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Though artificial intelligence decreases human error in experimentation, human experts outperform AI when identifying causation or working with small data sets.
To capitalize on AI and researcher strengths, ORNL scientists, in collaboration with colleagues at National Cheng Kung University, Taiwan, and the University of Tennessee, Knoxville, developed a human-AI collaboration recommender system for improved experimentation performance.