Algorithmic decision making in public administration: A CSCW-perspective

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

In this paper, I propose a study of algorithmic decision making in public administration from a computer supported cooperative work (CSCW) perspective. Each day the public administration makes thousands of decisions with consequences for the welfare of its citizens. An increasing number of such decisions are supported or made by algorithmic decision making (ADM) systems, yet in the scientific and public sphere there is a growing concern that these algorithms become a 'black box' possibly containing hidden bias (Olsen et al., 2019), obstacles for human discretion (Rason, 2017), low transparency (Alkhatib and Bernstein, 2019) or trust (Mittelstadt et al. 2016). For example, ADM is currently tested in public administration in job placement for the prediction of a citizen's risk of long-term unemployment.

Original languageEnglish
Title of host publicationGROUP 2020 - Companion of the 2020 ACM International Conference on Supporting Group Work
PublisherAssociation for Computing Machinery
Publication date2020
Pages15-24
ISBN (Electronic)9781450367677
DOIs
Publication statusPublished - 2020
Event21st ACM International Conference on Supporting Group Work, GROUP 2020 - Sanibel Island, United States
Duration: 6 Jan 20208 Jan 2020

Conference

Conference21st ACM International Conference on Supporting Group Work, GROUP 2020
LandUnited States
BySanibel Island
Periode06/01/202008/01/2020
SponsorACM SIGCHI

    Research areas

  • Algorithmic decision making, Casework, Civic participation, Collaborative work, Public administration, Transparency, Trust

ID: 240685549