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Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems

In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282 (2019)

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  1. Assembled Bias: Beyond Transparent Algorithmic Bias.Robyn Repko Waller & Russell L. Waller - forthcoming - Minds and Machines:1-30.
    In this paper we make the case for the emergence of novel kind of bias with the use of algorithmic decision-making systems. We argue that the distinctive generative process of feature creation, characteristic of machine learning, contorts feature parameters in ways that can lead to emerging feature spaces that encode novel algorithmic bias involving already marginalized groups. We term this bias assembled bias. Moreover, assembled biases are distinct from the much-discussed algorithmic bias, both in source and in content. As such, (...)
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  • Presumptuous Aim Attribution, Conformity, and the Ethics of Artificial Social Cognition.Owen C. King - 2020 - Ethics and Information Technology 22 (1):25-37.
    Imagine you are casually browsing an online bookstore, looking for an interesting novel. Suppose the store predicts you will want to buy a particular novel: the one most chosen by people of your same age, gender, location, and occupational status. The store recommends the book, it appeals to you, and so you choose it. Central to this scenario is an automated prediction of what you desire. This article raises moral concerns about such predictions. More generally, this article examines the ethics (...)
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