Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Impact based fairness framework for socio-technical decision making
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-7788-3986
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-8423-8029
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-7409-5813
2023 (English)In: Proceedings of the 1st workshop on fairness and bias in AIco-located with 26th european conference on artificial intelligence (ECAI 2023) / [ed] Roberta Calegari; Andrea Aler Tubella; Gabriel González Castañe; Virginia Dignum; Michela Milano, CEUR-WS , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Avoiding bias and understanding the consequences of artificial intelligence used in decision making is of high importance to avoid mistreatment and unintended harm. This paper aims to present an impact focused approach to model the information flow of a socio-technical decision system for analysis of bias and fairness. The framework roots otherwise abstract technical accuracy and bias measures in stakeholder effects and forms a scaffold around which further analysis of the socio-technical system and its components can be coordinated. Two example use-cases are presented and analysed.

Place, publisher, year, edition, pages
CEUR-WS , 2023.
Series
CEUR Workshop Proceedings, ISSN 16130073 ; 3523
Keywords [en]
decision-making system, Fairness, information-flow, socio-technical factors
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-217267Scopus ID: 2-s2.0-85177071301OAI: oai:DiVA.org:umu-217267DiVA, id: diva2:1815668
Conference
1st Workshop on Fairness and Bias in AI, AEQUITAS 2023, Krakow, 1 October, 2023.
Funder
EU, Horizon 2020, 101070363Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2023-11-30Bibliographically approved

Open Access in DiVA

fulltext(382 kB)66 downloads
File information
File name FULLTEXT01.pdfFile size 382 kBChecksum SHA-512
176d194d4e8a2bd66a3fa2ce09a30032d5cdbfb54a48a7b513e606bd877ece501a44fb2fb65edb57579be7030cba971ace13b4ea0acfee3a42c4c2565097867a
Type fulltextMimetype application/pdf

Other links

ScopusProceeding

Authority records

Brännström, MattiasJiang, LiliAler Tubella, AndreaDignum, Virginia

Search in DiVA

By author/editor
Brännström, MattiasJiang, LiliAler Tubella, AndreaDignum, Virginia
By organisation
Department of Computing Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 66 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 329 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf