Trustworthy AI

TL;DR

This paper discusses the importance of Trustworthy AI by addressing key issues such as bias, explainability, robustness, privacy, decency, and attribution to enhance user trust and system reliability.

Contribution

It proposes a comprehensive tutorial on Trustworthy AI, covering six critical issues to improve transparency, fairness, security, and accountability in AI systems.

Findings

Identifies six key areas for Trustworthy AI improvement.

Highlights the importance of transparency and bias mitigation.

Emphasizes privacy and attribution in AI development.

Abstract

Modern AI systems are reaping the advantage of novel learning methods. With their increasing usage, we are realizing the limitations and shortfalls of these systems. Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, high opacity in terms of revealing the lineage of the system, how they were trained and tested, and under which parameters and conditions they can reliably guarantee a certain level of performance, are some of the most prominent limitations. Ensuring the privacy and security of the data, assigning appropriate credits to data sources, and delivering decent outputs are also required features of an AI system. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems, namely: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of adversarial attacks, (iv) improved privacy and security in model building, (v) being decent, and (vi) model attribution, including the right level of credit assignment to the data sources, model architectures, and transparency in lineage.