One cloud shading the otherwise positive outlook for AI and machine learning, as aids to human advancement, is the concern that the algorithms that drive these technologies can be unfair—can carry ingrained biases that distort impartial results.
The concept of fairness is at the heart of many of the new legal frameworks and policy consultations concerning AI. Unfairness is often understood as discrimination, with policymakers concerned that algorithms could reproduce and strengthen individual and systemic discrimination in healthcare, the criminal justice system, housing, employment, and education.
IEDP was delighted to hostDr Kate Vredenburgh, Assistant Professor in the Department of Philosophy, Logic and Scientific Method, London School of Economics (LSE), in a complementary, interactive, webinar that explores the vital and topical issue of algorithmic fairness. This is a recording:
In this webinar, Dr Kate Vredenburgh explores the concept of fairness to understand and improve upon current research and strategies for algorithmic fairness. She uses this concept to discuss research and industry interventions to improve algorithmic fairness and argue for two policy strategies to increase fair decision-making with AI: increased transparency and control, and a decreased reliance on decision thresholds.
About the speaker
Dr Kate Vredenburgh is Assistant Professor in the Department of Philosophy, Logic and Scientific Method at LSE. Kate is the Course Convenor of the LSE Online Masterclass Ethics of AI, which provides you with the tools and skills to practise ethical reasoning and manage the diverse impacts of AI on individuals and society.