Formal regulation designed to protect workers is often opaque, with narrow definitions of injury. This means that even when workers report harm, regulators fail to intervene on cumulative, chronic, and collective experiences. Through interviews and co-design workshops with unionized cleaning workers, we explore alternative forms of documentation that support collective action rather than institutional proof. Our study illuminates four interlocking worker-led documentation practices: (1) Surfacing experiences of working conditions, (2) Collectively making sense of existing reporting avenues, (3) Negotiating with management and publics, and (4) Building trust and solidarity within the union. For each of these practices, we offer concrete design concepts developed in collaboration with workers. Finally, we contribute conceptual knowledge on how engaging in the process of documentation functions as an engine of solidarity-building, a prerequisite for addressing workplace harms beyond disparate data points.

Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long-term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators’ severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators’ mental health.

The rapid expansion of the AI industry relies heavily on the production, verification, and maintenance of data, otherwise known as "data work". Companies outsource and offshore this work through global AI supply chains that operate under exploitative conditions. Drawing on semi-structured interviews with Kenyan data workers across platforms and BPOs, this paper examines how such conditions take shape and persist. We argue that workers are caught within a regime of entrapment, a system of interconnected mechanisms that make it difficult for workers to leave or improve their positions. These mechanisms include the push to invest in the promise of ‘AI’ jobs, the use of precarious contracts to govern workers, the capture of regulatory institutions, and the exploitation of global labor arbitrage. Using complementary lenses of neoliberal governmentality, precarity, and supply chain capitalism, we analyze why labor mobilization in this sector remains uniquely constrained. We conclude by outlining an orientation for research and scholarly practice that can support workers’ organizing efforts and contest the structural conditions sustaining this regime.

Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, drawing on 16 semi-structured interviews and a survey with 50 HCI researchers. We discuss the ways in which LLMs are already being utilized throughout the entire HCI research pipeline, from ideation to system development and paper writing. While researchers described nuanced understandings of ethical issues, they were rarely or only partially able to identify and address those ethical concerns in their own projects. This lack of action and reliance on workarounds was explained through the perceived lack of control and distributed responsibility in the LLM supply chain, the conditional nature of engaging with ethics, and competing priorities. Finally, we reflect on the implications of our findings and present opportunities to shape emerging norms of engaging with large language models in HCI research.

Diversity in datasets is a key component to building responsible AI/ML. Despite this recognition, we know little about the diversity among the annotators involved in data production. We investigated the approaches to annotator diversity through 16 semi-structured interviews and a survey with 44 AI/ML practitioners. While practitioners described nuanced understandings of annotator diversity, they rarely designed dataset production to account for diversity in the annotation process. The lack of action was explained through operational barriers: from the lack of visibility in the annotator hiring process, to the conceptual difficulty in incorporating worker diversity. We argue that such operational barriers and the widespread resistance to accommodating annotator diversity surface a prevailing logic in data practices—where neutrality, objectivity and ‘representationalist thinking’ dominate. By understanding this logic to be part of a regime of existence, we explore alternative ways of accounting for annotator subjectivity and diversity in data practices.

Technology companies continue to invest in efforts to incorporate responsibility in their Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of practitioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specific practices and their associated challenges have yet to be surfaced in the literature, and distilling them offers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reified in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI.

Most prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority—AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.

Grassroots workers are increasingly subjected to data-driven systems worldwide. While there has been increasing attention to processes of datafication in state sponsored welfare programs, not much attention has been focused on everyday workplace of the poor particularly in global south. In this paper, we examine the datafication experiences of sanitation and domestic workers, marginalized by caste, gender, and income, in India that goes beyond a welfare program setting. We report from interviews with 25 workers and 7 community leaders. Contrary to the modernist narratives around data and development, we find that data-driven systems invisibly inherited discriminatory properties from past institutions. These datafication processes are refracted through lack of access to supporting infrastructure, intentional opacity, and automated oppressive institutional norms.

AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades—compounding events causing negative, downstream effects from data issues—triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence), invisible, delayed, but often avoidable. We discuss HCI opportunities in designing and incentivizing data excellence as a first-class citizen of AI, resulting in safer and more robust systems for all.

With easy access to affordable internet-powered smartphones, developing countries are adopting smartphone applications to provide enabling services to its citizens, through eHealth, eGovernance, and digital payments. The challenge is to ensure equitable access to these services by everyone, including people with semi-literacy or low-literacy who form a large part of the population in developing countries. However, extensive HCI literature has identified literacy as one of the barriers to designing user interfaces. In this work, we propose a framework of actionable guidelines for designing smartphone UIs that would be usable by low-literate users. We reviewed the last two decades of HCI literature engaging people with low literacy, to synthesize our framework-designing SARAL. To evaluate the framework, we conducted a preliminary study with a group of 20 practitioners and researchers working in the field of UI/UX/HCI. We also analyzed six publicly available industry reports on designing UIs for people with low-literacy. The proposed guidelines intend to support researchers, practitioners, designers, and implementers in the design and evaluation of UIs of smartphone applications for people with low literacy. We present the evolutionary nature of the proposed framework while highlighting the importance of adopting a translational approach when building such frameworks.