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  1. Justice as fairness: a restatement.John Rawls (ed.) - 2001 - Cambridge: Harvard University Press.
    This book originated as lectures for a course on political philosophy that Rawls taught regularly at Harvard in the 1980s.
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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.
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  • Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking.Priscilla M. Regan & Jolene Jesse - 2019 - Ethics and Information Technology 21 (3):167-179.
    With the increase in the costs of providing education and concerns about financial responsibility, heightened consideration of accountability and results, elevated awareness of the range of teacher skills and student learning styles and needs, more focus is being placed on the promises offered by online software and educational technology. One of the most heavily marketed, exciting and controversial applications of edtech involves the varied educational programs to which different students are exposed based on how big data applications have evaluated their (...)
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  • Fairness: Theory & Practice of Distributive Justice.Nicholas Rescher - 2002 - Transaction.
    In theory and practice, the notion of fairness is far from simple. The principle is often elusive and subject to confusion, even in institutions of law, usage, and custom. In Fairness, Nicholas Rescher aims to liberate this concept from misunderstandings by showing how its definitive characteristics prevent it from being absorbed by such related conceptions as paternalistic benevolence, radical egalitarianism, and social harmonization. Rescher demonstrates that equality before the state is an instrument of justice, not of social utility or public (...)
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