Explainable artificial intelligence and its practical applications

Research Article
Open access

Explainable artificial intelligence and its practical applications

Yijun Liu 1*
  • 1 Jiangxi's Agriculture University, Nanchang, China, 330000    
  • *corresponding author yijun-liu02@163.com
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

With the continuous development of the times, the artificial intelligence industry is also booming, and its presence in various fields has a huge role in promoting social progress and advancing industrial development. Research on it is also in full swing. People are eager to understand the cause-and-effect relationship between the actions performed or the strategies decided based on the black-box model, so that they can learn or judge from another perspective. Thus the Explainable AI is proposed, it is a new generation of AI that allows humans to understand the cause and give them a decision solution, so that every outcome has its own basis for decision. Although some considerable results have been achieved in the application of explainable AI, it is still at the beginning stage and there are still some challenges to be solved. From the transportation industry, which facilitates people's access to autonomous driving, to the medical industry, which saves people's lives, to the financial industry, which is a huge industry, and even in the education industry, which is accessible to all people, it has a presence. This paper talks about the current situation and problems of explainable AI through its application in various aspects. Explainable AI can serve not only developers but also users by satisfying their interest-related needs. The transparency of explainable AI is important when it is used in socially relevant applications, which is why we have conducted extensive research on explainable AI.

Keywords:

XAI's theory, XAI's applications, Explainable Artificial Intelligence, Explainability, Interpretability, Explanations.

Liu,Y. (2023). Explainable artificial intelligence and its practical applications. Applied and Computational Engineering,4,755-759.
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References

[1]. Dr G.R. Karpagam, Aditya Varma, Samrddhi M. Understanding, Visualizing and Explaining XAI Through Case Studies, 2022 8th International Conference on Advanced Computing and Communication Systems, 2022.

[2]. David Gunning, Mark Stefik, Jaesik Choi. XAI-Explainable artificial intelligence [J]. SCIENCE ROBOTICS, 18 Dec 2019, Vol 4, Issue 37. DOI: 10.1126/scirobotics.aay7120.

[3]. Nakul Tanwar, Yasha Hasija. Explainable AI; Are we there yet? 2022 IEEE Delhi Section Conference, 2022.

[4]. Alexander Binder, Michael Bockmayr, Miriam Hägele. Morphological and molecular breast cancer profiling through explainable machine learning [J]. Nature Machine Intelligence, 2021.

[5]. K. Muthamil Sudar; P. Nagaraj; S. Nithisaa; R. Aishwarya. Alzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI). 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 2022.

[6]. Jurgita Černevičienė, Audrius Kabašinskas. review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence [J]. Frontiers in artificial intelligence, 10 March 2022, doi: 10.3389/frai.2022.827584.

[7]. Wang P., Tian S-Y, Qiaoyu Sun. Explainable educational artificial intelligence research: system framework, application value and case study [J]. Journal of Distance Education, Vol.6, 2021.

[8]. Mastromattei M., Ranaldi L., Fallucchi F., Zanzotto F. M. Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled. Peer J Computer Science 8:e859, 2022. https://doi.org/10.7717/peerj-cs.859.

[9]. W. W. Guo, Q. Wang. Explainable Interaction in Human-Autonomous Vehicle Interaction [J]. Packaging Engineering, Vol. 18, 2020.

[10]. Wu D., Sun G.Y. Towards Explainable Interactive Artificial Intelligence: Motivations, Approaches, and Research Trends [J]. Journal of Wuhan University (Philosophy and Social Science Edition), 2021, (No. 5).

[11]. F. K. Došilović, M. Brčić and N. Hlupić, Explainable artificial intelligence: a survey, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 0210-0215. doi: 10.23919/MIPRO.2018.8400040.

Cite this article

Liu,Y. (2023). Explainable artificial intelligence and its practical applications. Applied and Computational Engineering,4,755-759.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Dr G.R. Karpagam, Aditya Varma, Samrddhi M. Understanding, Visualizing and Explaining XAI Through Case Studies, 2022 8th International Conference on Advanced Computing and Communication Systems, 2022.

[2]. David Gunning, Mark Stefik, Jaesik Choi. XAI-Explainable artificial intelligence [J]. SCIENCE ROBOTICS, 18 Dec 2019, Vol 4, Issue 37. DOI: 10.1126/scirobotics.aay7120.

[3]. Nakul Tanwar, Yasha Hasija. Explainable AI; Are we there yet? 2022 IEEE Delhi Section Conference, 2022.

[4]. Alexander Binder, Michael Bockmayr, Miriam Hägele. Morphological and molecular breast cancer profiling through explainable machine learning [J]. Nature Machine Intelligence, 2021.

[5]. K. Muthamil Sudar; P. Nagaraj; S. Nithisaa; R. Aishwarya. Alzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI). 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 2022.

[6]. Jurgita Černevičienė, Audrius Kabašinskas. review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence [J]. Frontiers in artificial intelligence, 10 March 2022, doi: 10.3389/frai.2022.827584.

[7]. Wang P., Tian S-Y, Qiaoyu Sun. Explainable educational artificial intelligence research: system framework, application value and case study [J]. Journal of Distance Education, Vol.6, 2021.

[8]. Mastromattei M., Ranaldi L., Fallucchi F., Zanzotto F. M. Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled. Peer J Computer Science 8:e859, 2022. https://doi.org/10.7717/peerj-cs.859.

[9]. W. W. Guo, Q. Wang. Explainable Interaction in Human-Autonomous Vehicle Interaction [J]. Packaging Engineering, Vol. 18, 2020.

[10]. Wu D., Sun G.Y. Towards Explainable Interactive Artificial Intelligence: Motivations, Approaches, and Research Trends [J]. Journal of Wuhan University (Philosophy and Social Science Edition), 2021, (No. 5).

[11]. F. K. Došilović, M. Brčić and N. Hlupić, Explainable artificial intelligence: a survey, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 0210-0215. doi: 10.23919/MIPRO.2018.8400040.

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