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A Design Thinking Framework for Human-Centric Explainable Artificial Intelligence in Time-Critical Systems

Stone, Paul Benjamin
http://rave.ohiolink.edu/etdc/view?acc_num=wright1673285878771171

Abstract Details

2022, Doctor of Philosophy (PhD), Wright State University, Engineering PhD.
Artificial Intelligence (AI) has seen a surge in popularity as increased computing power has made it more viable and useful. The increasing complexity of AI, however, leads to can lead to difficulty in understanding or interpreting the results of AI procedures, which can then lead to incorrect predictions, classifications, or analysis of outcomes. The result of these problems can be over-reliance on AI, under-reliance on AI, or simply confusion as to what the results mean. Additionally, the complexity of AI models can obscure the algorithmic, data and design biases to which all models are subject, which may exacerbate negative outcomes, particularly with respect to minority populations. Explainable AI (XAI) aims to mitigate these problems by providing information on the intent, performance, and reasoning process of the AI. Where time or cognitive resources are limited, the burden of additional information can negatively impact performance. Ensuring XAI information is intuitive and relevant allows the user to quickly calibrate their trust in the AI, in turn improving trust in suggested task alternatives, reducing workload and improving task performance. This study details a structured approach to the development of XAI in time-critical systems based on a design thinking framework that preserves the agile, fast-iterative approach characteristic of design thinking and augments it with practical tools and guides. The framework establishes a focus on shared situational perspective, and the deep understanding of both users and the AI in the empathy phase, provides a model with seven XAI levels and corresponding solution themes, and defines objective, physiological metrics for concurrent assessment of trust and workload.
Subhashini Ganapathy, Ph.D. (Advisor)
Sherif Elbasiouny, Ph.D. (Committee Member)
Asaf Harel, Ph.D. (Committee Member)
Victor Middleton, Ph.D. (Committee Member)
238 p.
Artificial Intelligence; Design; Engineering; Experiments; Industrial Engineering
Explainable Artificial Intelligence; Design Framework; Transparency; Trust; Human-Machine Teaming; Human-Centric AI; Eye-Tracking; Empathy; Design Thinking

Recommended Citations

Citations

  • Stone, P. B. (2022). A Design Thinking Framework for Human-Centric Explainable Artificial Intelligence in Time-Critical Systems [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1673285878771171

    APA Style (7th edition)

  • Stone, Paul. A Design Thinking Framework for Human-Centric Explainable Artificial Intelligence in Time-Critical Systems. 2022. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1673285878771171.

    MLA Style (8th edition)

  • Stone, Paul. "A Design Thinking Framework for Human-Centric Explainable Artificial Intelligence in Time-Critical Systems." Doctoral dissertation, Wright State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=wright1673285878771171

    Chicago Manual of Style (17th edition)

wright1673285878771171
401
© 2022, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.