Human-Guided Fair Classification for Natural Language Processing
This work addresses fairness in NLP for high-stake applications like resume screening and content moderation, offering a novel approach to bridge the gap between human intuition and formal specifications, though it is incremental in improving existing methods.
The paper tackled the problem of aligning formal fairness specifications with human intuition in text classifiers by proposing methods to generate and validate semantically similar sentence pairs that differ along sensitive attributes, using unsupervised style transfer and GPT-3, and showing that crowdsourced validation confirms alignment with human fairness intuitions in toxicity classification.
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.