Framework improves fairness and creates alternative text scenarios
MUtE: A Dual Framework for Concept Erasure and Counterfactual Interventions
Machine LearningComputation and Language
Summary
Sometimes, machine learning models accidentally focus on certain ideas or stereotypes, leading to unfair or biased decisions. The authors created a method that removes these unwanted ideas from the model’s understanding while keeping all other useful information intact. Their approach can also make realistic 'what if' changes to texts by imagining what a sentence would look like if it didn't contain the removed idea. This helps in making algorithms fairer and in generating alternative versions of texts for better analysis.
What this means in practice
- •For machine learning engineers: Improve fairness in AI models by removing specific biased concepts from their representations while preserving other important information.
- •For natural language processing developers: Generate realistic counterfactual text versions to test and interpret language models’ decisions and behaviors.
Authors
Antoine Saillenfest
Abstract
Erasing concept-specific information from representations has been proven useful for mitigating bias or interpreting model decisions. The joint objective is to transform the original representations such that the target concept becomes unpredictable, while maximally preserving concept-unrelated information. In this work, we revisit the optimal bounds of concept erasure to derive a novel class of erasure functions that naturally induce a deterministic, dual counterfactual mapping. Bridging the gap between theoretical optimality and practical representation learning, we design an implementation that imposes a translational bias on counterfactual trajectories - a constraint that aligns with how many concepts geometrically manifest in modern language models. Our framework enables seamless navigation between concept erasure and counterfactual generation. We empirically demonstrate its efficacy in improving downstream algorithmic fairness and generating counterfactual texts.