The Role of Explainable AI in the Research Field of AI Ethics

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.

SLM-AIE: A Systematic Literature Map of Artificial Intelligence Ethics

“…2 shows that approximately 39% are Opinion research articles [37,38,39,40,41], as well as 31% are Philosophical Research articles as in [42,43,44] which, with regard to the topic of this study, predominates among the research types that carried out. Moreover, 16% of the obtained investigations are validation studies, such as [27,45,46,47,25,24,48]. Additionally, there is a lack of Experience research (6%) such as [49,50,51,52] Solution Proposals (5%) such as [53,54,55] and Evaluation research publications (3%) such as [56,57,58,45].…”

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

“…Vainio-Pekka et al (2023) [42] emphasized the critical role of Explainable AI in detecting flaws and biases within systems, thereby ensuring the transparency of these systems. XAI techniques not only aid in reducing biases in data but also facilitate a deeper understanding of the problem at hand, promoting ethical AI practices that are understandable and interpretable by humans.…”