Bridging the AI Trustworthiness Gap between Functions and Norms
This addresses the problem of assessing AI trustworthiness for developers and regulators, but it is incremental as it builds on existing TAI concepts without introducing new methods or data.
The paper tackles the gap between Functional Trustworthy AI (FTAI) and Normative Trustworthy AI (NTAI) by proposing a conceptual semantic language to bridge them, aiming to assist developers in assessing AI trustworthiness and stakeholders in translating regulations into implementation steps.
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.