This document provides an outline for research on how technology and organizational practices affect firm success when deploying artificial intelligence for complex decision making. The study examines how varying incentive structures and the capabilities of AI/ML systems impact the performance of human decision makers. A controlled experiment will be conducted using loan application evaluations to test if aligning the goals of AI/ML with organizational incentives leads to more reliable decisions and mixed performance when technology and practices are not aligned. The results could provide practical insights for integrating AI/ML across organizations.
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Man-Machine Collaboration in Organizational Decision- Making: An Experimental Study Using Loan Application Evaluations
1. MAN-MACHINE COLLABORATION IN
ORGANIZATIONAL DECISION-
MAKING: AN EXPERIMENTAL STUDY
USING LOAN APPLICATION
EVALUATIONS
Anh Luong, Nanda Kumar, Karl R. Lang
anh.luong@baruch.cuny.edu
Zicklin School of Business,
Baruch College,
Graduate Center,
City University of New York
December 15, 2019
SIG-DSA
4. RESEARCH
MOTIVATION
Firms across industries are rapidl adopting Artificial
Intelligence/Machine Learning (AI/ML) technologies
However, not et full reaped their benefits:
Unclear understanding of AI-human relationship
Lacking foundational resources to integrate AI/ML across
units within firms
The use of AI/ML in organi ational decision-making involves
three components:
the e e,
the ech g , and
the ga i a i a practices
Extant AI/ML usage literature has focused on examining the
perspectives and characteristics of the users, while not et
full explored the impacts of the other two factors
Anh Luong
5. RESEARCH
QUESTION
Our stud examines the var ing impacts of AI/ML
s stems (the technolog ), incentive structures (the
organi ational practices), and importantl , their
d namics, on the performance of the human decision
makers (the people), which comprise and ultimatel
represent fi c e .
Research Question:
How do technolog and organi ational resources affect
firm success when deplo ing AI/ML for complex
decision-making?
Anh Luong
6. 1950S
Meehl 1954
1970S
Dawes 1979
2000-2016
Rader & Gra 2015
Rosenblat & Stark 2016
Lee et al. 2015
Dietvorst et al. 2015
2016-NOW
Dietvorst et al. 2016
Logg et al. 2019
Yeomans et al. 2019
Alexander et al. 2018
Packin et al. 2019
RELATED LITERATURE
AI/ML SAGE:
FROM A ERSION O ADOP ION
Anh Luong
29. Fi h ld c ide
b h he ali f
AI/ML a d alig e
f hei ga i a i al
ac ice a d
i le e he
c c e l
C ib e Alg i h
U age li e a e b
f c i g he
cha ac e i ic f bo h
he IT e ce a d he
ga i a i al ac ice
E a i i g hei
in erdependen effec
he fi de l a
eliable AI/ML
la f a d gi e
h a DM ade a e
i e i e ac i h i
=> i ed
e f a ce
D namic e ec i e :
Ca e S die i h ke
DM a d a age
ac a i fi :
eal- ld e ec i e
PRACTICAL THEORY THEORY
FUTURE
RESEARCH
Implica ions. Ne S eps