Artificial-Intelligence Assisted Decision Making: A Statistical Framework

61 Pages Posted: 11 Jan 2020

See all articles by Han Hong

Han Hong

Stanford University

Xin Lin

Beihang University (BUAA) - Department of Computer Science

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Jingyuan Wang

Beihang University (BUAA)

Date Written: December 22, 2019

Abstract

This paper proposes a statistical framework in which artificial intelligence can assist human decision making. Using observational data we benchmark the performance of each decision maker against the machine predictions, and replace decision makers whose information process quality is dominated by machine predictions based on the proposed criteria. The statistical frameworks that we proposed are applicable based on both Bayesian principles and frequentist principles of hypothesis testing and confidence set formation. Our theoretical discussion is illustrated by an example of birth defect detection, using a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples that are provided by the Chinese Ministry of Health. Based on doctor's diagnosis, we find doctors, especially those who are from rural areas, can be replaced by the machine learning prediction. Statistically, the overall quality of our algorithm on a testable data set outperforms the diagnoses made only by doctors, with higher true positive rate and lower false positive rate. Our example also informs that decision making with artificial intelligence is more beneficial to poor areas relative to developed places.

Keywords: artificial intelligence, machine learning, decision making, ROC curve

JEL Classification: C1, C5, D9, J2

Suggested Citation

Hong, Han and Lin, Xin and Tang, Ke and Wang, Jingyuan, Artificial-Intelligence Assisted Decision Making: A Statistical Framework (December 22, 2019). Available at SSRN: https://ssrn.com/abstract=3508224 or http://dx.doi.org/10.2139/ssrn.3508224

Han Hong (Contact Author)

Stanford University ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States

Xin Lin

Beihang University (BUAA) - Department of Computer Science ( email )

Beijing, 100083
China

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China
13466777332 (Phone)

Jingyuan Wang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

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