从欺骗到感知：Deepfakes在检测、衡量和缓解偏见方面的惊人益处
From Deception to Perception: The Surprising Benefits of Deepfakes for Detecting, Measuring, and Mitigating Bias
AI总结：
研究利用deepfake生成受控面部图像以扩展通信研究，证明其可在疼痛评估等场景中有效检测、衡量并纠正偏见，从而促进社会公平。
摘要：
尽管deepfake技术主要因其潜在滥用而受到批评，但我们的研究表明，它们作为检测、衡量和缓解关键社会领域偏见的工具具有巨大潜力。通过利用deepfake技术生成受控的面部图像，我们将传统通信研究的范围扩展到单纯的文本操纵之外。这种增强在疼痛评估等场景中至关重要，因为面部图像中敏感特征引发的主观偏见会深刻影响结果。我们的研究结果表明，deepfakes不仅保持了通信研究的有效性，而且在偏见衡量和纠正技术方面引入了开创性的进展。本研究强调了deepfake技术作为促进社会公平与公正的重要工具的建设性作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
