Fair & Responsible AI Workshop @ CHI2020

Designing for Human-Centered AI in the U.S.Child-Welfare System


Workshop paper


Devansh Saxena, Shion Guha

Abstract
Child-Welfare System (CWS) in many states in the United States has come under escalating public and media scrutiny because of the potential damage done to children who are removed from the care of their parents. CWS has increasingly turned towards artificial intelligence (AI) as a way of standardizing decisions and demonstrating that these decisions are unbiased and evidence-based. Moreover, CWS in almost every state is underfunded and AI systems, from the perspective of policymakers, offer a potential means to reduce costs. Our research focuses on the collaborative work of child-welfare teams that participate in meetings mediated by policy, practice, and algorithms. In the following paragraphs, we first offer some background context in regards to the child-welfare system in the state of Wisconsin. Next, we establish a need for human-centered AI, and finally, we discuss how strategies proposed by human-centered algorithm design (HCAD) can help inform the development of algorithms in CWS.

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Cite

APA
Saxena, D., & Guha, S. Designing for Human-Centered AI in the U.S.Child-Welfare System.

Chicago/Turabian
Saxena, Devansh, and Shion Guha. Designing for Human-Centered AI in the U.S.Child-Welfare System, n.d.

MLA
Saxena, Devansh, and Shion Guha. Designing for Human-Centered AI in the U.S.Child-Welfare System.