Algorithmic bias: on the implicit biases of social technology.
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 arg...
| Published in: | Synthese Vol. 198; no. 10; pp. 9941 - 9962 |
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| Format: | Article |
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Springer Nature
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152559383&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152559383 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Oct2021 vid: 198 iid: 10 pid: 237 pub: Springer Nature artinfo: ui: 152559383 10.1007/s11229-020-02696-y ppf: 9941 ppct: 21 formats: fmt: @attributes: type: P size: 919KB tig: atl: Algorithmic bias: on the implicit biases of social technology. aug: au: Johnson, Gabbrielle M. affil: New York University, New York, USA su: Cognitive bias Information processing Machine learning Computer scientists Social services sug: subj: Cognitive bias Information processing Machine learning Computer scientists Social services keyword: Algorithmic bias Bias Implicit bias Machine bias Social bias ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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