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

arXiv: https://arxiv.org/abs/2206.14983 · HTML: https://mlsanity.com/paper/2206.14983/

## 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 (most cited first, 30 of 38)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [Stochastic Optimization for Performative Prediction](https://mlsanity.com/paper/2006.06887/) (2020)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [The Disparate Effects of Strategic Manipulation](https://mlsanity.com/paper/1808.08646/) (2018)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Causal Strategic Linear Regression](https://mlsanity.com/paper/2002.10066/) (2020)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks](https://mlsanity.com/paper/2106.05498/) (2021)
- [Counterfactual Risk Assessments, Evaluation, and Fairness](https://mlsanity.com/paper/1909.00066/) (2019)
- [The age of secrecy and unfairness in recidivism prediction](https://mlsanity.com/paper/1811.00731/) (2018)
- [Residual Unfairness in Fair Machine Learning from Prejudiced Data](https://mlsanity.com/paper/1806.02887/) (2018)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)

## Built on by (most cited first, 30 of 30)

- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints](https://mlsanity.com/paper/2306.13738/) (2023)
- [Making Intelligence: Ethical Values in IQ and ML Benchmarks](https://mlsanity.com/paper/2209.00692/) (2022)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems](https://mlsanity.com/paper/2308.00868/) (2023)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge](https://mlsanity.com/paper/2308.15700/) (2023)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024)
- [Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions](https://mlsanity.com/paper/2505.20692/) (2025)
- [Attesting Distributional Properties of Training Data for Machine Learning](https://mlsanity.com/paper/2308.09552/) (2023)
- [The Value of Prediction in Identifying the Worst-Off](https://mlsanity.com/paper/2501.19334/) (2025)
- [Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift](https://mlsanity.com/paper/2403.00886/) (2024)
- [Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs](https://mlsanity.com/paper/2411.04994/) (2024)
- [Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution](https://mlsanity.com/paper/2508.07111/) (2025)
- [Predictive Performance Comparison of Decision Policies Under Confounding](https://mlsanity.com/paper/2404.00848/) (2024)
- [Does AI Coaching Prepare us for Workplace Negotiations?](https://mlsanity.com/paper/2509.22545/) (2025)
- [Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standards](https://mlsanity.com/paper/2510.18238/) (2025)
- [An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models](https://mlsanity.com/paper/2302.04525/) (2023)
- [The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology](https://mlsanity.com/paper/2401.10877/) (2024)
- [S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving](https://mlsanity.com/paper/2408.08584/) (2024)
- [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design](https://mlsanity.com/paper/2303.14602/) (2023)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variables](https://mlsanity.com/paper/2510.25974/) (2025)
- [Falsifying Discriminant Validity of Predictive Algorithms](https://mlsanity.com/paper/2601.17146/) (2026)
- [Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions](https://mlsanity.com/paper/2605.22612/) (2026)
- [A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance](https://mlsanity.com/paper/2609.16592/) (2026)

## 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.