Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems
This work addresses the need for trustworthy AI in critical applications like ADAS, but it is incremental as it evaluates an existing framework in a specific domain without introducing new methods.
The paper applied the Assessment List for Trustworthy AI (ALTAI) to an Advanced Driver-Assistance System (ADAS) development project, finding it largely applicable but identifying specific parts like human agency and transparency as less relevant, and highlighting broader societal and environmental impacts as beyond a single supplier's scope.
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.