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Six Challenges for Human-AI Co-learning

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Author: Bosch, K. van den · Schoonderwoerd, T. · Blankendaal, R. · Neerincx, M.
Type:article
Date:2019
Publisher: Springer Verlag
Source:Schwarz, J.Sottilare, R.A., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1st International Conference on Adaptive Instructional Systems, AIS 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019, 26 July 2019 through 31 July 2019, 572-589
Identifier: 868465
doi: doi:10.1007/978-3-030-22341-0_45
ISBN: 9783030223403
Keywords: Co-active learning · Explainable AI · Artificial intelligence · Behavioral research · Cognitive systems · Human computer interaction · Active Learning · Human-agent teaming · Hybrid teams · Mental model · Theory of minds · Learning systems

Abstract

The increasing use of ever-smarter AI-technology is changing the way individuals and teams learn and perform their tasks. In hybrid teams, people collaborate with artificially intelligent partners. To utilize the different strengths and weaknesses of human and artificial intelligence, a hybrid team should be designed upon the principles that foster successful human-machine learning and cooperation. The implementation of the identified principles sets a number of challenges. Machine agents should, just like humans, have mental models that contain information about the task context, their own role (self-awareness), and the role of others (theory of mind). Furthermore, agents should be able to express and clarify their mental states to partners. In this paper we identify six challenges for humans and machines to collaborate in an adaptive, dynamic and personalized fashion. Implications for research are discussed. © 2019, Springer Nature Switzerland AG.