# Towards Audit Requirements for AI-based Systems in Mobility Applications

## TL;DR

This paper proposes 50 technical requirements to extend existing safety standards for AI-based mobility systems, aiming to improve regulation, trust, and safety of DNN-based functionalities in real-world driving environments.

## Contribution

It introduces a comprehensive set of AI-specific requirements for mobility applications and demonstrates their practical application through an exemplary audit of a traffic sign recognition system.

## Key findings

- The proposed requirements are applicable and useful for auditing DNN-based systems.
- An exemplary audit validates the relevance of the proposed requirements.
- The work bridges the gap between current regulations and AI-specific safety needs.

## Abstract

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.