Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap

TL;DR

This paper explores the importance of accountability in AI systems, emphasizing robustness and reliability, reviewing current challenges, and proposing future research directions to develop trustworthy AI.

Contribution

It provides a preliminary analysis of accountability in AI, reviews existing literature, and outlines a roadmap for future research in trustworthy AI systems.

Findings

Highlights key challenges in AI robustness and reliability

Emphasizes the role of accountability in trustworthy AI

Proposes future research directions and identifies gaps

Abstract

This vision paper presents initial research on assessing the robustness and reliability of AI-enabled systems, and key factors in ensuring their safety and effectiveness in practical applications, including a focus on accountability. By exploring evolving definitions of these concepts and reviewing current literature, the study highlights major challenges and approaches in the field. A case study is used to illustrate real-world applications, emphasizing the need for innovative testing solutions. The incorporation of accountability is crucial for building trust and ensuring responsible AI development. The paper outlines potential future research directions and identifies existing gaps, positioning robustness, reliability, and accountability as vital areas for the development of trustworthy AI systems of the future.