No abstract

Artificial Intelligence (AI) systems are increasing in significance within software services. Unfortunately, these systems are not flawless. Their faults, failures and other systemic issues have emphasized the urgency for consideration of ethical standards and practices in AI engineering. Despite the growing number of studies in AI ethics, comparatively little attention has been placed on how ethical issues can be mitigated in software engineering (SE) practice. Currently understanding is lacking regarding the provision of useful tools that can help companies transform high-level ethical guidelines for AI ethics into the actual workflow of developers. In this paper, we explore the idea of using user stories to transform abstract ethical requirements into tangible outcomes in Agile software development. We tested this idea by studying master’s level student projects (15 teams) developing web applications for a real industrial client over the course of five iterations. These projects resulted in 250+ user stories that were analyzed for the purposes of this paper. The teams were divided into two groups: half of the teams worked using the ECCOLA method for AI ethics in SE, while the other half, a control group, was used to compare the effectiveness of ECCOLA. Both teams were tasked with writing user stories to formulate customer needs into system requirements. Based on the data, we discuss the effectiveness of ECCOLA, and Primary Empirical Contributions (PECs) from formulating ethical user stories in Agile development.

The public and academic discussion on Artificial Intelligence (AI) ethics is accelerating and the general public is becoming more aware AI ethics issues such as data privacy in these systems. To guide ethical development of AI systems, governmental and institutional actors, as well as companies, have drafted various guidelines for ethical AI. Though these guidelines are becoming increasingly common, they have been criticized for a lack of impact on industrial practice. There seems to be a gap between research and practice in the area, though its exact nature remains unknown. In this paper, we present a gap analysis of the current state of the art by comparing practices of 39 companies that work with AI systems to the seven key requirements for trustworthy AI presented in the "The Ethics Guidelines for Trustworthy Artificial Intelligence". The key finding of this paper is that there is indeed notable gap between AI ethics guidelines and practice. Especially practices considering the novel requirements for software development, requirements of societal and environmental well-being and diversity, nondiscrimination and fairness were not tackled by companies. CCS CONCEPTS• Software and its engineering → Software development process management; • Computing methodologies → Artificial intelligence; • Social and professional topics → Codes of ethics.

Advances in machine learning (ML) technologies have greatly improved Artificial Intelligence (AI) systems. As a result, AI systems have become ubiquitous, with their application prevalent in virtually all sectors. However, AI systems have prompted ethical concerns, especially as their usage crosses boundaries in sensitive areas such as healthcare, transportation, and security. As a result, users are calling for better AI governance practices in ethical AI systems. Therefore, AI development methods are encouraged to foster these practices. This research analyzes the ECCOLA method for developing ethical and trustworthy AI systems to determine if it enables AI governance in development processes through ethical practices. The results demonstrate that while ECCOLA fully facilitates AI governance in corporate governance practices in all its processes, some of its practices do not fully foster data governance and information governance practices. This indicates that the method can be further improved.

AI ethics is a research area characterized by a prominent gap between research and practice. With most studies in the area being conceptual in nature or focused on technical ML (Machine Learning) solutions, the link between AI (Artificial Intelligence) ethics and SE (Software Engineering) practice remains thin. Establishing this link, we argue, is vital going forward. While conceptual discussion is required to define AI ethics, much progress has already been made in this regard. Similarly, though technical ML solutions are also required for practical implementation, ML systems are ultimately still software, and thus SE cannot be forgotten. In this paper, we propose one way of bringing AI ethics closer to conventional SE practice: utilizing user stories to implement AI ethics by means of Ethical User Stories (EUS). EUS can be used to formulate both functional and non-functional requirements, although an ethical framework is required produce them. By treating AI ethics as a part of the development process in this fashion, as opposed to a separate task, it can ideally become a part of SE for ML systems.