首页|University of Notre Dame Details Findings in Machine Learning (Should Fairness B e a Metric or a Model? a Model-based Framework for Assessing Bias In Machine Lea rning Pipelines)
University of Notre Dame Details Findings in Machine Learning (Should Fairness B e a Metric or a Model? a Model-based Framework for Assessing Bias In Machine Lea rning Pipelines)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Notre Dame, Indiana, b y NewsRx journalists, research stated, “Fairness measurement is crucial for asse ssing algorithmic bias in various types of machine learning (ML) models, includi ng ones used for search relevance, recommendation, personalization, talent analy tics, and natural language processing. However, the fairness measurement paradig m is currently dominated by fairness metrics that examine disparities in allocat ion and/or prediction error as univariate key performance indicators (KPIs) for a protected attribute or group.”
Notre DameIndianaUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningUnivers ity of Notre Dame
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