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| Sec. | AI | for Human Learning and Behavior |
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| artificial intelligence ( | AI | ) have been, and will continue |
| 2020). | AI | has also been useful in |
| ai | , 2020). Though |
| artificial intelligence | is already applied to a |
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| considered true | artificial intelligence | that is comparable to human |
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| ai | , 2020). Humans are not capable |
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| artificial intelligence” (X | AI | ; Gunning et al., 2019 |
| AI | , but, rather, discuss |
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| the autonomous capabilities of | AI | systems advance (Lyons, 2013 |
| lead to misuse of the | AI | system. Further, over-reliance on |
| complacency and failing to detect | AI | system errors |
| AI | , and resulting inappropriately reliance on |
| noted above, trust in | AI | systems is closely related to |
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| collaboration between computer science, | AI | , and machine learning |
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| developing | AI | systems that effectively interact with |
| the future behavior of the | AI | system more accurately (Riedl |
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| learning, explanation in AI, or interpretability in | AI | , nor |
| apply theories to | artificial intelligence | . From this, we lay the |
| foundation for computer science and | AI | researchers to understand what |
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| interactions, failures can occur because | AI | has neglected the |
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| intensive problem-solving at which | AI | is already adept |
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| has great implications for human- | AI | interactions |
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| Related to social signal processing, | AI | capabilities are still |
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| traditional evaluation of | AI | systems that are primarily based |
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| teammate. We know that current | AI | |
| understanding of how | AI | systems come to their decisions |
| human teammates to reject the | AI | system (Akula et al |
| ai | , 2020; Papagni and Koeszegi, 2021 |
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| AI | system within a human-agent |
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| factors that can affect an | AI | system's |
| As | AI | progresses, interdisciplinary research is needed |
| structure. | AI | , then, can only be expected |
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| ai | , J |
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| emergence to explainable and empathetic | AI | |
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| you: theory of mind in | AI | . Psychol. Med. 50 |
| DARPA (2016). Explainable Artificial Intelligence (X | AI | ). Technical |
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| artificial intelligence | : why, when, what for, and |
| ai | .2021.104541 |
| Z. (2019). XAI-Explainable | artificial intelligence | . Sci. Robot. 4, 37 |
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| Intelligence ( | AI | ) applications in orthopaedics: an innovative |
| AI | Soc. 34, 535–543. doi |
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| Formalizing trust in | artificial intelligence | : prerequisites, causes |
| goals of human trust in | AI | ,” in Proceedings of the 2021 |
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| artificial intelligence | : Nonverbal social signal prediction in |
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| implications of | artificial intelligence | . Bus. Horizons 62, 15–25 |
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| AI | Spring Symposium Series |
| Miller, T. (2019). Explanation in | artificial intelligence | : insights |
| A. (2020). “The use of | AI | in |
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| Rai, A. (2020). Explainable | AI | : from black box to glass |
| M. O. (2019). Human-centered | artificial intelligence | and machine |
| 2021). Symbolic behaviour in | artificial intelligence | . arXiv preprint |
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