a better
generalization capability to various models. Experiments on multiple real-world
benchmarks, including tabular and text datasets, show that ExpGA presents
higher efficiency and effectiveness than four state-of-the-art approaches.
