AI in Business and Society
Paper Number
2482
Paper Type
Completed
Description
Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g., for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans' private information role is larger (or smaller) than that of machine-accessible public information. We propose and design a complementary framework that shifts the focus to the regulation of each human revision; and, in doing so, adjusts for human vulnerability to systematic biases. To test our framework, we collaborate with a European retailer to compile a large dataset (~1.1 mn transactions) on machine-led demand forecasts and human revisions. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our approach is theory-driven and easy to implement for practitioners.
Recommended Citation
Chen, Christopher; Jain, Nitish; and Karamshetty, Varun, "Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments" (2023). ICIS 2023 Proceedings. 24.
https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/24
Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments
Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g., for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans' private information role is larger (or smaller) than that of machine-accessible public information. We propose and design a complementary framework that shifts the focus to the regulation of each human revision; and, in doing so, adjusts for human vulnerability to systematic biases. To test our framework, we collaborate with a European retailer to compile a large dataset (~1.1 mn transactions) on machine-led demand forecasts and human revisions. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our approach is theory-driven and easy to implement for practitioners.
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