TY - GEN

T1 - Algorithmic decision making in public services

T2 - 21st ACM International Conference on Supporting Group Work, GROUP 2020

AU - Flügge, Asbjørn Ammitzbøll

AU - Hildebrandt, Thomas

AU - Møller, Naja Holten

PY - 2020

Y1 - 2020

N2 - 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 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.

AB - 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 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.

KW - Algorithmic decision making

KW - Casework

KW - Civic participation

KW - Collaborative work

KW - Public administration

KW - Transparency

KW - Trust

UR - http://www.scopus.com/inward/record.url?scp=85078361324&partnerID=8YFLogxK

U2 - 10.1145/3323994.3369886

DO - 10.1145/3323994.3369886

M3 - Article in proceedings

AN - SCOPUS:85078361324

SP - 111

EP - 114

BT - GROUP 2020 - Companion of the 2020 ACM International Conference on Supporting Group Work

PB - Association for Computing Machinery

Y2 - 6 January 2020 through 8 January 2020

ER -