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  1. Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?Paul Laat - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected (...)
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  • Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?Massimo Durante & Marcello D'Agostino - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...)
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  • Ethics in artificial intelligence: introduction to the special issue.Virginia Dignum - 2018 - Ethics and Information Technology 20 (1):1-3.
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  • Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?Paul B. de Laat - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...)
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