LSE: The Ballpark | How AI is influencing society with Dr Mona Sloane
The widening use of AI raises questions of transparency and accountability, as well as challenges for regulators. Since the emergence of AI as a highly transformative force for modern society, governments, industries, and communities have faced challenges in updating their policies to address the potentially harmful effects of unrestricted AI development and integration into everyday life. Yet as some scholars have pointed out, governing AI is not just a question of balancing economic opportunities with political control, but also one which involves social contracts; as individuals and institutions decide the extent to which AI will become part of their day-to-day decision-making.
For this episode of our AI mini-series, I spoke to Mona Sloane about the societal dimensions of AI’s influence, how these relate to AI accountability and transparency, and the current challenges facing AI regulators.
Mona Sloane is an Assistant Professor of Data Science and Media Studies at the University of Virginia. She heads the Sloane Lab, a growing research group which focuses on social science leadership on AI in professions and responsible AI governance.
This episode was produced by Chris Gilson and Avan Fata.
Further reading and resources
- Predicted: How AI is Restructuring Social Life – Mona Sloane – 2026, University of California Press
- Co-Opting AI: Public Conversations About Artificial Intelligence and Society – virtual public speaker series at the Digital Technology for Democracy Lab at the University of Virginia
- Sloane, Mona. 2022. "To Make AI Fair, Here’s What We Must Learn to Do." Nature605 (7908): 9–9.
- Calhoun, C., Duus, E., Ho, D., Kitzmann, O., and Sloane, M. (2025): ‘Students in the Driver’s Seat: Establishing Collaborative Cultures of Technology Governance at Universities.’ In: Reimagining AI: For Environmental Justice and Creativity. University of Virginia: Digital Technology for Democracy Lab.
- Buolamwini, Joy, and Timnit Gebru. 2018. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.
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