# Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design

arXiv: https://arxiv.org/abs/2303.14602 · HTML: https://mlsanity.com/paper/2303.14602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (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)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems](https://mlsanity.com/paper/2102.01196/) (2021)
- [Algorithms and Decision-Making in the Public Sector](https://mlsanity.com/paper/2106.03673/) (2021)
- [Designing for human-AI complementarity in K-12 education](https://mlsanity.com/paper/2104.01266/) (2021)
- [Leveraging Expert Consistency to Improve Algorithmic Decision Support](https://mlsanity.com/paper/2101.09648/) (2021)

## Abstract (arXiv)

AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research has called for increased scrutiny around the validity of AI system designs. However, in real-world settings, it is often not possible to fully address questions around the validity of an AI tool without also considering the design of associated organizational and public policies. Yet, considerations around how an AI tool may interface with policy are often only discussed retrospectively, after the tool is designed or deployed. In this short position paper, we discuss opportunities to promote multi-stakeholder deliberations around the design of AI-based technologies and associated policies, at the earliest stages of a new project.