Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1007/978-3-031-60615-1_22guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Logical Interference: Using AI to Correct Flaws in Human Judgment

Published: 29 June 2024 Publication History

Abstract

Humans and machines have disparate skills, with humans exemplifying higher-level cognitive skills such as problem-solving, decision-making, and creativity. In contrast, machines have an enormous computational capacity that no human could hope to match in accuracy or speed. These disparate sets of skills make humans and artificial intelligence (AI) ideal teammates to meet the demands of the most complex problem scenarios, including in the future Multi-Domain Operations Battlespace. To optimize performance, these skills should be strategically leveraged to form the basis for AI decision support software developments. This paper discusses decision-support systems, including their functions, current configurations, and whether these configurations are designed with the strengths of humans and AI in mind. A new model for decision-support systems offers an alternative to the traditional model that can maximize the benefits they provide each other in meeting mission goals.

References

[1]
Hawkins, T., Cassenti, D.: Defining the relationship between level of autonomy in a computer and cognitive workload of its user. In Mukherjee, S., Dutt, V., Srinivasan, N. (eds.) Applied Cognitive Science and Technology: Implications of Interaction Between Human Cognition and Technology, pp. 29–40. Springer, Singapore (2023).
[2]
Blasch, E., Salerno, J., Tadda, G.: Measuring the worthiness of situation assessment. In: Proceedings of 2011 IEEE National Aerospace and Electronics Conference, pp. 87–94. IEEE (2011)
[3]
Fitts, P.: Human engineering for an effective air navigation and traffic control system: National Research Council Washington, DC (1951)
[4]
Cummings M Man versus machine or man+machine? IEEE Intell. Syst. 2014 29 62-67
[5]
Dastani M, Indurkhya B, and Scha R Analogical projection in pattern perception J. Exp. Theor. Artif. Intell. 2003 15 489-511
[6]
Sutton R and Barto A Reinforcement learning: an introduction Robotica 1999 17 229-235
[7]
De Winter J and Dodou D Why the Fitts list has persisted throughout the history of function allocation Cogn. Technol. Work 2014 16 1-11
[8]
Cassenti, D., Roy, A., Kaplan, L.: Representing uncertainty information from AI for human understanding. In: Proceedings of Human Factors & Ergonomics Society Meeting (2023)
[9]
Kaber D and Endsley M The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task Theor. Issues Ergon. Sci. 2004 5 113-153
[10]
Endsley M and Kaber D Level of automation effects on performance, situation awareness and workload in a dynamic control task Ergonomics 1999 42 462-492
[11]
Sheridan T and Verplank W Human and Computer Control of Undersea Teleoperators 1978 Cambridge Massachusetts Institute of Technology
[12]
Cassenti, D., Roy, A., Hawkins, T., Thomson, R.: The effect of varying levels of automation during initial triage of intrusion detection. In: Ahram, T. Kalra, J., Karwowski, W. (eds.) Artificial Intelligence and Social Computing, AHFE International Conference. AHFE International, New York (2022)
[13]
Endsley M and Garland D Theoretical underpinnings of situation awareness: a critical review Situat. Aware. Anal. Measure. 2000 1 3-21
[14]
Bélanger, M., Guitouni, A., Pageau, N.: Decision support tools for the operational planning process. In: Proceedings of the 14th International Command and Control Research and Technology Symposium “C2 and Agility”, pp. 15–17. Washington, DC (2009)
[15]
Zachary, W.: Decision support systems: designing to extend the cognitive limits. In M. G. Hollander, M. (Ed), Handbook of Human-Computer Interaction, pp. 997–1030. North Holland, Amsterdam, Netherlands (1988)
[16]
Power D and Sharda R Model-driven decision support systems: concepts and research directions Decis. Support Syst. 2007 43 1044-1061
[17]
Reese, P.: Military decisionmaking process: Lessons and best practices. Center for Army Lessons Learned, Fort Leavenworth, Kansas (2015)
