WhatsApp is the most popular social messaging platform, and modified versions (or “mods”) of the official WhatsApp are increasingly popular. Mods advertise additional features and customization. However, some of these features, e.g., retaining deleted messages and statuses, enable mod users to subvert the privacy of others, and have the potential for seri-ous security and privacy implications. In this study, we explore user perspectives of WhatsApp mods through an interview study$(n=20)$of mod users in Kenya, one of the countries with the highest WhatsApp mod usage. Many turned to WhatsApp mods for their “advanced” features to protect themselves (e.g., “anti-delete” for legal liability), while others admitted to using mod features to hide their behavior or to stalk others. To understand how users' expectations of WhatsApp mods align with the apps' behavior, we identify and analyze 13 instances of the most common mod (GB WhatsApp). While WhatsApp mods contained the features they claimed to offer, some participants incorrectly believed that features currently available in the official app only existed in mods. Additionally, several mods were significantly over-permissioned compared to the official WhatsApp, despite participants believing that they requested the same permissions as the official app. While almost half of participants indicated they trust mods more than the official WhatsApp, we found two mods contained malware. The use of WhatsApp mods poses risks to mod users and those they communicate with, but also empowers users in ways that the official app does not. We caution developers and mod users to do their due diligence before using or distributing mods.

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.

Surveillance of communication between incarcerated and non-incarcerated people has steadily increased, enabled partly by technological advancements. Third-party vendors control communication tools for most U.S. prisons and jails and offer surveillance capabilities beyond what individual facilities could realistically implement. Frequent communication with family improves mental health and post-carceral outcomes for incarcerated people, but does discomfort about surveillance affect how their relatives communicate with them? To explore this and the understanding, attitudes, and reactions to surveillance, we conducted 16 semi-structured interviews with participants who have incarcerated relatives. Among other findings, we learn that participants communicate despite privacy concerns that they felt helpless to address. We also observe inaccuracies in participants’ beliefs about surveillance practices. We discuss implications of inaccurate understandings of surveillance, misaligned incentives between end-users and vendors, how our findings enhance ongoing conversations about carceral justice, and recommendations for more privacy-sensitive communication tools.