TY - GEN
T1 - An Explainable AI Solution
T2 - International Conference on Cybersecurity, Situational Awareness, and Social Media, Cyber Science 2022
AU - Wheeler, Richard
AU - Carroll, Fiona
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023/3/8
Y1 - 2023/3/8
N2 - Machine Learning (ML) and Artificial Intelligence (AI) have not only transformed the way we work (i.e. how we integrate information, analyse data, and how we make decisions) but also how organisations operate (i.e. adding new business processes and services etc.). In fact, many private, public and even third sector organisations are now capitalising on the true value of having systems that can learn on their own without any human intervention. However, with these benefits also come challenges regarding project productivity and collaboration. In detail, the need to explain how these systems work and how organisations interpret their output to achieve transparency and trust. This paper details the potential of using Extended reality (XR) as a way for enabling Explainable AI (XAI) focusing on the design and development of a novel XAI XR solution. The paper also highlights the ‘positive’ responses from an initial solution evaluation study noting participant’s impressions of the solution. It then makes recommendations for further research and development into the effectiveness of XR for explainable AI.
AB - Machine Learning (ML) and Artificial Intelligence (AI) have not only transformed the way we work (i.e. how we integrate information, analyse data, and how we make decisions) but also how organisations operate (i.e. adding new business processes and services etc.). In fact, many private, public and even third sector organisations are now capitalising on the true value of having systems that can learn on their own without any human intervention. However, with these benefits also come challenges regarding project productivity and collaboration. In detail, the need to explain how these systems work and how organisations interpret their output to achieve transparency and trust. This paper details the potential of using Extended reality (XR) as a way for enabling Explainable AI (XAI) focusing on the design and development of a novel XAI XR solution. The paper also highlights the ‘positive’ responses from an initial solution evaluation study noting participant’s impressions of the solution. It then makes recommendations for further research and development into the effectiveness of XR for explainable AI.
UR - http://www.scopus.com/inward/record.url?scp=85151075707&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-6414-5_15
DO - 10.1007/978-981-19-6414-5_15
M3 - Conference contribution
AN - SCOPUS:85151075707
SN - 9789811964138
T3 - Springer Proceedings in Complexity
SP - 255
EP - 276
BT - Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media - Cyber Science 2022
A2 - Onwubiko, Cyril
A2 - Rosati, Pierangelo
A2 - Rege, Aunshul
A2 - Erola, Arnau
A2 - Bellekens, Xavier
A2 - Hindy, Hanan
A2 - Jaatun, Martin Gilje
PB - Springer Science and Business Media B.V.
Y2 - 20 June 2022 through 21 June 2022
ER -