FairnessBiasExplainabilitycs.LG
Fair Interpretable Learning via Correction Vectors
Mattia Cerrato, Marius Köppel, Alexander Segner, Stefan Kramer
January 17, 2022
arXiv.org
2 citations
Key Takeaways
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Fairness doesn't require sacrificing accuracy: The paper demonstrates that their correction vector approach maintains performance levels comparable to existing fair representation methods, challenging the assumption that debiasing necessarily comes at a cost to model accuracy.
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Explainability is crucial for trustworthy AI: By making corrections explicit and interpretable (rather than hidden within neural network layers), the authors address a key limitation of existing debiasing techniques—that their "opaque" nature makes it difficult for practitioners and stakeholders to understand how fairness is actually being achieved.
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Simple, transparent approaches can work: The correction vector method adds adjustments directly to original features rather than learning complex hidden representations, meaning each feature's penalty or bonus can be analyzed and understood by non-technical stakeholders, improving accountability.
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Interpretability enables better governance: Because the corrections are explicit and analyzable, this framework allows organizations to audit, validate, and explain their fairness interventions to regulators, affected communities, and other stakeholders—essential for responsible AI deployment.
Abstract
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various "representation debiasing" techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning which is centered around the learning of "correction vectors", which have the same dimensionality as the given data vectors. The corrections are then simply summed up to the original features, and can therefore be analyzed as an explicit penalty or bonus to each feature. We show experimentally that a fair representation learning problem constrained in such a way does not impact performance.