  Text classifiers have promising applications in high-stake tasks such as
resume screening and content moderation. These classifiers must be fair and
avoid discriminatory decisions by being invariant to perturbations of sensitive
attributes such as gender or ethnicity. However, there is a gap between human
intuition about these perturbations and the formal similarity specifications
capturing them. While existing research has started to address this gap,
current methods are based on hardcoded word replacements, resulting in
specifications with limited expressivity or ones that fail to fully align with
human intuition (e.g., in cases of asymmetric counterfactuals). This work
proposes novel methods for bridging this gap by discovering expressive and
intuitive individual fairness specifications. We show how to leverage
unsupervised style transfer and GPT-3's zero-shot capabilities to automatically
generate expressive candidate pairs of semantically similar sentences that
differ along sensitive attributes. We then validate the generated pairs via an
extensive crowdsourcing study, which confirms that a lot of these pairs align
with human intuition about fairness in the context of toxicity classification.
Finally, we show how limited amounts of human feedback can be leveraged to
learn a similarity specification that can be used to train downstream
fairness-aware models.

Computation and Language;Artificial Intelligence;Computers and Society;Machine Learning