mlsanityData to 14 Oct 2026

AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development

arXiv 2411.14442 ↗First posted 5 Nov 2024cs.CY cs.CLcited by 1

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Abstract (arXiv)

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

13 of 38 references.

References · 13

  1. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  2. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  3. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  4. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  5. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 230
  6. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
  7. 2017Vulnerability of Deep Reinforcement Learning to Policy Induction Attackscited by 91
  8. 2022Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examplescited by 69
  9. 2024Building Guardrails for Large Language Modelscited by 65
  10. 2019Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signalscited by 50
  11. 2024Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them incited by 46
  12. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
1 more
  1. 2023Adding guardrails to advanced chatbotscited by 5

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Prompt Injection 2.0: Hybrid AI Threatscited by 13

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  1. 2026Building the ethical AI framework of the future: from philosophy to practice
  2. 2024Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative Values
  3. 2025From Values to Frameworks: A Qualitative Study of Ethical Reasoning in Agentic AI Practitioners
  4. 2021The Challenge of Value Alignment: from Fairer Algorithms to AI Safety
  5. 2026REBAR: Reference Ethical Benchmark for Autonomy Readiness
  6. 2026Operationalizing Ethics for AI Agents: How Developers Encode Values into Repository Context Files
  7. 2026Pluralism in AI Governance: Toward Sociotechnical Alignment and Normative Coherence ↗
  8. 2024Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology