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Ground(less) Truth: A Causal Framework for Proxy Labels in
  Human-Algorithm Decision-Making
v1v2v3v4 (latest)

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

Conference on Fairness, Accountability and Transparency (FAccT), 2023
13 February 2023
Luke M. Guerdan
Amanda Coston
Zhiwei Steven Wu
Kenneth Holstein
    CML
ArXiv (abs)PDFHTML

Papers citing "Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making"

19 / 19 papers shown
Title
Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks
Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks
Luke M. Guerdan
Devansh Saxena
Stevie Chancellor
Zhiwei Steven Wu
Kenneth Holstein
101
1
0
03 Jul 2025
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen Pfohl
Natalie Harris
Chirag Nagpal
David Madras
Vishwali Mhasawade
...
Nnamdi Ezeanochie
Heather Cole-Lewis
Katherine Heller
Sanmi Koyejo
Alexander D’Amour
210
2
0
04 Jun 2025
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
AI Mismatches: Identifying Potential Algorithmic Harms Before AI DevelopmentInternational Conference on Human Factors in Computing Systems (CHI), 2025
Devansh Saxena
Ji-Youn Jung
Jodi Forlizzi
Kenneth Holstein
John Zimmerman
240
7
0
25 Feb 2025
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language
  Models
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models
Eddie L. Ungless
Nikolas Vitsakis
Zeerak Talat
James Garforth
Bjorn Ross
Arno Onken
Atoosa Kasirzadeh
Alexandra Birch
149
3
0
17 Oct 2024
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine
  Learning
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine LearningNeural Information Processing Systems (NeurIPS), 2024
Jake Fawkes
Nic Fishman
Mel Andrews
Zachary C. Lipton
216
3
0
12 Oct 2024
From Biased Selective Labels to Pseudo-Labels: An
  Expectation-Maximization Framework for Learning from Biased Decisions
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions
Trenton Chang
Jenna Wiens
185
0
0
27 Jun 2024
The Neutrality Fallacy: When Algorithmic Fairness Interventions are
  (Not) Positive Action
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action
Hilde J. P. Weerts
Raphaële Xenidis
Fabien Tarissan
Henrik Palmer Olsen
Mykola Pechenizkiy
FaML
101
9
0
18 Apr 2024
Predictive Performance Comparison of Decision Policies Under Confounding
Predictive Performance Comparison of Decision Policies Under Confounding
Luke M. Guerdan
Amanda Coston
Kenneth Holstein
Zhiwei Steven Wu
OffRL
262
0
0
01 Apr 2024
Does AI help humans make better decisions? A methodological framework
  for experimental evaluation
Does AI help humans make better decisions? A methodological framework for experimental evaluationProceedings of the National Academy of Sciences of the United States of America (PNAS), 2024
Eli Ben-Michael
D. J. Greiner
Melody Huang
Kosuke Imai
Zhichao Jiang
Sooahn Shin
105
6
0
18 Mar 2024
Towards Optimizing Human-Centric Objectives in AI-Assisted
  Decision-Making With Offline Reinforcement Learning
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning
Zana Buçinca
S. Swaroop
Amanda E. Paluch
Susan A. Murphy
Krzysztof Z. Gajos
136
14
0
09 Mar 2024
Evaluating and Correcting Performative Effects of Decision Support
  Systems via Causal Domain Shift
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
Philip A. Boeken
O. Zoeter
Joris M. Mooij
185
3
0
01 Mar 2024
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Tzu-Sheng Kuo
Aaron L Halfaker
Zirui Cheng
Jiwoo Kim
Meng-Hsin Wu
Tongshuang Wu
Kenneth Holstein
Haiyi Zhu
155
34
0
21 Feb 2024
Are We Asking the Right Questions?: Designing for Community
  Stakeholders' Interactions with AI in Policing
Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing
Md. Romael Haque
Devansh Saxena
Katherine Weathington
Joseph Chudzik
Shion Guha
171
15
0
08 Feb 2024
Training Towards Critical Use: Learning to Situate AI Predictions
  Relative to Human Knowledge
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human KnowledgeInternational Conference on Climate Informatics (ICCI), 2023
Anna Kawakami
Luke M. Guerdan
Yanghuidi Cheng
Matthew L. Lee
Scott A. Carter
Nikos Arechiga
Kate Glazko
Haiyi Zhu
Kenneth Holstein
134
11
0
30 Aug 2023
Algorithmic Harms in Child Welfare: Uncertainties in Practice,
  Organization, and Street-level Decision-Making
Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-MakingACM Journal on Responsible Computing (JRC), 2023
Devansh Saxena
Shion Guha
139
27
0
09 Aug 2023
Open Problems and Fundamental Limitations of Reinforcement Learning from
  Human Feedback
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper
Xander Davies
Claudia Shi
T. Gilbert
Jérémy Scheurer
...
Erdem Biyik
Anca Dragan
David M. Krueger
Dorsa Sadigh
Dylan Hadfield-Menell
ALMOffRL
257
660
0
27 Jul 2023
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
Stephen Casper
Jason Lin
Joe Kwon
Gatlen Culp
Dylan Hadfield-Menell
AAML
160
115
0
15 Jun 2023
Correcting for Selection Bias and Missing Response in Regression using
  Privileged Information
Correcting for Selection Bias and Missing Response in Regression using Privileged InformationConference on Uncertainty in Artificial Intelligence (UAI), 2023
Philip A. Boeken
Noud de Kroon
Mathijs de Jong
Joris M. Mooij
O. Zoeter
131
4
0
29 Mar 2023
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate
  Human-ML Complementarity
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML ComplementarityProceedings of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022
Charvi Rastogi
Liu Leqi
Kenneth Holstein
Hoda Heidari
177
19
0
22 Apr 2022
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