Solutions in artificial intelligence (AI) are becoming increasingly widespread in system development endeavors. As the AI systems affect various stakeholders due to their unique nature, the growing influence of these systems calls for eth-ical considerations. Academic discussion and practical examples of autonomous system failures have highlighted the need for implementing ethics in software development. However, research on methods and tools for implementing ethics into AI system design and development in practice is still lacking. This paper be-gins to address this focal problem by providing a baseline for ethics in AI based software development. This is achieved by reporting results from an industrial multiple case study on AI systems development in the health care sector. In the context of this study, ethics were perceived as interplay of transparency, re-sponsibility and accountability, upon which research model is outlined. Through these cases, we explore the current state of practice out on the field in the ab-sence of formal methods and tools for ethically aligned design. Based on our data, we discuss the current state of practice and outline existing good practic-es, as well as suggest future research directions in the area.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· arXiv.org· 2 citations
Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.
Anh Nguyen-Duc, P. Abrahamsson· ESEC/SIGSOFT FSE· 9 citations
There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.
Ville Vakkuri, Marianna Jantunen, Erika Halme et al.· SafeAI@AAAI· 17 citations· ⚡1
Affects--emotions and moods--have an impact on cognitive processing activities and the working performance of individuals. It has been established that software development tasks are undertaken through cognitive processing activities. Therefore, we have proposed to employ psychology theory and measurements in software engineering (SE) research. We have called it "psychoempirical software engineering". However, we found out that existing SE research has often fallen into misconceptions about the affect of developers, lacking in background theory and how to successfully employ psychological measurements in studies. The contribution of this paper is threefold. (1) It highlights the challenges to conduct proper affect-related studies with psychology; (2) it provides a comprehensive literature review in affect theory; and (3) it proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
The study of affects (i.e., emotions, moods) in the workplace has received a lot of attention in the last 15 years. Despite the fact that software development has been shown to be intellectual, creative, and driven by cognitive activities, and that affects have a deep influence on cognitive activities, software engineering research lacks an understanding of the affects of software developers. This note provides (1) common misconceptions of affects when dealing with job satisfaction, motivation, commitment, well-being, and happiness; (2) validated measurement instruments for affect measurement; and (3) our recommendations when measuring the affects of software developers.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· 2015 IEEE/ACM 8th Internatio...· 29 citations· ⚡3
Affects---emotions and moods---have an impact on cognitive activities and the working performance of individuals. Development tasks are undertaken through cognitive processes, yet software engineering research lacks theory on affects and their impact on software development activities. In this paper, we report on an interpretive study aimed at broadening our understanding of the psychology of programming in terms of the experience of affects while programming, and the impact of affects on programming performance. We conducted a qualitative interpretive study based on: face-to-face open-ended interviews, in-field observations, and e-mail exchanges. This enabled us to construct a novel explanatory theory of the impact of affects on development performance. The theory is explicated using an established taxonomy framework. The proposed theory builds upon the concepts of events, affects, attractors, focus, goals, and performance. Theoretical and practical implications are given.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· PeerJ Computer Science· 63 citations· ⚡5
Software startups are challenging endeavours, with various road blocks on their path to success. The current understanding of the challenges that software startups may encounter is very limited. In this paper, we use the research framework of learning and product development stages to analyse the key challenges that software startups have to deal with at different life cycle stages, from problem definition to solution validation and from concept to mature product. Based on an analysis of the empirical data collected by a large survey of 4100 startups, we find out that what perceived as biggest challenges by software startups do vary across different life cycle stages. Building product is the biggest obstacle for software startups, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature. Business related challenges such as customer acquisition and scaling are more noticeable at the later stages. Our study raises the awareness of these challenges and suggests to tackle right challenges at the right time.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
