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.

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.

Commercial satellite imaging is used for diverse applications in a wide range of sectors, from agriculture to the military. As satellite images continue to become more widely available and detailed in resolution, the potential for individual and population-level monitoring increases and raises new privacy concerns compared to previous Earth observation technologies. We anticipate that these technologies will only continue to improve in the upcoming decade. To better understand privacy threats and concerns of commercial satellite imagery, we conducted a survey of 99 participants from the United States. We found that most respondents were not aware that commercial satellites existed, and once informed about the capabilities of commercial satellites, most are not comfortable with how good the current state-of-the-art satellite imaging capabilities are. Few respondents want satellite imagery cost-free and widely available, which conflicts with current trends in geospatial data. In addition to aiding our understanding of the public's current perception and relationship with remote sensing technologies, we use these results to propose possible new satellite image legislation, regulation, and technological mitigations, both nationally and internationally.

Research in computer security has increasingly considered the needs of marginalized and vulnerable groups in technology. Through this work, we hope to translate this research movement into practice and, ultimately, cause designers-in-training (and, eventually, designers) to consider a more inclusive range of stakeholders. Thus, we created an educational intervention to center marginalized and vulnerable populations in the context of threat modeling. We find that computer security students are more likely to consider unique threats and vulnerabilities facing marginalized and vulnerable populations after being exposed to an intervention prompting them to think about populations that might often be overlooked. We suggest practical methods to teach designers-in-training inclusive methods in computer security and discuss other possible adoptions of this practice across the field. This work is part of an important shift toward inclusive security that centers marginalized and vulnerable populations both in research and in practice.