Responsible AI in the Software Industry: A Practitioner-Centered Perspective

TL;DR

This paper investigates how software practitioners implement Responsible AI principles, revealing gaps in current practices and emphasizing the need for comprehensive frameworks to better operationalize ethical guidelines in AI development.

Contribution

It provides empirical insights into practitioners' practices and challenges in applying Responsible AI principles within the software industry.

Findings

Practitioners focus on fairness, inclusiveness, and reliability.

Transparency and accountability are less emphasized.

Current strategies have notable gaps in comprehensive Responsible AI implementation.

Abstract

Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we investigated their methods, concerns, and strategies for addressing Responsible AI in software development. Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices. This scenario highlights gaps in current strategies and the need for more comprehensive frameworks to fully operationalize Responsible AI principles in software engineering.