Egalitarian Machine Learning

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets modern life. Ethical concerns about this widespread practice have given rise to field fair learning and a number fairness measures, mathematically precise definitions that purport determine whether prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in context be placeholder for variety normative egalitarian considerations. We explore few measures suss out their roots evaluate them, both as formalizations ideas assertions what demands predictive systems. pay special attention recent popular measure, counterfactual fairness, holds prediction an individual if it same actual world any where belongs different demographic group (cf. Kusner et al. (2018)).