The Role of Explainable AI in the Research Field of AI Ethics-Reference-Cited by-同舟云学术

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

Published:2023-12-08 Issue:4 Volume:13 Page:1-39
ISSN:2160-6455
Container-title:ACM Transactions on Interactive Intelligent Systems
language:en
Short-container-title:ACM Trans. Interact. Intell. Syst.

Author:

Vainio-Pekka Heidi¹^ORCID,Agbese Mamia Ori-Otse¹^ORCID,Jantunen Marianna¹^ORCID,Vakkuri Ville²^ORCID,Mikkonen Tommi¹^ORCID,Rousi Rebekah²^ORCID,Abrahamsson Pekka³^ORCID

Affiliation:

1. University of Jyväskylä, Finland

2. University of Vaasa, Finland

3. Tampere University, Finland

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 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.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Link

https://dl.acm.org/doi/pdf/10.1145/3599974

Reference200 articles.

1. Dhaminda B. Abeywickrama, Corina Cirstea, and Sarvapali D. Ramchurn. 2019. Model checking human-agent collectives for responsible AI. In 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN’19). IEEE, 1–8. DOI:10.1109/RO-MAN46459.2019.8956429

2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

3. Janet Adams and Hani Hagras. 2020. A type-2 fuzzy logic approach to explainable AI for regulatory compliance, fair customer outcomes and market stability in the global financial sector. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’20). IEEE, 1–8. DOI:10.1109/FUZZ48607.2020.9177542

4. AI Management An Exploratory Survey of the Influence of GDPR and FAT Principles

5. HLEG AI. 2019. High-level expert group on artificial intelligence. European Commission. Available at https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai.