An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI

TL;DR

This study examines the ethical decision-making challenges in responsible AI software engineering, highlighting gaps between guidelines and practice, and emphasizing the need for operational frameworks, interdisciplinary skills, and organizational culture to improve responsible AI development.

Contribution

It provides empirical insights into the practical challenges and gaps in implementing responsible AI practices within software engineering, emphasizing the need for operational frameworks and interdisciplinary skills.

Findings

Current ethical guidelines are insufficiently operationalized in AI RSE.

Practitioners face challenges in embedding ethics throughout the AI development lifecycle.

Interdisciplinary collaboration and organizational ethics culture are crucial for responsible AI.

Abstract

Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI, underscoring the need for practical,scenario-driven operational guidelines. Given the complexity of AI and the relative inexperience of professionals in this rapidly evolving field, continuous learning and market adaptation are crucial. Through qualitative interviews with seven practitioners(conducted until saturation), quantitative surveys of 51 practitioners, and static validation of results with four industry experts in AI, this study explores how personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI. A key finding is the gap between the current state of the art and actual practice in RSE for AI, particularly in the failure to operationalize ethical and responsible decision-making within the software engineering life cycle for AI. While ethical issues in RSE for AI largely mirror those found in broader SE process, the study highlights a distinct lack of operational frameworks and resources to guide RSE practices for AI effectively. The results reveal that current ethical guidelines are insufficiently implemented at the operational level, reinforcing the complexity of embedding ethics throughout the software engineering life cycle. The study concludes that interdisciplinary collaboration, H-shaped competencies(Ethical-Technical dual competence), and a strong organizational culture of ethics are critical for fostering RSE practices for AI, with a particular focus on transparency and accountability.