Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems

TL;DR

This paper applies the AI Trustworthiness Assessment List (ALTAI) to an Advanced Driver-Assistance System, demonstrating its applicability and proposing improvements for future iterations in the context of safety-critical AI applications.

Contribution

It provides an empirical case study of applying ALTAI to ADAS development and offers specific recommendations for enhancing the assessment tool.

Findings

ALTAI is largely applicable to ADAS development

Certain ALTAI aspects like human agency and transparency can be deprioritized for ADAS

Recommendations include life-cycle variants, domain-specific adaptations, and redundancy removal

Abstract

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI-HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domain-specific adaptations, and removed redundancy.