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Collaboration and delegation between humans and artificial intelligence

Prof. Dr. Andreas Fügener, Prof. Dr. Jörn Grahl, Prof. Dr. Alok Gupta, Prof. Dr. Wolfgang Ketter

CDS members associated with the project: Prof. Dr. Jörn Grahl, Prof. Dr. Wolfgang Ketter

A defining question of our age is how general purpose artificial intelligence will influence the workplace of the future and, thereby, the human condition. The dominant perspective on this ongoing discussion is that the competition between AI and humans will be won by either humans or machines.

We argue that the future workplace may not belong exclusively to humans or machines. Instead, it may be better to use AI together with humans by combining their unique characteristics, strength, and abilities. We perform experimental studies on collaboration and work delegation between humans and AI. For example, we let humans and a state of the art AI classify images alone and together. The results raise more questions than they answer. For example, as expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was a configuration called inversion, where the AI delegated to the AI when it was uncertain.

To be clear: humans can outperform all other configurations (including the AI and inversion) if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well, the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI.

A social science perspective on artificial intelligence is rare. Studies that combine humans and AI in experimental settings and let them work together on Big-Data questions are important. We believe such research can have broad implications for the future of work, the design of decision support systems, and management education in the age of AI.