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

2025-01-26

AI Summary

This study addresses the practical challenge of implementing ethical principles in AI software engineering, identifying a systemic absence of a “values–roles–impacts” triadic decision-making mechanism across the software development lifecycle. Through a mixed-methods approach—including semi-structured interviews with seven practitioners, a quantitative survey of 51 professionals, and static validation by four domain experts—we find that existing ethical guidelines fail operationally due to fragmented technical and ethical competencies, insufficient cross-disciplinary collaboration, and weak accountability structures. We propose two complementary solutions: an H-shaped competence model—integrating deep technical expertise with broad ethical literacy—and the cultivation of organizational ethical culture. Critically, we empirically derive the first actionable decision framework for Responsible Software Engineering (RSE) in AI contexts, bridging the theory-practice gap in AI ethics through both conceptual grounding and implementable pathways.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Ethical AI
• Societal Impact
• Responsible Design

Innovation

• Responsible Decision-Making
• Artificial Intelligence Software Engineering
• Ethical Guidelines Implementation