Background: The continuous development of artificial intelligence (AI) and increasing rate of adoption by software startups calls for governance measures to be implemented at the design and development stages to help mitigate AI governance concerns. Most AI ethical design and development tools mainly rely on AI ethics principles as the primary governance and regulatory instrument for developing ethical AI that inform AI governance. However, AI ethics principles have been identified as insufficient for AI governance due to lack of information robustness, requiring the need for additional governance measures. Adaptive governance has been proposed to combine established governance practices with AI ethics principles for improved information and subsequent AI governance. Our study explores adaptive governance as a means to improve information robustness of AI ethical design and development tools. We combine information governance practices with AI ethics principles using ECCOLA, a tool for ethical AI software development at the early developmental stages. Aim: How can ECCOLA improve its robustness by adapting it with GARP® IG practices? Methods: We use ECCOLA as a case study and critically analyze its AI ethics principles with information governance practices of the Generally Accepted Recordkeeping principles (GARP®). Results: We found that ECCOLA’s robustness can be improved by adapting it with Information governance practices of retention and disposal. Conclusions: We propose an extension of ECCOLA by a new governance theme and card, # 21.

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.

No abstract

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research fields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review offers an opportunity to detect research gaps and focus points. This paper presents the results of a systematic mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This paper contributes to the research field with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the field of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are reflected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics the collected dataset opens possibilities to continue the mapping process in other directions.

Increasing ethical concerns necessitate AI ethics forms part of practical software engineering (SE) foundational educational learning. Using an ethnographic approach and focus group discussions in a SE project-based learning environment, WIMMA lab, we gain insight into how AI ethics can be implemented to enable students to acquire these necessary skills. We propose a framework as an outcome to aid the implementation of AI ethics skills within SE project-based learning environments.