Bridging the AI Trustworthiness Gap between Functions and Norms

TL;DR

This paper discusses the gap between functional and normative trustworthiness in AI, proposing a semantic language to bridge the divide and improve assessment and implementation of trustworthy AI systems.

Contribution

It introduces the idea of a semantic language to align functional and normative AI trustworthiness, aiding developers and stakeholders in assessment and compliance.

Findings

Identifies the gap between FTAI and NTAI.

Proposes a semantic language framework.

Discusses future steps for TAI assessment.

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.