mlsanityData to 14 Oct 2026

A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms

arXiv 2206.14983 ↗First posted 30 Jun 2022cs.LG cs.CY cs.HCcited by 30

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Abstract (arXiv)

Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improve value alignment in these tools, comparatively less work has centered concerns around the fundamental justifiability of using these tools. This work seeks to center validity considerations in deliberations around whether and how to build data-driven algorithms in high-stakes domains. Toward this end, we translate key concepts from validity theory to predictive algorithms. We apply the lens of validity to re-examine common challenges in problem formulation and data issues that jeopardize the justifiability of using predictive algorithms and connect these challenges to the social science discourse around validity. Our interdisciplinary exposition clarifies how these concepts apply to algorithmic decision making contexts. We demonstrate how these validity considerations could distill into a series of high-level questions intended to promote and document reflections on the legitimacy of the predictive task and the suitability of the data.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 38 of its 132 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

38 of 132 references.

References · 38

  1. 2018Datasheets for Datasetscited by 1,386
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  4. 2020Performative Predictioncited by 327
  5. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
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  11. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 147
  12. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
26 more
  1. 2017Strategic Classification from Revealed Preferencescited by 128
  2. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  3. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  4. 2019FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learningcited by 93
  5. 2020Stochastic Optimization for Performative Predictioncited by 93
  6. 2022The Fallacy of AI Functionalitycited by 88
  7. 2019Problem Formulation and Fairnesscited by 85
  8. 2018The Disparate Effects of Strategic Manipulationcited by 75
  9. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
  10. 2020Causal Strategic Linear Regressioncited by 65
  11. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
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  21. 2021A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfarecited by 23
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 9; 2024: 9; 2025: 8; 2026: 3.

30 papers.

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  4. 2023Counterfactual Prediction Under Outcome Measurement Errorcited by 10
  5. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  6. 2023Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systemscited by 9
  7. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  8. 2023Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledgecited by 6
  9. 2024"This is not a data problem": Algorithms and Power in Public Higher Education in Canadacited by 6
  10. 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Usecited by 6
  11. 2025Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptionscited by 6
  12. 2023Attesting Distributional Properties of Training Data for Machine Learningcited by 5
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  3. 2024Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needscited by 3
  4. 2025Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolutioncited by 3
  5. 2024Predictive Performance Comparison of Decision Policies Under Confoundingcited by 2
  6. 2025Does AI Coaching Prepare us for Workplace Negotiations?cited by 2
  7. 2025Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standardscited by 2
  8. 2023An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Modelscited by 1
  9. 2024The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technologycited by 1
  10. 2024S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Drivingcited by 1
  11. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  12. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  13. 2023Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Designcited by 0
  14. 2024Beyond Predictive Algorithms in Child Welfarecited by 0
  15. 2025Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variablescited by 0
  16. 2026Falsifying Discriminant Validity of Predictive Algorithmscited by 0
  17. 2026Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptionscited by 0
  18. 2026A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidancecited by 0