It's Trying Too Hard To Look Real: Deepfake Moderation Mistakes and Identity-Based Bias

Online platforms employ manual human moderation to distinguish human-created social media profiles from deepfake-generated ones. Biased misclassification of real profiles as artificial can harm general users as well as specific identity groups; however, no work has yet systematically investigated such mistakes and biases. We conducted a user study (𝑛=695) that investigates how 1) the identity of the profile, 2) whether the moderator shares that identity, and 3) components of a profile shown affect the perceived artificiality of the profile. We find statistically significant biases in people's moderation of LinkedIn profiles based on all three factors. Further, upon examining how moderators make decisions, we find they rely on mental models of AI and attackers, as well as typicality expectations (how they think the world works). The latter includes reliance on race/gender stereotypes. Based on our findings, we synthesize recommendations for the design of moderation interfaces, moderation teams, and security training.

• Security and privacy → Social aspects of security and privacy; • Human-centered computing → Empirical studies in HCI .