首页|University of Florida Researcher Has Published New Study Findings on Machine Lea rning (Goal Orientation for Fair Machine Learning Algorithms)
University of Florida Researcher Has Published New Study Findings on Machine Lea rning (Goal Orientation for Fair Machine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news originating from the University of Florida by News Rx correspondents, research stated, "A key challenge facing the use of machine l earning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minoritie s." Financial supporters for this research include National Science Foundation. Our news editors obtained a quote from the research from University of Florida: "To address this challenge, a rich literature of Fairness-Aware ML (FAML) algori thms has emerged, attempting to ameliorate biases while maintaining the predicti ve accuracy of ML algorithms. Almost all existing FAML algorithms define their o ptimization goals according to a selection task, meaning that ML outputs are ass umed to be the final selection outcome. In practice, though, ML outputs are rare ly used as-is. In personnel selection, for example, ML often serves a support ro le to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection t ask. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate."
University of FloridaAlgorithmsCybor gsEmerging TechnologiesMachine Learning
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