 Optimal Integration: Human, Machine, and Generative AIHongda Zhong No 20330, CEPR Discussion Papers from Centre for Economic Policy Research Abstract: I study the optimal integration of humans and technologies in multi-layered decision-making processes. Each layer can correct existing errors but may also introduce new ones. A one-dimensional quality metric – a decision-maker’s error correction capability normalized by its new errors – determines the optimal rule: deploying higher-quality technologies in later stages. Interestingly, the final decision-making layer may not achieve the greatest error reduction; instead, its role hinges on minimizing new errors. Human effort varies asymmetrically across layers—early stages prioritize error correction with lower effort, while later stages emphasize avoiding new errors with higher effort. Applying the model to artificial intelligence (AI) reveals that AI's generative capabilities make it more likely to serve as the final decision-maker, reducing the need for costly human input, but underscoring the risks of AI hallucination. The theoretical framework also extends to applications including repeated delegation, automation design, loan screening, tenure review, and other multi-layer decision-making scenarios. Keywords: Automation; Delegation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:20330 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK. |
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