Fuzzy Representation of Norms

• URL: http://arxiv.org/abs/2601.04249v1
• Date: Tue, 06 Jan 2026 12:51:18 GMT
• Title: Fuzzy Representation of Norms
• Authors: Ziba Assadi, Paola Inverardi,
• Abstract summary: This paper proposes a logical representation of SLEEC rules and presents a methodology to embed these ethical requirements using test-score semantics and fuzzy logic.<n>The use of fuzzy logic is motivated by the view of ethics as a domain of possibilities, which allows the resolution of ethical dilemmas that AI systems may encounter.
• Score: 1.0098114696565863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous systems (AS) powered by AI components are increasingly integrated into the fabric of our daily lives and society, raising concerns about their ethical and social impact. To be considered trustworthy, AS must adhere to ethical principles and values. This has led to significant research on the identification and incorporation of ethical requirements in AS system design. A recent development in this area is the introduction of SLEEC (Social, Legal, Ethical, Empathetic, and Cultural) rules, which provide a comprehensive framework for representing ethical and other normative considerations. This paper proposes a logical representation of SLEEC rules and presents a methodology to embed these ethical requirements using test-score semantics and fuzzy logic. The use of fuzzy logic is motivated by the view of ethics as a domain of possibilities, which allows the resolution of ethical dilemmas that AI systems may encounter. The proposed approach is illustrated through a case study.

Related papers

• Mirror: A Multi-Agent System for AI-Assisted Ethics Review [104.3684024153469]
  Mirror is an agentic framework for AI-assisted ethical review.<n>It integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture.
  arXiv  Detail & Related papers  (2026-02-09T03:38:55Z)
• Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems [4.854297874710511]
  A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments.<n>Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts.<n>We outline the necessary ingredients for building a holistic, meta-level framework that combines intermediate representations, probabilistic reasoning, and knowledge representation.
  arXiv  Detail & Related papers  (2025-02-28T17:25:11Z)
• Deontic Temporal Logic for Formal Verification of AI Ethics [4.028503203417233]
  This paper proposes a formalization based on deontic logic to define and evaluate the ethical behavior of AI systems.<n>It introduces axioms and theorems to capture ethical requirements related to fairness and explainability.<n>The authors evaluate the effectiveness of this formalization by assessing the ethics of the real-world COMPAS and loan prediction AI systems.
  arXiv  Detail & Related papers  (2025-01-10T07:48:40Z)
• 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)
• Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications [0.0]
  This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable.<n>Different case studies validate this framework by integrating AI in both academic and practical environments.
  arXiv  Detail & Related papers  (2024-09-25T12:39:28Z)
• 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)
• Unpacking the Ethical Value Alignment in Big Models [46.560886177083084]
  This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
  arXiv  Detail & Related papers  (2023-10-26T16:45:40Z)
• Ethics in conversation: Building an ethics assurance case for autonomous AI-enabled voice agents in healthcare [1.8964739087256175]
  The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape. This paper presents the interim findings of a case study applying this ethics assurance framework to the use of Dora, an AI-based telemedicine system.
  arXiv  Detail & Related papers  (2023-05-23T16:04:59Z)
• Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions [1.864621482724548]
  We develop a taxonomy of 21 normative ethical principles which can be operationalised in AI. We envision this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in reasoning capacities of responsible AI systems.
  arXiv  Detail & Related papers  (2022-08-12T08:48:16Z)
• Ethics of AI: A Systematic Literature Review of Principles and Challenges [3.7129018407842445]
  Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI.
  arXiv  Detail & Related papers  (2021-09-12T15:33:43Z)
• Case Study: Deontological Ethics in NLP [119.53038547411062]
  We study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems.
  arXiv  Detail & Related papers  (2020-10-09T16:04:51Z)
• On the Morality of Artificial Intelligence [154.69452301122175]
  We propose conceptual and practical principles and guidelines for Machine Learning research and deployment. We insist on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.
  arXiv  Detail & Related papers  (2019-12-26T23:06:54Z)

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.