Algorithmic Decision Making in Public Services: A CSCW-Perspective

dc.contributor.authorFlügge, Asbjörn William Ammitzböll
dc.contributor.authorHildebrandt, Thomas
dc.contributor.authorMöller, Naja Holten
dc.date.accessioned2023-03-17T22:48:52Z
dc.date.available2023-03-17T22:48:52Z
dc.date.issued2020
dc.description.abstractEach 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 there is a widespread concern that these algorithms create a 'black box' of embedded bias, lack of human discretion, transparency or trust. For example, ADM is currently tested in public administration in job placement for prediction of a citizen's risk of long-term unemployment. This research project focus on bringing about research on citizens' 'trust' and 'transparency' from a practice-oriented perspective when algorithms are increasingly introduced in public services such as job placement. We propose a study of citizen-government relations to begin to uncover how computational systems and semi-automated decisions affect the relationship between citizens and caseworker, as they work through the collaborative processes around casework. In this context, our question is: What are citizens and caseworkers' different concepts of trust and transparency? How are casework processes affected as we are beginning to see a closer integration between legal guidelines and computational systems in casework? These questions are of huge importance to get a better understanding of how algorithms are changing the ways society makes decisions in core areas of public services in order to inform the responsible design of technologies in areas such as job placement.en
dc.identifier.doi10.1145/3323994.3369886
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4555
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjectpublic administration
dc.subjecttrust
dc.subjectcollaborative work
dc.subjectcasework.
dc.subjectcivic participation
dc.subjectalgorithmic decision making
dc.subjecttransparency
dc.titleAlgorithmic Decision Making in Public Services: A CSCW-Perspectiveen
dc.typeText/Conference Paper
gi.citation.startPage111–114
gi.citations.count5
gi.citations.elementAuste Simkute, Aditi Surana, Ewa Luger, Michael Evans, Rhianne Jones (2022): XAI for learning: Narrowing down the digital divide between “new” and “old” experts, In: Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference, doi:10.1145/3547522.3547678
gi.citations.elementMelissa A. Valentine, Amanda L. Pratt, Rebecca Hinds, Michael S. Bernstein (2024): The Algorithm and the Org Chart: How Algorithms Can Conflict with Organizational Structures, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(8), doi:10.1145/3686903
gi.citations.elementNicholas Diakopoulos, Daniel Trielli, Grace Lee (2021): Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(5), doi:10.1145/3479550
gi.citations.elementAsbjørn Ammitzbøll Flügge, Naja Holten Møller (2022): The Role of Physical Cues in Co-located and Remote Casework, In: Computer Supported Cooperative Work (CSCW) 2(32), doi:10.1007/s10606-022-09449-0
gi.citations.elementRikke Hagensby Jensen, Stine S. Johansen, Nicolai Brodersen Hansen, Ashna Mahmood Zada, Peter Axel Nielsen (2023): Tensions in Data Journey Activities: Mobilising, Processing, Producing, and Re-purposing Data in Environmental Assessment Practice, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(7), doi:10.1145/3610212
gi.conference.locationSanibel Island, Florida, USA

Files

Collections