Current study on human-computer interaction in machine learning

Research Article
Open access

Current study on human-computer interaction in machine learning

Yalan Cai 1 , Yuyang Lu 2*
  • 1 Fuzhou No. 8 High School    
  • 2 Nanjing No.13 High School    
  • *corresponding author 1910751103@mail.sit.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230425
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

Machine learning has become one of the research hotspots at home and internationally due to the continued growth of artificial intelligence, and the application of machine learning is more and more widely developed. In the process of applying machine learning methods to real problems, there are defects that lead to biased results. This paper discusses the importance and necessity of human-machine interaction in the application of machine learning methods, as well as where human-machine interaction occurs, and puts forward two questions: "whether human should interact with machine in the process of machine learning" and "how to make machine learning have better performance". To answer the above two questions, this paper concludes that in the application of machine learning methods, people with certain professional knowledge can get better results in the machine learning process. Further, when machine learning is applied to the real world, there are some flaws that lead to failure or unsatisfactory results, and this paper proposes a way to improve this undesirable phenomenon by involving people in the machine learning process. Finally, this paper summarizes the main shortcomings of current machine learning, clarifies the development direction of machine learning that must be anthropocentric, and expresses some views on machine learning.

Keywords:

interactive machine learning, human-computer interaction, anthropocentric development

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References

[1]. Barnova, K., Mikolasova, M., Kahankova, R. V., Jaros, R., Kawala-Sterniuk, A., Snasel, V., Mirjalili, S., Pelc, M., & Martinek, R. (2023). Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Computers in biology and medicine, 163, 107135. Advance online publication.

[2]. Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. iScience, 23(11), 101656.

[3]. Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised Machine Learning: A Brief Primer. Behavior therapy, 51(5), 675–687.

[4]. Eckhardt, C. M., Madjarova, S. J., Williams, R. J., Ollivier, M., Karlsson, J., Pareek, A., & Nwachukwu, B. U. (2023). Unsupervised machine learning methods and emerging applications in healthcare. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 31(2), 376–381.

[5]. Kunneman, F., Lambooij, M., Wong, A., Bosch, A. V. D., & Mollema, L. (2020). Monitoring stance towards vaccination in twitter messages. BMC medical informatics and decision making, 20(1), 33.

[6]. Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. iScience, 23(11), 101656.

[7]. Peng X, Yang J, Hu Y, Li J, & Jia Z. (2008). An early warning method of flavistratus jumping armor based on semi-supervised learning. Agricultural Mechanization Research (3), 4.

[8]. Liang J, Gao J, & Chang Y. (2009). Progress in semi-supervised learning. Journal of Shanxi University: Natural Science Edition, 32(4), 7.

[9]. Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural networks : the official journal of the International Neural Network Society, 152, 267–275.

[10]. Maadi, M., Akbarzadeh Khorshidi, H., & Aickelin, U. (2021). A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications. International journal of environmental research and public health, 18(4), 2121.

[11]. Ni Mhurchu, C., Eyles, H., Jiang, Y., & Blakely, T. (2018). Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial. Appetite, 121, 360–365.

[12]. Çelikok, M. M., Murena, P. A., & Kaski, S. (2023). Modeling needs user modeling. Frontiers in artificial intelligence, 6, 1097891.

[13]. Peng Y. (2006). A novel ensemble machine learning for robust microarray data classification. Computers in biology and medicine, 36(6), 553–573.

[14]. King, A. J., Cooper, G. F., Clermont, G., Hochheiser, H., Hauskrecht, M., Sittig, D. F., & Visweswaran, S. (2019). Using machine learning to selectively highlight patient information. Journal of biomedical informatics, 100, 103327.

[15]. Martin, R. K., Ley, C., Pareek, A., Groll, A., Tischer, T., & Seil, R. (2022). Artificial intelligence and machine learning: an introduction for orthopaedic surgeons. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 30(2), 361–364.


Cite this article

Cai,Y.;Lu,Y. (2024). Current study on human-computer interaction in machine learning. Applied and Computational Engineering,36,77-83.

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 2023 International Conference on Machine Learning and Automation

ISBN:978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor:Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN:2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. Barnova, K., Mikolasova, M., Kahankova, R. V., Jaros, R., Kawala-Sterniuk, A., Snasel, V., Mirjalili, S., Pelc, M., & Martinek, R. (2023). Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Computers in biology and medicine, 163, 107135. Advance online publication.

[2]. Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. iScience, 23(11), 101656.

[3]. Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised Machine Learning: A Brief Primer. Behavior therapy, 51(5), 675–687.

[4]. Eckhardt, C. M., Madjarova, S. J., Williams, R. J., Ollivier, M., Karlsson, J., Pareek, A., & Nwachukwu, B. U. (2023). Unsupervised machine learning methods and emerging applications in healthcare. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 31(2), 376–381.

[5]. Kunneman, F., Lambooij, M., Wong, A., Bosch, A. V. D., & Mollema, L. (2020). Monitoring stance towards vaccination in twitter messages. BMC medical informatics and decision making, 20(1), 33.

[6]. Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. iScience, 23(11), 101656.

[7]. Peng X, Yang J, Hu Y, Li J, & Jia Z. (2008). An early warning method of flavistratus jumping armor based on semi-supervised learning. Agricultural Mechanization Research (3), 4.

[8]. Liang J, Gao J, & Chang Y. (2009). Progress in semi-supervised learning. Journal of Shanxi University: Natural Science Edition, 32(4), 7.

[9]. Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural networks : the official journal of the International Neural Network Society, 152, 267–275.

[10]. Maadi, M., Akbarzadeh Khorshidi, H., & Aickelin, U. (2021). A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications. International journal of environmental research and public health, 18(4), 2121.

[11]. Ni Mhurchu, C., Eyles, H., Jiang, Y., & Blakely, T. (2018). Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial. Appetite, 121, 360–365.

[12]. Çelikok, M. M., Murena, P. A., & Kaski, S. (2023). Modeling needs user modeling. Frontiers in artificial intelligence, 6, 1097891.

[13]. Peng Y. (2006). A novel ensemble machine learning for robust microarray data classification. Computers in biology and medicine, 36(6), 553–573.

[14]. King, A. J., Cooper, G. F., Clermont, G., Hochheiser, H., Hauskrecht, M., Sittig, D. F., & Visweswaran, S. (2019). Using machine learning to selectively highlight patient information. Journal of biomedical informatics, 100, 103327.

[15]. Martin, R. K., Ley, C., Pareek, A., Groll, A., Tischer, T., & Seil, R. (2022). Artificial intelligence and machine learning: an introduction for orthopaedic surgeons. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 30(2), 361–364.