CACM May 2020 - A Snapshot of the Frontiers of Fairness in Machine Learning
Автор: Association for Computing Machinery (ACM)
Загружено: 2020-04-21
Просмотров: 918
Описание: The last decade has seen a vast increase both in the diversity of applications to which machine learning is applied, and to the import of those applications. Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things. The result has been a major concern for the potential for data-driven methods to introduce and perpetuate discriminatory practices, and to otherwise be unfair. At the same time, the last two years have seen an unprecedented explosion in interest from the academic community in studying fairness and machine learning. "Fairness and transparency" transformed from a niche topic to a major subfield of machine learning, complete with a dedicated archival conference—ACM FAT*). In this video, Alexandra Chouldechova and Aaron Roth discuss "A Snapshot of the Frontiers of Fairness in Machines Learning" (cacm.acm.org/magazines/2020/5/244336), a Review Article in the May 2020 CACM.
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