Explore a conference talk that delves into accountability practices for machine learning datasets, drawing insights from software engineering and infrastructure. Examine the research presented by B. Hutchinson, E. Denton, M. Mitchell, A. Hanna, A. Smart, C. Greer, P. Barnes, and O. Kjartansson at the FAccT 2021 virtual conference. Discover how principles from software development can be applied to improve transparency, responsibility, and ethical considerations in the creation and maintenance of ML datasets. Learn about potential strategies for addressing challenges in dataset accountability and their implications for the broader field of artificial intelligence.
Towards Accountability for Machine Learning Datasets - Practices from Software Engineering and Infrastructure
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infras
Taught by
ACM FAccT Conference
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