Conducting Participatory Design to Improve Algorithms in Public Services: Lessons and Challenges
Devansh Saxena (Marquette University); Shion Guha (Marquette University)

CSCW '20: ACM Conference on Computer-Supported Cooperative Work and Social Computing
Session: Data Science

Abstract
Government agencies are increasingly looking towards algorithmic decision-making systems as a means to reduce costs and optimize processes. However, these algorithms are being constructed in an opaque and isolated manner with calls to adopt a more participatory approach such that stakeholders become co-designers in the process. We share our experiences from conducting participatory design to improve algorithms in the Child-Welfare System. We discuss a policy-mandated algorithm and an agency-level theory-driven algorithm to show how tensions arise when the values of workers are not embedded in the design of an algorithm.

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Pre-recorded for the ACM ACM Conference on Computer-Supported Cooperative Work and Social Computing, October 17-21, 2020.

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