On Monday, April 15, NYU Stern's Fubon Center for Technology, Business and Innovation will host a talk on “Algorithmic Fairness in Business” by Dr. Solon Barocas.
Machine learning is now deeply embedded in business decisions both routine and high-stakes. Consumers' everyday interactions with businesses and their ability to gain access to critical opportunities depend on the output of machine learned models. These techniques have been embraced in regulated domains such as employment, credit, and insurance precisely because they promise to improve the consistency and quality of decision-making. Yet there is growing recognition that learning models from historical data can end up replicating the human biases they promised to stamp out. Over the past few years, algorithmic fairness has become a watchword for consumer advocates, regulators, policymakers, and businesses alike. Less well understood, however, are the many ways that machine learning figures into the far more quotidian business decisions that do not fall under any regulation, but nevertheless raise concerns with fairness, ranging from marketing and advertising to information retrieval and personalization. In this talk, Solon will offer a survey of the wide range of fairness concerns prompted by businesses' embrace of machine learning.
A fireside chat featuring Dr. Barocas in conversation with NYU Stern Professor Foster Provost, the Director of the Fubon Data Analytics and AI Initiative, will follow.
This event is part of the Fubon Center's AI in Business Speaker Series.