If a machine-learning model is trained using an unbalanced dataset, such as one that contains far more images of people with lighter skin than people with darker skin, there is serious risk the ...
Editor’s note: Deep Dive – a feature looking in depth at timely issues from tech to jobs is a regular feature on Wednesdays in TechWire. CHAPEL HILL – The recent explosion of large language models ...
Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and ...
GAINESVILLE, Fla.--(BUSINESS WIRE)--Exactech, a developer and producer of innovative implants, instrumentation, and smart technologies for joint replacement surgery, reports a new study 1 that ...
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, ...
Applying machine learning to a U.S. Environmental Protection Agency initiative, researchers reveal how key design elements determine what communities bear the burden of pollution. The approach could ...
Scientists have introduced a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. Seventy years ago, ...
Bias is machine learning’s original sin. It’s embedded in machine learning’s essence: the system learns from data, and thus is prone to picking up the human biases that the data represents. For ...
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