An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence

Conference paper

Hepenstal, S., Zhang, L. and Wong, BL William 2021. An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence. https://doi.org/10.1109/SMC52423.2021.9659095
AuthorsHepenstal, S., Zhang, L. and Wong, BL William
TypeConference paper
Description

Intelligence analysis involves unpredictable processes and decision making about complex domains where analysts rely upon expertise. Artificial Intelligence (AI) systems could support analysts as they perform analysis tasks, to enhance their expertise. However, systems must also be cognisant about how expertise is gained and designed so that this is not impinged. In this paper, we describe the results of Cognitive Task Analysis interviews with 6 experienced intelligence analysts. We capture themes, in terms of their decision making paths during an analysis task, and highlight how each theme is both influenced by expertise and an influence upon expertise. We also identify important interdependencies between themes. We propose that our findings can be used to help design Human-Centered AI (HCAI) systems for supporting intelligence analysts.

Year2021
Conference 2021 IEEE International Conference on Systems, Man and Cybernetics
Digital Object Identifier (DOI)https://doi.org/10.1109/SMC52423.2021.9659095
File
File Access Level
Open
Publisher's version
Publication process dates
Deposited10 Feb 2022
Permalink -

https://repository.canterbury.ac.uk/item/905qy/an-analysis-of-expertise-in-intelligence-analysis-to-support-the-design-of-human-centered-artificial-intelligence

Download files

File
decision-making-DI (1).pdf
File access level: Open
Publisher's version
An_analysis_of_expertise_in_intelligence_analysis_to_support_the_design_of_Human-Centered_Artificial_Intelligence.pdf

  • 101
    total views
  • 185
    total downloads
  • 3
    views this month
  • 4
    downloads this month

Export as

Related outputs

Investigating security issues (multilayer attacks) on IoT devices using machine learning
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Investigating security issues (multilayer attacks) on IoT devices using machine learning.
Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2024. Safeguarding IoMT: Semi-automated Intrusion Detection System (SAIDS) for detecting multilayer attacks.
Exploring optimal set of features in machine learning for improving IoT multilayer security
Al Sukhni, B., Manna, S., Dave, J. and Zhang, Leishi 2023. Exploring optimal set of features in machine learning for improving IoT multilayer security. 2023 IEEE 9th World Forum on Internet of Things (WF-IoT). https://doi.org/10.1109/wf-iot58464.2023.10539376
A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions
Obarafor, Victor, Qi, Man and Zhang, L. 2023. A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions. in: 2023 8th IEEE International Conference on Signal and Image Processing (ICSIP) Wuxi, China IEEE. pp. 1074-1078
The impact of system transparency on analytical reasoning
Hepenstal, S., Zhang, L. and Wong, B. 2023. The impact of system transparency on analytical reasoning. in: CHI '23: CHI Conference on Human Factors in Computing Systems, Hamburg Germany, April 23 - 28, 2023 New York ACM.
The impact of system transparency on analytical reasoning
Hepenstal, S., Zhang, L. and Wong, B.L.W. 2023. The impact of system transparency on analytical reasoning.
Machine learning-based solutions for securing IoT systems against multilayer attacks
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks. in: Singh Tomar, R., Verma, S., Kumar Chaurasia, B., Singh, V., Abawajy, J. H., Akashe, S., Hsiung, Pao-Ann and Prasad, R. (ed.) Communication, Networks and Computing Third International Conference, CNC 2022, Gwalior, India, December 8–10, 2022, Proceedings, Part I Cham Springer. pp. 140-153
Investigating the security issues of multi-layer IoMT attacks using machine learning techniques
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Investigating the security issues of multi-layer IoMT attacks using machine learning techniques.
Designing a system to mimic expert cognition: An initial prototype
Hepenstal, Sam, Zhang, Leishi and William Wong, B. L. 2022. Designing a system to mimic expert cognition: An initial prototype. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 66 (1), pp. 2057-2061. https://doi.org/10.1177/1071181322661092
Automated identification of insight seeking behaviours, strategies and rules: a preliminary study
Hepenstal, S., Zhang, L. and Wong, BL William 2021. Automated identification of insight seeking behaviours, strategies and rules: a preliminary study. Sage Journals: Proceedings of the Human Factors and Ergonomics Society Annual Meeting . (65), pp. 1269-1273. https://doi.org/https://doi.org/10.1177/1071181321651348
A granular computing approach to provide transparency of intelligent systems for criminal investigations
Zhang, L. 2021. A granular computing approach to provide transparency of intelligent systems for criminal investigations. in: Pedrycz, W. and Chen, S.-M. (ed.) Interpretable Artificial Intelligence: A Perspective of Granular Computing Cham Springer.
Developing conversational agents for use in criminal investigations
Hepenstal, S., Zhang, L., Kodagoda N. and Wong B.L.W 2021. Developing conversational agents for use in criminal investigations. ACM Transactions on Interactive Intelligent Systems. 11 (3-4), pp. 1-35. https://doi.org/10.1145/3444369