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Algorithmic Bias: On the Implicit Biases of Social Technology

Johnson, Gabbrielle (2020) Algorithmic Bias: On the Implicit Biases of Social Technology. [Preprint]

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Abstract

Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call 'the Proxy Problem'. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Johnson, GabbrielleM0000-0003-1463-4496
Keywords: algorithmic bias, implicit bias, bias, fairness
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Philosophers of Science
General Issues > Social Epistemology of Science
Specific Sciences > Psychology > Social Psychology
General Issues > Technology
General Issues > Values In Science
Depositing User: Dr. Gabbrielle Johnson
Date Deposited: 11 May 2020 03:07
Last Modified: 11 May 2020 03:07
Item ID: 17169
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Cognitive Science > Computation
Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Philosophers of Science
General Issues > Social Epistemology of Science
Specific Sciences > Psychology > Social Psychology
General Issues > Technology
General Issues > Values In Science
Date: 10 May 2020
URI: http://philsci-archive.pitt.edu/id/eprint/17169

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