What is it about?
Software profoundly influences how we live and work, often reflecting human behavior and societal values. Consequently, software-intensive systems and software engineering technologies must be developed and deployed responsibly to avoid harming users, society, or the environment. This raises questions about what constitutes the responsible development and use of technology, including which human values and which social and environmental norms are embedded in software engineering practices and products. With the increased incorporation of AI in software-intensive systems, software engineers are confronted with even more ethical and moral choices to incorporate into these processes and products. To address these responsibility issues, the workshop hosted a half-day event featuring themed presentation tracks, interactive discussions, and a stimulating concluding panel. The workshop comprised research papers (visionary, exploratory, empirical, and solution-oriented) and industrial papers (lessons learned and challenges) published in the ACM workshop proceedings.
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Why is it important?
This paper is important because it calls for a fundamental shift in how we understand and practice responsibility in software engineering: from a reactive, backward-looking approach that assigns blame after failures occur to a proactive model of “active responsibility” that anticipates potential harms and deliberately designs for societal benefit from the outset. As software increasingly shapes safety, rights, public trust, and everyday life, technical quality alone is no longer sufficient. The paper highlights transparency, traceability, and explainability as essential principles for developing trustworthy software-intensive systems, from autonomous vehicles and pervasive computing to AI systems that may reproduce societal biases. The paper connects these principles to concrete engineering practices that can be integrated into software lifecycles and DevOps workflows. Drawing on emerging empirical evidence - including research on the environmental costs of LLM-assisted coding, online safety, and the rapid obsolescence of AI fairness tools - it shows that responsible software engineering is inseparable from sustainability, equity, and developer well-being. Its central message is that responsibility cannot be added as an afterthought or addressed by software engineers alone. It must become a continuous, collective, and transdisciplinary practice involving engineers, social scientists, legal experts, policymakers, and affected communities. In this way, the paper provides an important foundation for developing safer, more trustworthy, and socially beneficial socio-technical systems.
Perspectives
The emergence of generative and agentic AI represents a fundamental structural break in software engineering. As the contributions to the Workshop on Responsible Software Engineering demonstrate, we are rapidly moving beyond traditional programming paradigms toward autonomous, LLM-driven, and highly dynamic systems. This technological shift fundamentally changes how software is designed, deployed, and maintained - and brings unprecedented ethical, ecological, and socio-technical challenges to the forefront. As these systems increasingly shape human behaviour and embed societal norms and values, the negotiation of expectations, boundaries, and responsibilities for and with society must begin now. Software quality can no longer be understood as a purely technical matter. Public trust increasingly depends not only on technical quality, but also on transparency, public acceptance, and societal alignment. Determining how responsibility is shared, mitigated, and translated into engineering practice must therefore be a collective, transdisciplinary, and participatory endeavour involving social scientists, legal experts, policymakers, engineers, and affected communities. In this urgent societal process, the scientific community has an indispensable role to play. Academia must help shape and inform this development through empirical research, conceptual frameworks, and rigorous scientific evidence. The workshop contributions - from measuring the environmental footprint of LLM-assisted software engineering to developing evaluation approaches for agentic AI - demonstrate how scientific research can turn abstract ethical concerns into actionable, evidence-based insights. Only by combining societal dialogue with rigorous scientific inquiry can we build a trustworthy and responsible foundation for the future of software engineering.
Ina Schieferdecker
Technische Universitat Berlin
Read the Original
This page is a summary of: First Workshop on Responsible Software Engineering, ACM SIGSOFT Software Engineering Notes, August 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3820786.3820792.
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