From Deception to Perception: The Surprising Benefits of Deepfakes for Detecting, Measuring, and Mitigating Bias

TL;DR

This paper explores how deepfake technology can be repurposed from a tool of deception to a valuable resource for detecting, measuring, and reducing biases in societal assessments, especially in sensitive areas like pain evaluation.

Contribution

It introduces a novel application of deepfakes for bias analysis, extending traditional correspondence studies with controlled facial image generation to improve bias detection and mitigation.

Findings

Deepfakes effectively generate controlled facial images for bias studies.

They enhance the measurement of subjective biases in sensitive assessments.

Deepfakes facilitate bias correction techniques, promoting societal fairness.

Abstract

While deepfake technologies have predominantly been criticized for potential misuse, our study demonstrates their significant potential as tools for detecting, measuring, and mitigating biases in key societal domains. By employing deepfake technology to generate controlled facial images, we extend the scope of traditional correspondence studies beyond mere textual manipulations. This enhancement is crucial in scenarios such as pain assessments, where subjective biases triggered by sensitive features in facial images can profoundly affect outcomes. Our results reveal that deepfakes not only maintain the effectiveness of correspondence studies but also introduce groundbreaking advancements in bias measurement and correction techniques. This study emphasizes the constructive role of deepfake technologies as essential tools for advancing societal equity and fairness.