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Bridging the AI Trustworthiness Gap between Functions and Norms

Daan Di Scala, Sophie Lathouwers, Michael van Bekkum

TL;DR

This paper addresses the gap between functional and normative trustworthiness in AI, arguing for a bridging semantic language that maps AI system functions to regulatory norms. It outlines an initial framework that combines a standardized function taxonomy, system-level descriptions, and expressive trust relationships, anchored to existing normative sources like the AI Act, ALTAI, OECD, NIST, and AIRO, as well as functional modelling approaches. The authors present a concrete design direction and a roadmap for standardisation and tooling, aiming to help developers translate norms into implementable requirements and enable coherent lifecycle assessment of TAI. The work highlights the need for practical, evolvable tools rather than definitive judgments, with phased scope and iterative validation as central elements for real-world impact.

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.

Bridging the AI Trustworthiness Gap between Functions and Norms

TL;DR

This paper addresses the gap between functional and normative trustworthiness in AI, arguing for a bridging semantic language that maps AI system functions to regulatory norms. It outlines an initial framework that combines a standardized function taxonomy, system-level descriptions, and expressive trust relationships, anchored to existing normative sources like the AI Act, ALTAI, OECD, NIST, and AIRO, as well as functional modelling approaches. The authors present a concrete design direction and a roadmap for standardisation and tooling, aiming to help developers translate norms into implementable requirements and enable coherent lifecycle assessment of TAI. The work highlights the need for practical, evolvable tools rather than definitive judgments, with phased scope and iterative validation as central elements for real-world impact.

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.
Paper Structure (3 sections, 2 figures)

This paper contains 3 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Towards a Bridging Semantic Framework
  3. Discussion

Figures (2)

  • Figure 1: Seven normative key principles (pillars) of TAI as defined in hlegaiactsection3.
  • Figure 2: Conceptual language bridging functional aspects of AI components to normative (AI Act's) trustworthiness principles. Yellow blocks denote classes and blue labelled arrows denote relations.