用深度伪造图像检测并纠正面部感知偏见，提升评估公平性
From Deception to Perception: The Surprising Benefits of Deepfakes for Detecting, Measuring, and Mitigating Bias
- 用可控的深度伪造人脸拓展传统对应研究
- 实证显示可有效测量与修正主观偏见
- 适合关注公平性与社会公正的研究者
尽管深度伪造技术常被批评可能被滥用，但本研究揭示其在检测、测量和缓解关键社会领域偏见方面的巨大潜力。通过使用深度伪造技术生成受控的面部图像，我们突破了传统对应研究仅限于文本操纵的局限。这一改进在疼痛评估等场景中尤为重要，因为面部敏感特征引发的主观偏见会显著影响结果。结果显示，深度伪造不仅保持了对应研究的有效性，还在偏见测量与修正技术上实现突破性进展。研究强调，深度伪造技术可作为推动社会公平与正义的重要工具。
原文摘要 · Abstract (English)
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.
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