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 article 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 article 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.

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.

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.

Artificial Intelligence (AI) systems are becoming increasingly widespread and exert a growing influence on society at large. The growing impact of these systems has also highlighted potential issues that may arise from their utilization, such as data privacy issues, resulting in calls for ethical AI systems. Yet, how to develop ethical AI systems remains an important question in the area. How should the principles and values be converted into requirements for these systems, and what should developers and the organizations developing these systems do? To further bridge this gap in the area, in this paper, we present a method for implementing AI ethics: ECCOLA. Following a cyclical action research approach, ECCOLA has been iteratively developed over the course of multiple years, in collaboration with both researchers and practitioners.

Artificial Intelligence (AI) solutions are becoming increasingly common in software development endeavors, and consequently exert a growing societal influence as well. Due to their unique nature, AI based systems influence a wide range of stakeholders with or without their consent, and thus the development of these systems necessitates a higher degree of ethical consideration than is currently carried out in most cases. Various practical examples of AI failures have also highlighted this need. However, there is only limited research on methods and tools for implementing AI ethics in software development, and we currently have little knowledge of the state of practice. In this study, we explore the state of the art in startup-like environments where majority of the AI software today gets developed. Based on a multiple case study, we discuss the current state of practice and highlight issues. The cases underline the complete ignorance of ethical consideration in AI endeavors. We also outline existing good practices that can already support the implementation of AI ethics, such as documentation and error handling.