On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services

TL;DR

This paper discusses the practical challenges in developing fair and ethical AI solutions in financial services, highlighting gaps between principles and real-world applications to foster industry-wide solutions.

Contribution

It provides a comprehensive survey of the current obstacles in implementing ethical AI in finance, emphasizing design complexities, tool shortages, and organizational issues.

Findings

Identifies key practical challenges in ethical AI deployment.

Highlights gaps between principles and actual AI applications.

Calls for industry-wide discussions on solutions.

Abstract

Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wide objective. While many ethical principles and guidelines have been published in recent years, they fall short of addressing the serious challenges that model developers face when building ethical AI solutions. We survey the practical and overarching issues surrounding model development, from design and implementation complexities, to the shortage of tools, and the lack of organizational constructs. We show how practical considerations reveal the gaps between high-level principles and concrete, deployed AI applications, with the aim of starting industry-wide conversations toward solution approaches.