Related papers: Tailoring Requirements Engineering for Responsible…
Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to…
Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the…
Generative AI is reshaping software work, yet we lack clear guidance on where developers most need support and how to design it responsibly. We report a large-scale, mixed-methods study of N=860 developers examining where, why, and how they…
Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from…
Regulatory compliance is increasingly being addressed in the practice of requirements engineering as a main stream concern. This paper points out a gap in the theoretical foundations of regulatory compliance, and presents a theory that…
There is a growing demand for software engineering education (SEE) for professionals because of the increasing demand, active evolution of the technological landscape, and changes in the skills required by the practice. Integrating…
This paper explores Site Reliability Engineering (SRE), a modern approach to maintaining scalable and reliable software systems. It presents observations on how structured SRE processes improve operational efficiency, reduce system…
Context] Problems in Requirements Engineering (RE) can lead to serious consequences during the software development lifecycle. [Goal] The goal of this paper is to propose empirically-based guidelines that can be used by different types of…
Our research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in…
Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at…
Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable--benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active…
In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs)…
Legacy software systems typically include vital data for organizations that use them and should thus to be regularly maintained. Ideally, organizations should rely on Requirements Engineers to understand and manage changes of stakeholder…
There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the…
Machine Learning (ML)-enabled systems challenge traditional Requirements Engineering (RE) and agile management due to data dependence, experimentation, and uncertain model behavior. Existing RE and agile practices remain poorly integrated…
Requirements Engineering (RE) is one of the prime areas in software development. Since agile software development englobes several emerging techniques and advocates for continuous improvement, it urges the question of which agile RE…
Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be defined as the possibility to reproduce the same or a similar experiment or method, thereby…
Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems. It, however, fails to accommodate the possibility of holding AI responsible per…
Responsible prompt engineering has emerged as a critical framework for ensuring that generative artificial intelligence (AI) systems serve society's needs while minimizing potential harms. As generative AI applications become increasingly…
With the wide spread use of AI-driven systems in the edge (a.k.a edge intelligence systems), such as autonomous driving vehicles, wearable biotech devices, intelligent manufacturing, etc., such systems are becoming very critical for our…