The document presents research on how human capital can complement machine learning to mitigate bias. The study hypothesizes that machine learning alone cannot address biases from incomplete inputs without domain expertise. An experiment shows that domain expertise helps correct ML tools by providing missing context, while technical skills ensure proper use of the tools. The results demonstrate that human capital can reduce ML bias through complementary expertise in the domain and technology vintage.

1. MACHINE LEARNING AND HUMAN CAPITAL COMPLEMENTARITIES:
EXPERIMENTAL EVIDENCE ON
BIAS MITIGATION
AUTHORS: PRITHWIRAJ CHOUDHURY| EVAN STARR| RAJSHREE AGARWAL
Presented by: MartinaVedrini Torricelli

2. BACKGROUND
◼ The use of machine learning
(ML) for productivity in the
knowledge economy
requires considerations of
important biases that may
arise from ML predictions.
ML's users care about the
bias of predictions may arise
from ML. For this reason,
the paper aims to explore
how to mitigate bias.

3. GAP
• If and how human capital may
serve as a complement to ML
as a potential solution to bias
arising from input
incompleteness.
RESEARCH QUESTION
• How can firms mitigate such
bias to unlock the potential of
ML?
• How may human capital
complement ML to do so?
HYPOTHESIS
• ML will not address biases
due to input incompleteness
without complementary
domain specific expertise, and
user-interface complexities of
ML require that humans who
provide such expertise also
have complementary vintage
specific human capital.

4. CORE PROPOSITIONS
Observational tests:
◼ Patent language dynamically changes over time → ML will have difficulty finding the
most relevant prior art
◼ Complementarities between ML and domain-expertise.
Experimental tests:
◼ ML is helpful in making your work easier and faster ?
◼ Disadvantage and/ or bias of ML → ML examines patents finds the most similar (but
superficial) but not the most relevant.
◼ Is the human capital able to reduce the bias ?
◼ Domain expertise
◼ CS&E backgrounds

5. THE EXPERIMENT IN BRIEF
Sample:
221 MBA student
(random sample)
Dependent variables:
• The silver bullet patent
• Productivity
Independent variables:
• ML
• Expert Advice
• CS&E

6. RESULTS
The observational and experimental analyses show where and
why human capital complements ML → domain expertise and
vintage-specific skills
Domain expertise complements ML by correcting for the
(strategic) incompleteness of the input to the ML tool, while
vintage-specific skills (computer science and engineering
knowledge CS&E) ensure the ability to properly operate the
technology.

7. CONTRIBUTIONS
◼ Creating new literature on bias
in ML: underlining input
incompleteness as a source of
bias.
◼ Highlighting the role of
domain-specific expertise as a
complement to ML.
◼ The results on productivity
differentials arising from
vintage-specific skills contribute
to the strategic management of
innovation literature on pace of
technology substitution.

8. LIMITATIONS
The authors
focus on the
early stages of
the evolution of
ML
technologies in
a one-shot
experiment
Reliance on an
experimental
design and
choice of MBA
students as
subjects
Finding the
silver bullet was
relatively
uncommon
Research
context and
technology
vintages are
very specific to
Sigma

9. IDEAS FOR THE FUTURE
1
Investigate the
issue of the silver
bullet in an even
larger sample
2
Analyze the
performance
improvement in a
longer periods of
time
3
Replicate the
experiment using
a different sample
4
Replicate the
experiment with
another ML tool
other than Sigma
in order to
compare the
results

10. THANKS