Cognitive Challenges in Human-Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation
By A. Fügener, Jörn Grahl, Alok Gupta, W. Ketter. Information systems research, 2021
365 citations recorded by the source corpus.
A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.
Subfields: Artificial Intelligence, Health Informatics, Safety Research
View Paper: https://www.semanticscholar.org/paper/73c4417142eea24e290bf574de653fcc9604c591
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Citations in the corpus
- AI literacy and its implications for prompt engineering strategies (2024, background): “…who rely on their Theory of Mind mental models to interpret natural language AI outputs may develop misconceptions, leading to frustrating interactions and failure to realize the true potential of this technology (Bewersdorff, Zhai, Roberts, & Nerdel, 2023; Fügener, Grahl, Gupta, & Ketter, 2022).”
- Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration (2026, unknown): “Existing literature has explored complementarity through the lens of human-algorithm collaboration, systematically investigating the conceptualization and methodologies to leverage the complementarity (Bansal et al. 2021, Fügener et al. 2022, Donahue et al. 2022).”