The Quest for Interpretable and Responsible Artificial Intelligence

TL;DR

This paper surveys the motivations and trends in developing interpretable and responsible AI, emphasizing the importance of understanding, trust, and accountability in AI systems across various applications.

Contribution

It provides an overview of current approaches and challenges in making AI systems interpretable and responsible, highlighting key trends and motivations.

Findings

Growing emphasis on interpretability for trust and accountability

Emerging methods to explain AI decisions

Recognition of ethical and societal implications

Abstract

Artificial Intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in computational biology, finance, law and robotics. However, such a highly positive impact is coupled with significant challenges: How do we understand the decisions suggested by these systems in order that we can trust them? How can they be held accountable for those decisions? In this short survey, we cover some of the motivations and trends in the area that attempt to address such questions.