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

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

International Conference on Human Factors in Computing Systems (CHI), 2022
5 April 2022
Anna Kawakami
Venkatesh Sivaraman
H. Cheng
Logan Stapleton
Yanghuidi Cheng
Diana Qing
Adam Perer
Zhiwei Steven Wu
Haiyi Zhu
Kenneth Holstein
ArXiv (abs)PDFHTML

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

11 / 61 papers shown
Title
Soliciting Stakeholders' Fairness Notions in Child Maltreatment
  Predictive Systems
Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive SystemsInternational Conference on Human Factors in Computing Systems (CHI), 2021
H. Cheng
Logan Stapleton
Ruiqi Wang
Paige E Bullock
Alexandra Chouldechova
Zhiwei Steven Wu
Haiyi Zhu
FaML
110
76
0
01 Feb 2021
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Maria De-Arteaga
Vincent Jeanselme
A. Dubrawski
Alexandra Chouldechova
385
23
0
24 Jan 2021
Does the Whole Exceed its Parts? The Effect of AI Explanations on
  Complementary Team Performance
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal
Tongshuang Wu
Joyce Zhou
Raymond Fok
Besmira Nushi
Ece Kamar
Marco Tulio Ribeiro
Daniel S. Weld
367
718
0
26 Jun 2020
A Human-Centered Review of the Algorithms used within the U.S. Child
  Welfare System
A Human-Centered Review of the Algorithms used within the U.S. Child Welfare SystemInternational Conference on Human Factors in Computing Systems (CHI), 2020
Devansh Saxena
Karla A. Badillo-Urquiola
Pamela J. Wisniewski
Shion Guha
164
115
0
07 Mar 2020
Keeping Community in the Loop: Understanding Wikipedia Stakeholder
  Values for Machine Learning-Based Systems
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based SystemsInternational Conference on Human Factors in Computing Systems (CHI), 2020
C. E. Smith
Bowen Yu
Anjali Srivastava
Aaron L Halfaker
Loren G. Terveen
Haiyi Zhu
KELM
186
75
0
14 Jan 2020
The What-If Tool: Interactive Probing of Machine Learning Models
The What-If Tool: Interactive Probing of Machine Learning ModelsIEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), 2019
James Wexler
Mahima Pushkarna
Tolga Bolukbasi
Martin Wattenberg
F. Viégas
Jimbo Wilson
VLM
180
546
0
09 Jul 2019
Unremarkable AI: Fitting Intelligent Decision Support into Critical,
  Clinical Decision-Making Processes
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
Qian Yang
Aaron Steinfeld
John Zimmerman
148
258
0
21 Apr 2019
Investigating Human + Machine Complementarity for Recidivism Predictions
Investigating Human + Machine Complementarity for Recidivism Predictions
S. Tan
Julius Adebayo
K. Quinn
Ece Kamar
FaML
193
55
0
28 Aug 2018
Manipulating and Measuring Model Interpretability
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh
D. Goldstein
Jake M. Hofman
Jennifer Wortman Vaughan
Hanna M. Wallach
270
756
0
21 Feb 2018
Fairness and Accountability Design Needs for Algorithmic Support in
  High-Stakes Public Sector Decision-Making
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
Michael Veale
Max Van Kleek
Reuben Binns
157
450
0
03 Feb 2018
Building machines that adapt and compute like brains
Building machines that adapt and compute like brains
Brenden M. Lake
J. Tenenbaum
AI4CEFedMLNAIAILaw
447
872
0
11 Nov 2017
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