Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence
| Autor: | Byung Hyung Kim, Seunghun Koh, Sejoon Huh, Sunghee Choi, Sungho Jo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning General Computer Science Computer science Process (engineering) Computer Science - Artificial Intelligence explanatory efficacy Interface (computing) Computer Science - Human-Computer Interaction 02 engineering and technology Recommender system Affect (psychology) Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences General Materials Science EEG human-centric explainable artificial intelligence 050107 human factors Neural correlates of consciousness business.industry 05 social sciences General Engineering Usability Workload Affect Artificial Intelligence (cs.AI) interactive explanation 020201 artificial intelligence & image processing brain lateralization Artificial intelligence Metric (unit) lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 189013-189024 (2020) |
| DOI: | 10.48550/arxiv.1912.07416 |
| Popis: | Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals with 62.4% accuracy, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|