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The present and future transition of lives and activities into virtual worlds --- worlds in which people interact using avatars --- creates novel privacy challenges and opportunities. Avatars present an opportunity for people to control the way they are represented to other users and the information shared or implied by that representation. Importantly, users with marginalized identities may have a unique set of concerns when choosing what information about themselves (and their identities) to conceal or expose in an avatar. We present a theoretical basis, supported by two empirical studies, to understand how marginalization impacts the ways in which people create avatars and perceive others' avatars: what information do people choose to reveal or conceal, and how do others react to these choices? In Study 1, participants from historically marginalized backgrounds felt more concerned about being devalued based on their identities in virtual worlds, which related to a lower desire to reveal their identities in an avatar, compared to non-marginalized participants. However, in Study 2 participants were often uncomfortable with others changing visible characteristics in an avatar, weighing concerns about others' anonymity with possible threats to their own safety and security online. Our findings demonstrate asymmetries in what information people prefer the self vs. others to reveal in their online representations: participants want privacy for themselves but to feel informed about others. Although avatars allow people to choose what information to reveal about themselves, people from marginalized backgrounds may still face backlash for concealing components of their identities to avoid harm. 

The Heilmeier Catechism consists of a set of questions that researchers and practitioners can consider when formulating research and applied engineering projects. In this article, we suggest explicitly asking who is included and who is left out of consideration. 

We applied techniques from psychology --- typically used to visualize human bias --- to facial analysis systems, providing novel approaches for diagnosing and communicating algorithmic bias. First, we aggregated a diverse corpus of human facial images (N=1492) with self-identified gender and race. We tested four automated gender recognition (AGR) systems and found that some exhibited intersectional gender-by-race biases. Employing a technique developed by psychologists --- face averaging --- we created composite images to visualize these systems' outputs. For example, we visualized what an average woman looks like, according to a system's output. Second, we conducted two online experiments wherein participants judged the bias of hypothetical AGR systems. The first experiment involved participants (N=228) from a convenience sample. When depicting the same results in different formats, facial visualizations communicated bias to the same magnitude as statistics. In the second experiment with only Black participants (N=223), facial visualizations communicated bias significantly more than statistics, suggesting that face averages are meaningful for communicating algorithmic bias. 

What makes faces seem trustworthy? We investigated how racial prejudice predicts the extent to which perceivers employ racially prototypical cues to infer trustworthiness from faces. We constructed participant-level computational models of trustworthiness and White-to-Black prototypicality from U.S. college students’ judgments of White (Study 1, N = 206) and Black–White morphed (Study 3, N = 386) synthetic faces. Although the average relationships between models differed across stimuli, both studies revealed that as participants’ anti-Black prejudice increased and/or intergroup contact decreased, so too did participants’ tendency to conflate White prototypical features with trustworthiness and Black prototypical features with untrustworthiness. Study 2 ( N = 324) and Study 4 ( N = 397) corroborated that untrustworthy faces constructed from participants with pro-White preferences appeared more Black prototypical to naive U.S. adults, relative to untrustworthy faces modeled from other participants. This work highlights the important role of racial biases in shaping impressions of facial trustworthiness. 

Inferring humans’ complex emotions is challenging but can be done with surprisingly limited emotion signals, including merely the eyes alone. Here, we test for a role of lower-level perceptual processes involved in such sensitivity using the well-validated Reading the Mind in the Eyes task. Over three experiments, we manipulated configural processing to show that it contributes to sensitivity to complex emotion from human eye regions. Specifically, inversion, a well-established manipulation affecting configural processing, undermined sensitivity to complex emotions in eye regions (Experiments 1-3). Inversion extended to undermine sensitivity to nonmentalistic information from human eye regions (gender; Experiment 2) but did not extend to affect sensitivity to attributes of nonhuman animals (Experiment 3). Taken together, the current findings provide evidence for the novel hypothesis that configural processing facilitates sensitivity to complex emotions conveyed by the eyes via the broader extraction of socially relevant information.