No Training Wheels: Steering Vectors for Bias Correction at Inference Time

TL;DR

This paper introduces a simple, training-free technique using steering vectors to reduce bias in neural network classifiers at inference time, improving fairness without retraining.

Contribution

It proposes a novel, inexpensive method to mitigate bias by subtracting bias vectors derived from group differences, applicable during inference in transformer classifiers.

Findings

Reduces classification bias and improves worst-group accuracy.

Effective in transformer-like classifiers without retraining.

Demonstrates applicability of steering vectors beyond generative models.

Abstract

Neural network classifiers trained on datasets with uneven group representation often inherit class biases and learn spurious correlations. These models may perform well on average but consistently fail on atypical groups. For example, in hair color classification, datasets may over-represent females with blond hair, reinforcing stereotypes. Although various algorithmic and data-centric methods have been proposed to address such biases, they often require retraining or significant compute. In this work, we propose a cheap, training-free method inspired by steering vectors used to edit behaviors in large language models. We compute the difference in mean activations between majority and minority groups to define a "bias vector," which we subtract from the model's residual stream. This leads to reduced classification bias and improved worst-group accuracy. We explore multiple strategies for extracting and applying these vectors in transformer-like classifiers, showing that steering vectors, traditionally used in generative models, can also be effective in classification. More broadly, we showcase an extremely cheap, inference time, training free method to mitigate bias in classification models.