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      Biased Face Recognition Technology Used by Government: A Problem for Liberal Democracy

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          Abstract

          This paper presents a novel philosophical analysis of the problem of law enforcement’s use of biased face recognition technology (FRT) in liberal democracies. FRT programs used by law enforcement in identifying crime suspects are substantially more error-prone on facial images depicting darker skin tones and females as compared to facial images depicting Caucasian males. This bias can lead to citizens being wrongfully investigated by police along racial and gender lines. The author develops and defends “A Liberal Argument Against Biased FRT,” which concludes that law enforcement use of biased FRT is inconsistent with the classical liberal requirement that government treat all citizens equally before the law. Two objections to this argument are considered and shown to be unsound. The author concludes by suggesting that equality before the law should be preserved while the problem of machine bias ought to be resolved before FRT and other types of artificial intelligence (AI) are deployed by governments in liberal democracies.

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          Most cited references27

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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review.

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              Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

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                Author and article information

                Contributors
                Lalovareigns@aol.com
                Journal
                Philos Technol
                Philos Technol
                Philosophy & Technology
                Springer Netherlands (Dordrecht )
                2210-5433
                2210-5441
                25 September 2021
                : 1-25
                Affiliations
                Pennsylvania, USA
                Author information
                http://orcid.org/0000-0002-3825-2743
                Article
                478
                10.1007/s13347-021-00478-z
                8475322
                04f19bc8-234f-4f62-800e-ce3cd8aa089c
                © The Author(s), under exclusive licence to Springer Nature B.V. 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 7 June 2020
                : 8 September 2021
                Categories
                Research Article

                Philosophy of science
                face recognition technology,classical liberalism,philosophy,ethics,ethics of technology,ethical issues in law enforcement,political philosophy,machine ethics,artificial intelligence and bias,ethics and discrimination

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