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  • August 2020
  • Article
  • Strategic Management Journal

Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation

By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
  • Format:Print
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Abstract

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize that domain expertise of users can complement ML by mitigating this bias. Our observational and experimental analyses in the patent examination context support this conjecture. In the face of “input incompleteness,” we find ML is biased towards finding prior art textually similar to focal claims, and domain expertise is needed to find the most relevant prior art. We also document the importance of vintage-specific skills and discuss the implications for artificial intelligence and strategic management of human capital.

Keywords

Machine Learning; Bias; Human Capital; Management; Strategy

Citation

Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
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About The Author

Prithwiraj Choudhury

Technology and Operations Management
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More from the Authors
  • Work-From-Anywhere: The Productivity Effects of Geographical Flexibility By: Prithwiraj Choudhury, Cirrus Foroughi and Barbara Larson
  • Pursuing the American Dream with 'WFA' By: Prithwiraj Choudhury
  • Doist: Building the Future of Asynchronous Work By: Prithwiraj Choudhury, Francesca Gino and Jeffrey Huizinga
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