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

TL;DR

This paper explores how justice is defined and operationalized in child welfare policy to inform the development of ethically aligned AI systems in high-stakes settings.

Contribution

It introduces a systematic method for discovering justice principles in policy to guide AI design, using mixed-methods analysis of New York child welfare policies.

Findings

Identified diverse justice principles in child welfare policy.

Revealed nuanced understandings of justice beyond traditional concepts.

Highlighted the importance of operationalized values in ethical AI development.

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.