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Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

Authors:

Venkatesh Sivaraman,

Logan Stapleton,

Yanghuidi Cheng,

Zhiwei Steven Wu,

Kenneth HolsteinAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 52, Pages 1 - 18

https://doi.org/10.1145/3491102.3517439

Published: 28 April 2022 Publication History

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Abstract

AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers’ experiences with these systems in practice. In this paper, we present findings from a series of interviews and contextual inquiries at a child welfare agency, to understand how they currently make AI-assisted child maltreatment screening decisions. Overall, we observe how workers’ reliance upon the ADS is guided by (1) their knowledge of rich, contextual information beyond what the AI model captures, (2) their beliefs about the ADS’s capabilities and limitations relative to their own, (3) organizational pressures and incentives around the use of the ADS, and (4) awareness of misalignments between algorithmic predictions and their own decision-making objectives. Drawing upon these findings, we discuss design implications towards supporting more effective human-AI decision-making.

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Cited By

Thakur PSaxena C(2024)From Technostress to TurnoverModern Trends and Future Innovation in Human Resource Management10.4018/979-8-3693-6402-4.ch004(105-136)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-6402-4.ch004
Villumsen ARosholm MBodilsen SToft SBerg LNirmalarajan L(2024)Ethical considerations in research when building predictive risk modelling in child and family welfareJournal of Comparative Social Work10.31265/jcsw.v19i1.61919:1(102-126)Online publication date: 3-Oct-2024
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Index Terms

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
1. Applied computing
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

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Published: 28 April 2022

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https://doi.org/10.4018/979-8-3693-6402-4.ch004
Villumsen ARosholm MBodilsen SToft SBerg LNirmalarajan L(2024)Ethical considerations in research when building predictive risk modelling in child and family welfareJournal of Comparative Social Work10.31265/jcsw.v19i1.61919:1(102-126)Online publication date: 3-Oct-2024
https://doi.org/10.31265/jcsw.v19i1.619
Zhang ZFeger SDullenkopf LLiao RSüsslin LLiu YButz A(2024)Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making ProcessProceedings of the ACM on Human-Computer Interaction10.1145/36870248:CSCW2(1-32)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687024
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