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

• URL: http://arxiv.org/abs/2511.08844v1
• Date: Thu, 13 Nov 2025 01:11:46 GMT
• Title: Operationalizing Justice: Towards the Development of a Principle Based Design Framework for Human Services AI
• Authors: Maria Y. Rodriguez, Seventy Hall, Pranav Sankhe, Melanie Sage, Winnie Chen, Atri Rudra, Kenny Joseph,
• Abstract summary: We conduct a mixed-methods analysis of child welfare policy in the state of New York.<n>We find a range of functional definitions of justice (which we term principles)<n>These principles reflect more nuanced understandings of justice across a spectrum of contexts.
• Score: 4.80173680296898
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• How May U.S. Courts Scrutinize Their Recidivism Risk Assessment Tools? Contextualizing AI Fairness Criteria on a Judicial Scrutiny-based Framework [18.509813368002]
  We conduct legal research to identify if and how technical AI conceptualizations of fairness surface in primary legal sources.<n>We propose a new framework, integrating U.S. demographics-related legal scrutiny concepts and technical fairness criteria.
  arXiv  Detail & Related papers  (2025-05-05T15:59:57Z)
• Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principles [30.405680322319242]
  The Helpful, Honest, and Harmless (HHH) principle is a framework for aligning AI systems with human values.<n>We argue for an adaptive interpretation of the HHH principle and propose a reference framework for its adaptation to diverse scenarios.<n>This work offers practical insights for improving AI alignment, ensuring that HHH principles remain both grounded and operationally effective in real-world AI deployment.
  arXiv  Detail & Related papers  (2025-02-09T22:41:24Z)
• Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [51.85131234265026]
  I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.<n>I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
  arXiv  Detail & Related papers  (2024-12-06T15:36:13Z)
• Fairness in AI: challenges in bridging the gap between algorithms and law [2.651076518493962]
  We identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. We introduce a set of core criteria that need to be taken into account when selecting a specific fairness definition for real-world use case applications.
  arXiv  Detail & Related papers  (2024-04-30T08:59:00Z)
• AI Fairness in Practice [0.46671368497079174]
  There is a broad spectrum of views across society on what the concept of fairness means and how it should be put to practice. This workbook explores how a context-based approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.
  arXiv  Detail & Related papers  (2024-02-19T23:02:56Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Toward a Theory of Justice for Artificial Intelligence [2.28438857884398]
  It holds that the basic structure of society should be understood as a composite of socio-technical systems. As a consequence, egalitarian norms of justice apply to the technology when it is deployed in these contexts.
  arXiv  Detail & Related papers  (2021-10-27T13:23:38Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• An Impact Model of AI on the Principles of Justice: Encompassing the Autonomous Levels of AI Legal Reasoning [0.0]
  It is argued that the infusion of AI into existing and future legal activities and the judicial structure needs to be undertaken by mindfully observing an alignment with the core principles of justice. By examining the principles of justice across the Levels of Autonomy (LoA) of AI Legal Reasoning, the case is made that there is an ongoing tension underlying the efforts to develop and deploy AI.
  arXiv  Detail & Related papers  (2020-08-26T22:56:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.