My research examines how artificial intelligence systems are designed, evaluated, and governed in real-world organizational and institutional settings. I work across Human-Computer Interaction, Responsible AI, and Science and Technology Studies, with a focus on how high-level ethical and regulatory ideals get translated into the everyday practice of machine learning development. My empirical orientation is qualitative and sociotechnical. My methodological commitments are participatory and comparative across Global North and Global South contexts.
My dissertation studies how responsible AI is enacted in industry. Drawing on interviews with AI developers, practitioners, and stakeholders across North America, Europe, and the UK, I argue that responsible AI work is increasingly shaped by compliance imperatives, with ethical toolkits valued for documentation and audit readiness rather than for normative deliberation. I am currently extending this inquiry to Pakistan, a setting with fragmented governance and limited AI-specific regulation, to examine how practitioners enact ethical responsibility in the absence of formal regulatory infrastructure. The dissertation develops a principle-to-practice translational matrix that functions as a boundary object connecting ethical and legal principles to concrete developer workflows, documentation, and evaluation strategies.
Before my PhD, I worked on funded interdisciplinary projects in Pakistan on maternal health, immunization, financial inclusion, voice-based crowdsourcing for low-literacy populations, and mobile security, with the Gates Foundation, UNICEF, DFID and Women's World Banking. That work shaped my commitment to qualitative inquiry, participatory design, and close attention to power and institutional capacity. Detailed case studies are on the Projects page.
My future research asks how AI governance can move beyond compliance toward participatory and responsive accountability for the communities most affected by AI systems. My CSCW 2026 paper on non-expert auditors in AI oversight is an early empirical step. Future projects will study community-centered AI auditing, cross-cultural adaptation of governance tools, and the alternative forms of oversight that emerge in resource-constrained settings.
Three threads anchor my next research program. The first is community-centered AI auditing in high-stakes domains such as health, education, and frontline social services, building on my CSCW 2026 work with non-expert auditors. The second is comparative fieldwork on how alternative forms of oversight and contestation emerge in resource-constrained settings, beginning with a study in Pakistan. The third is participatory design and design-based inquiry into translational artifacts that support dialogue between developers, policymakers, and affected communities. The longer-term goal is a framework for AI accountability that is legible, contestable, and actionable for the communities most affected.
Academic Publications
Corey Jackson, Tallal Ahmad, Devansh Saxena. "Re-imagining Fairness in Machine Learning: A Framework for Building in Socio-cultural and contextual Awareness" Workshop Paper in Supporting User Engagement in Testing, Auditing, and Contesting AI at CSCW 2023. (Presentation)
Industry Reports