[18]
Falcon, R., Abielmona, R., Billings, S.: Risk-driven intent assessment and response generation in maritime surveillance operations. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, pp. 151–157. IEEE (2015)
[19]
Chen, Y., Cheng, M.: Enhanced HTN planning approach for COA generation. In: 2013 International Conference on Information Technology and Applications, pp. 272–274. IEEE (2013)
[20]
Kewley, R., Argenta, C., Brawner, K.: Behaving like soldiers: A multi-agent system approach to course of action planning for simulated military units. In: The 35th International FLAIRS Conference Proceedings, AAAI (2022)
[21]
O’Donnell, M., Hunter, J., Hough, J. Wilt, B., Patterson, E.: Roadmap to implement artificial intelligence in course of action development& effect of weather variables on UH-60 performance. In: Proceedings of the Annual General Donald R. Keith Memorial Conference, pp. 278–283. U.S. Military Academy, West Point, NY (2021)
[22]
Haider, S., Levis, A.: Effective course-of-action determination to achieve desired effects. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 37(6), 1140–1150 (2007)
[23]
Yuksek, B., Guner, G., Karali, H., Candan, B., Inalhan, G.: Intelligent Wargaming Approach to Increase Course of Action Effectiveness in Military Operations. In: AIAA SciTech Forum, pp. 23–27, AIAA, National Harbor, Maryland (2023)
[24]
Tu, H, Levchuk, Y., Pattipati, K.: Robust action strategies to induce desired effects. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 34(5), 664–680 (2004)
[25]
Hoyt R, Snider D, Thompson C, and Mantravadi S IBM Watson analytics: automating visualization, descriptive, and predictive analytics JMIR Public Health Surveill. 2016 2 2 e5810
[26]
Mohanty, B., Aashima, Mishra, S.: Role of artificial intelligence in financial fraud detection. Acead. Market. Stud. J. 27(4), 1–16 (2023)
[27]
Moorcroft, T. Simanjuntak, K. Dorjsuren, O., Sanaakhorol, M., Enkhtaivan, E., Watt, G., Eickhoff, V., Cerny, L., Deasy, C., Zimmermann, T.: Oyu Tolgoi and Rio Tinto partnership with Palantir Technologies to provide effective geotechnical risk management. In Caving 2022: Fifth International Conference on Block and Sublevel Caving, pp. 877–890. Australian Centre for Geomechanics, Perth, Australia (2022)
[28]
Bellaby R Can AI weapons make ethical decisions? Crim. Justice Ethics 2021 40 2 86-107
[29]
Seville H and Field D What can AI do for ethics? AISB Q. 2000 104 499-510
[30]
Munir A, Aved A, and Blasch E Situational awareness: techniques, challenges, and prospects AI. 2022 3 1 55-77
[31]
Endsley M Supporting human-AI teams: transparency, explainability, and situation awareness Comput. Hum. Behav. 2023 140 1-16
[32]
Cassenti, D., Veksler, V., Ritter, F.: Cognition-inspired artificial intelligence [Special Issue]. Top. Cogn. Sci. 14(4), 647–903 (2022)
[33]
MacKay D The problems of flexibility, fluency, and speed–accuracy trade-off in skilled behavior Psychol. Rev. 1982 89 5 483-506
[34]
Longo L, Wickens C, Hancock G, and Hancock P Human mental workload: a survey and a novel inclusive definition Front. Psychol. 2022 13 883321
[35]
Hodrien A and Fernando T A review of post-study and post-task subjective questionnaires to guide assessment of system usability J. Usability Stud. 2021 16 3 203-232
[36]
Lee J and See K Trust in automation: designing for appropriate reliance Hum. Factors 2004 46 1 50-80
[37]
Li D and Du Y Artificial Intelligence with Uncertainty 2007 New York Taylor & Francis

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings
Artificial Intelligence in HCI: 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29–July 4, 2024, Proceedings, Part III
Jun 2024
497 pages
ISBN:978-3-031-60614-4
DOI:10.1007/978-3-031-60615-1
  • Editors:
  • Helmut Degen,
  • Stavroula Ntoa

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 29 June 2024

Author Tags

  1. Human-AI Collaboration
  2. Decision-Support Systems
  3. Heuristics
  4. Decision Making

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 03 Oct 2024

Other Metrics

Citations

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media