Racist in the Machine: The Disturbing Implications of Algorithmic Bias

By M.E.G. García. 2016

186 citations recorded by the source corpus.

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Citations in the corpus

Assessing gender bias in machine translation: a case study with Google Translate (2018, background): “risome results in machine learning have somewhat supported Schiebinger’s view. Not only Google photos’ statistical image labeling algorithm has been found to classify dark-skinned people as gorillas (Garcia, 2016) and purportedly intelligent programs have been suggested to be negatively biased again”
Algorithmic bias: review, synthesis, and future research directions (2021, background): “For instance, GDPR and similar regulations that include right to explanation provisions allow individuals to demand an explanation for the output of an algorithm and exercise their rights to contest automated and machine-assisted decisions (Almada, 2019; Garcia, 2016).”
How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, background): “We speculate that Managers may become more involved in this stage in the future, as bias issues become more salient in industry and academia [1, 26, 35].”
The Political Biases of ChatGPT (2023, background): “The topic of algorithmic bias has received an increasing amount of attention in the machine learning academic literature ( Wikipedia 2023a; Kirkpatrick 2016; Cowgill and Tucker 2017; Garcia 2016; Hajian et al. 2016).”
Algorithmic Bias in Education (2021, background): “Examples of algorithmic bias cross contexts, from criminal justice (Angwin et al., 2016), to medicine (O’Reilly-Shah et al., 2020), to computer vision (Klare et al., 2012), to hiring (Garcia, 2016).”