Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI

TL;DR

This paper advocates applying established software engineering and testing practices to evaluate the trustworthiness of AI systems, leveraging existing standards and procedures from software development.

Contribution

It bridges the gap between software engineering assessment methods and AI trustworthiness criteria, proposing a structured approach for evaluation.

Findings

Connects AI trustworthiness requirements with software testing practices

Raises questions for integrating software assessment tools into AI evaluation

Highlights the potential for software engineering to improve AI trustworthiness assessment

Abstract

Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission's AI high-level expert group and established procedures from software engineering and raise questions for future work.