Artificial intelligence (AI) is transforming the global economy, reshaping access to employment, resources, and mobility. While AI offers opportunities for innovation, it also has the potential to amplify existing structural inequalities, particularly affecting low-income individuals. This research initiative investigates how AI-driven technologies influence work opportunities for people from low-income backgrounds, focusing on their experiences and strategies in navigating AI systems. By analyzing these dynamics through a sociological lens and employing ethnographic and qualitative methodologies the initiative seeks to inform the development of equitable AI systems and policies that empower low-income communities and individuals and promote inclusive economic growth.
This research initiative particularly examines how to prevent AI-driven hiring platforms, skill-training tools, and algorithmic welfare systems from perpetuating societal biases, such as undervaluing certain types of work accessible to low-income individuals. For example, AI in hiring may prioritize certain technical or language skills over service, relational, or caregiving skills, which may disproportionately disadvantage those from low-income backgrounds. Similarly, AI-driven skill-building platforms may not account for barriers like limited access to digital resources and entrenched cultural expectations, further marginalizing these individuals.
To address these challenges, the initiative poses key research questions rooted in sociological inquiry:
- Under what conditions are low-income individuals able to navigate hiring biases in AI-driven platforms?
- How do limited digital access and cultural expectations affect their ability to utilize AI-driven skill-building tools, and under what conditions are they able to overcome these barriers?
- In what ways can AI systems be designed to recognize and value a broader range of skills possessed by low-income individuals?
- How do social networks and community ties influence the ability of low-income individuals to overcome exclusionary AI systems?
- What sociological factors/interventions contribute to the success or failure of AI interventions aimed at economic empowerment for marginalized communities?
The methodology will employ ethnographic and qualitative research methods to deeply understand the lived experiences of low-income individuals who may be directly or indirectly affected by AI or algorithmic bias, as well as possible strategies for overcoming the challenges of AI.
The deliverables of the initiative include an initial workshop where sociologists can connect to explore these issues, discussing the challenges and opportunities that AI presents to low-income individuals and communities. A series of guest lectures featuring experts in sociology and related fields will provide diverse perspectives and foster dialogue. Opportunities for ongoing research collaborations may lead to publications in academic journals and presentations at conferences. Collaborative papers or reports will summarize findings and offer recommendations for designing equitable AI systems and inclusive policies. Depending on interest and engagement, the initiative could host additional workshops, seminars, or webinars to continue the conversation and expand its impact.
By focusing on the intersection of AI, employability, and poverty, this research initiative uncovers the structural and cultural dynamics that shape access to work in an AI-driven economy. It provides a roadmap for creating equitable systems that address algorithmic biases and support the economic empowerment of marginalized individuals, ultimately ensuring that technological progress uplifts all members of society.
Lead
Nilanjan Raghunath (Nina) is a Sociology faculty member at the Singapore University of Technology and Design, specializing in organizational sociology, and the future of work. A 2023 Fulbright Scholar at Columbia University, she has held visiting roles at MIT, Cambridge, and Oxford. Author of Shaping the Futures of Work: Proactive Governance and Millennials (McGill Queens University Press) and numerous other publications, her research bridges sociology, technology policy, and organizational behavior to address challenges in rapidly evolving technological and economic futures.
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