Bridging the AI Trustworthiness Gap between Functions and Norms

• URL: http://arxiv.org/abs/2512.20671v1
• Date: Fri, 19 Dec 2025 14:06:57 GMT
• Title: Bridging the AI Trustworthiness Gap between Functions and Norms
• Authors: Daan Di Scala, Sophie Lathouwers, Michael van Bekkum,
• Abstract summary: Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits.<n>We argue that a bridge is needed, specifically by introducing a conceptual language which can match FTAI and NTAI.<n>Such a semantic language can assist developers as a framework to assess AI systems in terms of trustworthiness.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits. While Functional TAI (FTAI) focuses on how to implement trustworthy systems, Normative TAI (NTAI) focuses on regulations that need to be enforced. However, gaps between FTAI and NTAI remain, making it difficult to assess trustworthiness of AI systems. We argue that a bridge is needed, specifically by introducing a conceptual language which can match FTAI and NTAI. Such a semantic language can assist developers as a framework to assess AI systems in terms of trustworthiness. It can also help stakeholders translate norms and regulations into concrete implementation steps for their systems. In this position paper, we describe the current state-of-the-art and identify the gap between FTAI and NTAI. We will discuss starting points for developing a semantic language and the envisioned effects of it. Finally, we provide key considerations and discuss future actions towards assessment of TAI.

Related papers

• Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
  We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
  arXiv  Detail & Related papers  (2026-01-16T18:44:09Z)
• Is Trust Correlated With Explainability in AI? A Meta-Analysis [0.0]
  We conduct a comprehensive examination of the existing literature to explore the relationship between AI explainability and trust.<n>Our analysis, incorporating data from 90 studies, reveals a statistically significant but moderate positive correlation between the explainability of AI systems and the trust they engender.<n>This research highlights its broader socio-technical implications, particularly in promoting accountability and fostering user trust in critical domains such as healthcare and justice.
  arXiv  Detail & Related papers  (2025-04-16T23:30:55Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• The Journey to Trustworthy AI: Pursuit of Pragmatic Frameworks [0.0]
  This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions.<n>We argue against using terms such as Responsible or Ethical AI as substitutes for TAI.<n>Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability.
  arXiv  Detail & Related papers  (2024-03-19T08:27:04Z)
• RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems [4.268504966623082]
  Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them.
  arXiv  Detail & Related papers  (2023-05-29T11:57:07Z)
• The Role of Large Language Models in the Recognition of Territorial Sovereignty: An Analysis of the Construction of Legitimacy [67.44950222243865]
  We argue that technology tools like Google Maps and Large Language Models (LLM) are often perceived as impartial and objective. We highlight the case of three controversial territories: Crimea, West Bank and Transnitria, by comparing the responses of ChatGPT against Wikipedia information and United Nations resolutions.
  arXiv  Detail & Related papers  (2023-03-17T08:46:49Z)
• Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards [0.0]
  Legal standards facilitate robust communication of inherently vague and underspecified goals. Our research is an initial step toward a framework for evaluating AI understanding of legal standards more broadly.
  arXiv  Detail & Related papers  (2023-01-24T16:03:20Z)
• 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)
• Designing for Responsible Trust in AI Systems: A Communication Perspective [56.80107647520364]
  We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH. We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. We propose a checklist of requirements to help technology creators identify appropriate cues to use.
  arXiv  Detail & Related papers  (2022-04-29T00:14:33Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Multisource AI Scorecard Table for System Evaluation [3.74397577716445]
  The paper describes a Multisource AI Scorecard Table (MAST) that provides the developer and user of an artificial intelligence (AI)/machine learning (ML) system with a standard checklist. The paper explores how the analytic tradecraft standards outlined in Intelligence Community Directive (ICD) 203 can provide a framework for assessing the performance of an AI system.
  arXiv  Detail & Related papers  (2021-02-08T03:37:40Z)

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.