An Explainable AI Solution: Exploring Extended Reality as a Way to Make Artificial Intelligence More Transparent and Trustworthy

被引:0
|
作者
Wheeler, Richard [1 ]
Carroll, Fiona [1 ]
机构
[1] Cardiff Metropolitan Univ, Sch Technol, Cardiff, Wales
关键词
D O I
10.1007/978-981-19-6414-5_15
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:255 / 276
页数:22
相关论文
共 18 条
  • [1] Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses
    Qayyum, Adnan
    Butt, Muhammad Atif
    Ali, Hassan
    Usman, Muhammad
    Halabi, Osama
    Al-Fuqaha, Ala
    Abbasi, Qammer H.
    Imran, Muhammad Ali
    Qadir, Junaid
    ACM COMPUTING SURVEYS, 2024, 56 (07) : 1 - 38
  • [2] A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI
    Teng, Zixuan
    Li, Lan
    Xin, Ziqing
    Xiang, Dehui
    Huang, Jiang
    Zhou, Hailing
    Shi, Fei
    Zhu, Weifang
    Cai, Jing
    Peng, Tao
    Chen, Xinjian
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2024, 14 (12) : 9620 - 9652
  • [3] Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey
    Maathuis, Clara
    Cidota, Marina Anca
    Datcu, Dragos
    Marin, Letitia
    MATHEMATICS, 2025, 13 (02)
  • [4] A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare
    Fehr, Jana
    Citro, Brian
    Malpani, Rohit
    Lippert, Christoph
    Madai, Vince I.
    FRONTIERS IN DIGITAL HEALTH, 2024, 6
  • [5] Explainable artificial intelligence (XAI): How to make image analysis deep learning models transparent
    Song, Haekang
    Kim, Sungho
    2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022), 2022, : 1595 - 1598
  • [6] When XR and AI Meet - A Scoping Review on Extended Reality and Artificial Intelligence
    Hirzle, Teresa
    Mueller, Florian
    Draxler, Fiona
    Schmitz, Martin
    Knierim, Pascal
    Hornbaek, Kasper
    PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, 2023,
  • [7] Exploring the integration of artificial intelligence (AI) and augmented reality (AR) in maritime medicine
    Battineni, Gopi
    Chintalapudi, Nalini
    Ricci, Giovanna
    Ruocco, Ciro
    Amenta, Francesco
    ARTIFICIAL INTELLIGENCE REVIEW, 2024, 57 (04)
  • [8] Exploring the integration of artificial intelligence (AI) and augmented reality (AR) in maritime medicine
    Gopi Battineni
    Nalini Chintalapudi
    Giovanna Ricci
    Ciro Ruocco
    Francesco Amenta
    Artificial Intelligence Review, 57
  • [9] Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems
    Dong, Jiqian
    Chen, Sikai
    Miralinaghi, Mohammad
    Chen, Tiantian
    Li, Pei
    Labi, Samuel
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 156
  • [10] A Solution to the Hyper Complex, Cross Domain Reality of Artificial Intelligence: The Hierarchy of AI
    Kear, Andrew
    Folkes, Sasha L.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (03) : 49 - 59