摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据来自印第安纳州Notre Dame,By NewsRx记者的新闻报道，研究表明：“在各种类型的机器学习(ML)模型中，公平度量对于分析算法偏差至关重要，包括用于搜索相关性、推荐、个性化、人才分析和自然语言处理的模型。然而，公平性测量参数M目前由公平性指标主导，这些指标检查分配和/或预测误差的差异，作为受保护属性或组的单变量关键性能指标(KPIs)。"
Abstract
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.”
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