🤖 AI Summary
Machine learning models often exhibit discriminatory impacts on protected groups, yet existing fairness evaluation metrics provide only global, task-specific unfairness estimates—lacking fine-grained interpretability and cross-task generalizability. To address this, we propose Pointwise Normalized Kernel Alignment (PNKA), the first metric enabling pointwise, cross-task fairness intervention attribution at the representation level. PNKA quantifies how individual samples’ pairwise similarities shift in intermediate representation spaces under fairness interventions, thereby supporting subgroup identification, downstream behavior prediction, and fairness auditing. Experiments across tabular and language modalities demonstrate that PNKA accurately pinpoints vulnerable subgroups affected by interventions, reliably predicts changes in downstream model fairness, and uncovers systematic failures of mainstream language debiasing methods—particularly their inability to mitigate stereotypical semantic biases.
📝 Abstract
Machine learning (ML) algorithms can often exhibit discriminatory behavior, negatively affecting certain populations across protected groups. To address this, numerous debiasing methods, and consequently evaluation measures, have been proposed. Current evaluation measures for debiasing methods suffer from two main limitations: (1) they primarily provide a global estimate of unfairness, failing to provide a more fine-grained analysis, and (2) they predominantly analyze the model output on a specific task, failing to generalize the findings to other tasks. In this work, we introduce Pointwise Normalized Kernel Alignment (PNKA), a pointwise representational similarity measure that addresses these limitations by measuring how debiasing measures affect the intermediate representations of individuals. On tabular data, the use of PNKA reveals previously unknown insights: while group fairness predominantly influences a small subset of the population, maintaining high representational similarity for the majority, individual fairness constraints uniformly impact representations across the entire population, altering nearly every data point. We show that by evaluating representations using PNKA, we can reliably predict the behavior of ML models trained on these representations. Moreover, applying PNKA to language embeddings shows that existing debiasing methods may not perform as intended, failing to remove biases from stereotypical words and sentences. Our findings suggest that current evaluation measures for debiasing methods are insufficient, highlighting the need for a deeper understanding of the effects of debiasing methods, and show how pointwise representational similarity metrics can help with fairness audits.
