Conference item
'It's reducing a human being to a percentage'; Perceptions of justice in algorithmic decisions
- Abstract:
- Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to ‘meaningful information about the logic’ behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people’s perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles—under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no ‘best’ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 233.6KB, Terms of use)
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- Publisher copy:
- 10.1145/3173574.3173951
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- ACM CHI Conference on Human Factors in Computing Systems (CHI 2018)
- Journal:
- ACM CHI Conference on Human Factors in Computing Systems (CHI 2018) More from this journal
- Pages:
- 1-14
- Article number:
- 377
- Publication date:
- 2018-04-21
- Acceptance date:
- 2018-02-12
- DOI:
- Keywords:
- Pubs id:
-
pubs:827812
- UUID:
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uuid:633f21a3-96a2-4f54-ac0b-e067c39f8e1f
- Local pid:
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pubs:827812
- Source identifiers:
-
827812
- Deposit date:
-
2018-03-05
Terms of use
- Copyright holder:
- Binns et al
- Copyright date:
- 2018
- Notes:
- © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. This is the accepted manuscript version of the article. The final version is available online from the Association for Computing Machinery at: http://dx.doi.org/10.1145/3173574.3173951
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