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  1. Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...)
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  2. Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs.Michelle Seng Ah Lee & Luciano Floridi - 2020 - Minds and Machines 31 (1):165-191.
    To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use (...)
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    Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-offs.Michelle Seng Ah Lee & Luciano Floridi - 2021 - In Josh Cowls & Jessica Morley (eds.), The 2020 Yearbook of the Digital Ethics Lab. Springer Verlag. pp. 145-171.
    To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decision-making processes. Using U.S. mortgage lending as an example use (...)
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