US military veterans (USMVs) are a vulnerable population with an elevated risk of mental health issues and suicide. Peer support, especially through mobile technology, has proven effective in addressing mental health related challenges, but ensuring long-term engagement remains a concern. This study explores the opportunity of designing persuasive technology, particularly persuasive reminders, to enhance engagement in peer support interventions for veterans. We followed community-based participatory research with ten veterans to identify specific peer support processes that can benefit from persuasive reminders and to uncover the underlying community values and needs to guide design. The findings emphasize the importance of designing reminders that focus on personalized strategies, effective delivery of success stories, understanding motivation levels, careful language selection, actionable reminders, and mutual accountability. The study advocates context-specific design and highlights the need for a broader user-centered persuasion design perspective to cater to veterans’ unique needs.

Research into recidivism risk prediction in the criminal justice system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS) in this context. We conducted experiments and follow-up interviews with 60 participants, including community members, technical experts, and law enforcement agents (LEAs), to explore how lived experiences, technical knowledge, and domain expertise shape interactions with the ADS, impacting human-AI decision-making. Surprisingly, we found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them. Conversely, community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps. Our findings highlight that all three stakeholders were able to provide critical feedback regarding AI design and use - community members questioned the core motivation of the tool, technical experts drew attention to the elastic nature of data science practice, and LEAs suggested redesign pathways such that the tool could complement their domain expertise.

Mobile mental health applications are seen as a promising way to fulfill the growing need for mental health care. Although there are more than ten thousand mental health apps available on app marketplaces, such as Google Play and Apple App Store, many of them are not evidence-based, or have been minimally evaluated or regulated. The real-life experience and concerns of the app users are largely unknown. To address this knowledge gap, we analyzed 2159 user reviews from 117 Android apps and 2764 user reviews from 76 iOS apps. Our findings include the critiques around inconsistent moderation standards and lack of transparency. App-embedded social features and chatbots were criticized for providing little support during crises. We provide research and design implications for future mental health app developers, discuss the necessity of developing a comprehensive and centralized app development guideline, and the opportunities of incorporating existing AI technology in mental health chatbots.

Post-Traumatic Stress Disorder (PTSD) is one of the most common mental health disorders prevalent in the US. Most alarming, PTSD occurs at double the rate for combat veterans compared to the general population. Severity of PTSD is associated with risk taking behaviors such as substance abuse, non-suicidal self-injury, sexual risk behaviors, among other negative behaviors. Psychological disorders are often preceded by crisis events, thus monitoring for crisis events can help prevent risky behavior in veterans. Ecological momentary assessment techniques are effective in capturing possible crisis events for veterans. Mobile apps are commonly used to gather such behavioral changes in participants. Crisis events collected from m-health can be analyzed for the identification of long- term PTSD risk. Early identification of risk can help in planning intervention to mitigate the risk. Many scholars have used traditional statistical and machine learning methods for the prediction of mental health issues in individuals. But these models lack transparency in how decisions are made. Providing justifications for the predictions can increase the reliability of the model. Our research focused on developing an explainable prediction model using class association rules to identify veterans at risk of persistent PTSD. The generated association rules serve as precursors to the long-term crisis in veterans. Results of the analysis showed that having no family support, little or no interest in hobbies, stress and lack of sleep are some of the influencing factors of persistent PTSD in veterans.

This paper joins a growing body of work within ICTD and related fields studying the privacy challenges in the Global South. While most of the existing work in this area has focused on uses of technology in personal and home settings, a large part of computing in the Global South centers around public places, such as commercial Digital Service Centers (DSCs). In this paper, we present the findings from a six-month-long ethnography studying 19 Digital Service Centers in Dhaka, Bangladesh. We find that infrastructural limitations, local power politics, lack of knowledge, and insufficient protection mechanisms lead to privacy vulnerabilities for the customers of these centers. We apply the lens of informal markets to analyze these vulnerabilities and connect our findings to the broader concerns of ICTD around development, ethics, and postcolonial computing and discuss potential design and policy implications around these issues.

People in South Asia frequently share a single device among multiple individuals, resulting in digital privacy challenges. This paper explores a design concept that aims to mitigate some of these challenges through a 'tiered' privacy model. Using this model, a person creates a 'shared' account that contains data they are willing to share and that is assigned a password that will be shared. Simultaneously, they create a separate 'secret' account that contains data they prefer to keep secret and that uses a password they do not share with anyone. When a friend or family member asks to check their device, the user can tell them the password for their shared account, with their private data secure in the secret account that the other person is unaware of. We explore the benefits and trade-offs of our design through a three-week deployment with 21 participants in Bangladesh, presenting findings that show how our work aids digital privacy while also exposing the challenges that remain.

With the rapid growth of ICT adoption in the Global South, crimes over and through digital technologies have also increased. Consequently, governments have begun to undertake a variety of different surveillance programs, which in turn provoke questions regarding citizens' privacy rights. However, both the concepts of privacy and of citizens' corresponding political rights have not been well-developed in HCI for non-Western contexts. This paper presents findings from a three-month long ethnography and online survey (n=606) conducted in Bangladesh, where the government recently imposed mandatory biometric registration for every mobile phone user. Our analysis surfaces important privacy and safety concerns regarding identity, ownership, and trust, and reveals the cultural and political challenges of imposing biometric registration program in Bangladesh. We also discuss how alternative designs of infrastructure, technology, and policy may better meet stakeholders' competing needs in the Global South.

Prior research on technology use in the Global South suggests that people in marginalized communities frequently share a single device among multiple individuals. However, the data privacy challenges and tensions that arise when people share devices have not been studied in depth. This paper presents a qualitative study with 72 participants that analyzes how families in Bangladesh currently share mobile phones, their usage patterns, and the tensions and challenges that arise as individuals seek to protect the privacy of their personal data. We show how people share devices out of economic need, but also because sharing is a social and cultural practice that is deeply embedded in Bangladeshi society. We also discuss how prevalent power relationships affect sharing practices and reveal gender dynamics that impact the privacy of women's data. Finally, we highlight strategies that participants adopted to protect their private data from the people with whom they share devices. Taken together, our findings have broad implications that advance the CSCW community's understanding of digital privacy outside the Western world.