Wednesday, October 20, 2021

Regulatory Fairness Assessment Using Machine Learning

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IMAGE: Map of wastewater treatment facilities in the United States with general permits (orange) intended to cover multiple unloaders engaged in similar activities and individual permits (blue) that cover a specific facility. Individual states… view more

Credit: Benami, et al.

The dangers of machine learning – using computers to identify and analyze data models, like in facial recognition software – have been making headlines lately. Yet the technology also holds promise in helping enforce federal regulations, including those related to environment, in a fair and transparent way, according to a new study by Stanford researchers.

The analysis, published this week in the Proceedings of the Association of Computing Machinery Conference on Fairness, Accountability and Transparency (link is external), assesses machine learning techniques designed to support an initiative by the United States Protection Agency. Environment (EPA) aimed at reducing serious violations of the Clean Water Act. It reveals how two key elements of so-called algorithmic design influence the communities targeted by compliance efforts and, therefore, who bears the burden of pollution violations. The…

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