Towards Audit Requirements for AI-based Systems in Mobility Applications
This work tackles the problem of regulatory gaps for AI-based systems in mobility, which is crucial for safety and trust, but it is incremental as it builds on existing frameworks.
The paper addresses the lack of AI-specific regulations in mobility applications by proposing 50 technical requirements to extend existing standards, demonstrating their applicability through an audit of a traffic sign recognition system.
Various mobility applications like advanced driver assistance systems increasingly utilize artificial intelligence (AI) based functionalities. Typically, deep neural networks (DNNs) are used as these provide the best performance on the challenging perception, prediction or planning tasks that occur in real driving environments. However, current regulations like UNECE R 155 or ISO 26262 do not consider AI-related aspects and are only applied to traditional algorithm-based systems. The non-existence of AI-specific standards or norms prevents the practical application and can harm the trust level of users. Hence, it is important to extend existing standardization for security and safety to consider AI-specific challenges and requirements. To take a step towards a suitable regulation we propose 50 technical requirements or best practices that extend existing regulations and address the concrete needs for DNN-based systems. We show the applicability, usefulness and meaningfulness of the proposed requirements by performing an exemplary audit of a DNN-based traffic sign recognition system using three of the proposed requirements.