  Fairness testing aims at mitigating unintended discrimination in the
decision-making process of data-driven AI systems. Individual discrimination
may occur when an AI model makes different decisions for two distinct
individuals who are distinguishable solely according to protected attributes,
such as age and race. Such instances reveal biased AI behaviour, and are called
Individual Discriminatory Instances (IDIs).
  In this paper, we propose an approach for the selection of the initial seeds
to generate IDIs for fairness testing. Previous studies mainly used random
initial seeds to this end. However this phase is crucial, as these seeds are
the basis of the follow-up IDIs generation. We dubbed our proposed seed
selection approach I&D. It generates a large number of initial IDIs exhibiting
a great diversity, aiming at improving the overall performance of fairness
testing.
  Our empirical study reveal that I&D is able to produce a larger number of
IDIs with respect to four state-of-the-art seed generation approaches,
generating 1.68X more IDIs on average. Moreover, we compare the use of I&D to
train machine learning models and find that using I&D reduces the number of
remaining IDIs by 29% when compared to the state-of-the-art, thus indicating
that I&D is effective for improving model fairness
