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Man-Machine Collaboration in Organizational Decision- Making: An Experimental Study Using Loan Application Evaluations

Anh Luong
Anh Luong

Talk given at AIS SIG-DSA (Special Interest Group in Decision Support and Analytics) 2019, Munich, Germany

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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
OUTLINE Research Background
Theoretical Foundations
Stud Design
Results
Conclusion
1.
2.
3.
4.
5.
Anh Luong
Re ea ch Backg d
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
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
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
Ad

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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
  • 2. OUTLINE Research Background Theoretical Foundations Stud Design Results Conclusion 1. 2. 3. 4. 5. Anh Luong
  • 3. Re ea ch Backg d
  • 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
  • 14. T ea men Manip la ion - Incen i e Alignmen
  • 25. Reg e i M del
  • 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
  • 30. Tha k Y a h.l g@ba ch.c .ed