The Gendered Algorithm: Navigating Financial Inclusion & Equity in AI-facilitated Access to Credit

AI has the potential to help solve global problems and be employed “for good." One area of immense recent investment and interest is the financial technology sector. Boasting its ability to provide financial services for the underbanked, various startups are developing apps that collect mobile phone data and use machine learning (ML) to provide credit scores – and subsequently, opportunities to access loans – to groups often left out of traditional banking in low- and middle-income countries (LMICs).

This talk explores whether these technologies reinforce or mitigate gender inequitable access to finance in LMICs. Grounded in feminist and postcolonial theory and Science and Technology Studies (STS), Genevieve's methods include interviews with fintech designers and data scientists, as well as examining digital trace data and user survey data, to analyze how these tools are created, managed, and experienced. Findings reveal that while fintech innovations hold promise, they fall short in addressing gender inequitable access to finance. Algorithmic lending tools are shaped by underlying logics of their developers. Developers and managers do not consider or adequately address how gender shapes access to and use of the technology, nor how gender “blind” algorithms and profit priorities can inadvertently privilege male-coded financial and digital behaviors. Perceptions of fairness by fintechs fail to challenge – and even legitimize – gender inequities in financial access. Both male and female users report positive benefits that the technology facilitates, yet gender differences persist in app access and use. In addition to the empirical contributions, findings expand existing theories of algorithmic bias and feminist STS, and offer alternative paths forward. The talk will include a preview of her planned research agenda, "Social implications of AI & alternative technological futures."

Genevieve Smith is a postdoctoral research fellow at Stanford University and completed her doctoral degree at the University of Oxford in the Department of International Development, co-supervised through the Oxford Internet Institute. Smith founded the Responsible AI Initiative at the Berkeley Artificial Intelligence Research Lab, which conducts multidisciplinary research on topics of AI and society, and serves as professional faculty at UC Berkeley on responsible AI. She is a research affiliate at the Minderoo Centre for Technology & Democracy at Cambridge University and at the Technology & Management Centre for Development at University of Oxford. Smith was recently the Responsible AI Fellow at the United States Agency for International Development. Prior to her doctoral work, Smith spent over a decade researching topics of economic empowerment and inclusive technology including with UN Women and the International Center for Research on Women. Her research and work has been published in journals such as Big Data & Society, as well as shared in Wall Street Journal, Forbes, Social Stanford Innovation Review, the Economist and more. Her research has also been shared at leading conferences including the International Conference on Machine Learning (ICML), the ACM Conference on Fairness, Accountability & Transparency (FAccT) and the Academy of Management.