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Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents

Radu Calinescu, Ana Cavalcanti, Marsha Chechik, Lina Marsso, Beverley Townsend

TL;DR

A systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements is proposed and defined for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.

Abstract

As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.

Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents

TL;DR

A systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements is proposed and defined for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.

Abstract

As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.
Paper Structure (4 sections, 2 figures, 2 tables)

This paper contains 4 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. SLEEC Norm Operationalisation Process
  3. Open Challenges
  4. Conclusion

Figures (2)

  • Figure 1: SLEEC norm operationalisation process. Successful completion of all stages is necessary for the deployment of a SLEEC-norm-compliant AI agent; failure at any point precludes the agent's deployment.
  • Figure 2: Stills from the ALMI project stefanakos-2026 demonstration video at https://youtu.be/VhfQmJe4IPc. The images show the robot providing user assistance during a cooking task and after a simulated user fall (right).