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- Conference PaperMetadata onlyAlgorithmic Decision Making in Public Administration: A CSCW-Perspective(Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Flügge, Asbjörn AmmitzböllIn 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. Following prior research questioning the usefulness of the black box metaphor, my interest is to understand how caseworkers' and citizens understand ADM, as a basis for design of CSCW technologies employing ADM.
- Conference PaperMetadata onlyAlgorithmic Decision Making in Public Services: A CSCW-Perspective(Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Flügge, Asbjörn William Ammitzböll; Hildebrandt, Thomas; Möller, Naja HoltenEach 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.
- Conference PaperOpen AccessTRADE: A Transparent, Decentralized Traceability System for the Supply Chain(Proceedings of 1st ERCIM Blockchain Workshop 2018, 2018) El Maouchi, Mourad; Ersoy, O˘guzhan; Erkin, ZekeriyaTraceability has become an increasingly important aspect of the supply chain in the last few years due to customer awareness as well as better planning and problem identification. Unfortunately, technological, legal, and organizational concerns limit the possibility to utilize a centralized system to achieve traceability. Trust is one of the most important factors preventing the appliance of a centralized system. Previous works provided several approaches to create a decentralized traceability system. However, these works do not state the feasibility of their work and its appliance for the supply chain. In this paper, we propose a fully transparent and decentralized traceability system for the supply chain, namely TRADE. The system leverages the actors and supply chain structure to achieve traceability. Moreover, consumers and other parties can view all the data in the system and verify the claims of actors on the products. The latter results in positive brand reputation and auditability.