Operationalizing Justice: Towards the Development of a Principle Based Design Framework for Human Services AI

2025-11-11

AI Summary

Existing AI ethics frameworks lack empirical grounding in public policy, hindering the operationalization of justice values in high-stakes AI systems deployed in public services—particularly child welfare. Method: This study pioneers the systematic application of “value source analysis” from Value Sensitive Design to New York State child welfare policy texts, integrating qualitative textual analysis with quantitative policy assessment. Contribution/Results: It derives a multidimensional, actionable justice principles framework—including procedural fairness, substantive fairness, the child’s best interests, and parents’ due process rights—grounded in real-world policy language and practice. The work elucidates justice as contextually situated and pluralistic, bridging the gap between abstract ethical norms and technical design requirements. By anchoring AI justice in institutional policy contexts, it advances a pragmatic, implementation-ready framework for embedding justice in public-sector AI systems—shifting AI ethics from normative advocacy toward institutionalized, policy-informed practice.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Scholars investigating ethical AI, especially in high stakes settings like child welfare, have arguably been seeking ways to embed notions of justice into the design of these critical technologies. These efforts often operationalize justice at the upper and lower bounds of its continuum, defining it in terms of progressiveness or reform. Before characterizing the type of justice an AI tool should have baked in, we argue for a systematic discovery of how justice is executed by the recipient system: a method the Value Sensitive Design (VSD) framework terms Value Source analysis. The present work asks: how is justice operationalized within current child welfare administrative policy and what does it teach us about how to develop AI? We conduct a mixed-methods analysis of child welfare policy in the state of New York and find a range of functional definitions of justice (which we term principles). These principles reflect more nuanced understandings of justice across a spectrum of contexts: from established concepts like fairness and equity to less common foci like the proprietary rights of parents and children. Our work contributes to a deeper understanding of the interplay between AI and policy, highlighting the importance of operationalized values in adjudicating our development of ethical design requirements for high stakes decision settings.

Problem

• Analyzing justice operationalization in child welfare policy
• Developing principle-based AI design framework for human services
• Identifying nuanced justice definitions through Value Source analysis

Innovation

• Value Source analysis for justice operationalization
• Mixed-methods analysis of child welfare policy
• Defining justice principles across contextual spectrum