This study examines how human capital complements machine learning to mitigate biases. Through experiments with MBA students simulating patent examiner tasks, the researchers find that domain expertise is needed to address biases from incomplete ML inputs, while vintage-specific skills ensure proper use of ML technologies. The hypothesis that ML requires complementary domain expertise and vintage skills to mitigate biases is supported. Managers should consider both attributes of human capital - domain expertise corrects strategic input issues, while vintage skills allow technology operation. Limitations include the student sample and inability to assess long-term impacts.

1. Machine learning and human capital
complementarities: Experimental
evidence on bias mitigation
Prithwiraj Choudhury | Evan Starr | Rajshree Agarwal

2. Research Questions
• How do firms Mitigate the bias to unlock the potential of ML?
• How many Human capital complements Machine Learning takes to do so?
• Positioning of Domain Expertise vis-à-vis Machine Learning ?
2
Research Motivation
Unleashing the productivity benefits of machine learning (ML)
technologies in the future of work requires managers to pay careful
attention to mitigating potential biases from its use.
Gap
Identify the set of possible ways the machine could be misled, and then
feed the ML technology as a way to train the algorithm.
if and how human capital may serve as a complement to ML as a
potential solution to bias arising from input incompleteness
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.
Methodology
Theory building project /qualitative
research
• Observational Test
• analyze how language in patents
changed over time
• Complementarities between ML and
domain-expertise.
• Experiment Test
• reproduction of the patent examiners
situation with students
• 221 MBA students, random samples /
compared ML vs. Domain expertise vs.
CS&E.

3. Abstract:
Research Summary:
The use of machine learning(ML) for productivity in the knowledge economy .we
find ML is biased toward 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.
Managerial Summary:
To ensure productivity benefits of ML in light of potentially strategic inputs, our
research suggests that managers need to consider two attributes of human
capital—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 ensure the ability to properly operate the
technology.

4. INTRODUCTION
Artificial intelligence (AI) and machine learning (ML)—where algorithms learn
from existing patterns in data to conduct statistically driven predictions and
facilitate decisions whether it would substitute or complement human
capitalntarities between machines'
According to the authors, individuals with specific skills and knowledge in a
domain may be complementary to ML because they will be better for
judgements decisions and avoid input incompleteness
it knowle
Increasing productivity
AI & ML Human capital

5. Contributions to theory:
How do the firms ensure productivity benefits of Machine Learning ?
Domain specific expertise and vintage specific skills are complementary and
make the optimal solution
Domain Expertise
It corrects strategic
incompleteness of inputs
Improve search strategy
Contribute to the literature
the skills and knowledge
accumulated through prior
learning within a domain
Vintage Specific Skills
Ability to properly operate the
technology
Different rates of absorptive
capacity

6. 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
Observational tests: Assumptions

7. Experimental tests
Dependent variables
It is examined dependent variables that reflect Levenshtein similarity
scores (Thoma et al., 2010) between the patents cited by the examiners
a) the focal patent. b) the silver bullet patent
Independent variables
it is manipulated two variables in this experiment: First, MLi is a dummy variable set to 1 if
examiner i was assigned to use the ML based process technology, Sigma (and 0 otherwise).
Second, Expert Advicei, is a dummy variable set to 1 if examiner i received the expert advice
email (and 0 otherwise)
Controls:
It is included several pretreatment control
variables to reduce residual variation and increase the precision of our estimates. These include
indicators for section, gender, whether the individual has a partner, and U.S. citizen

8. Variable definitions
Balance tests and empirical approach:

9. Result
How can firms mitigate such bias to unlock the potential of ML?
The research suggests that managers need to consider two attributes of human
capital - domain expertise and vintage-specific skills.How may human capital
complement ML to do so?
Domain expertise complements ML by correcting for incompleteness of the input
to the ML toolVintage-specific skills ensure the ability to properly operate the
technology
The hypothesis is proved positively that
“ ML will not address biases due to input incompleteness without
complementary domain specific expertise, and user-interface complexities of
ML requires that humans who provide such expertise also have
complementary vintage specific human capital”.
This makes it a two way process.

10. Limitations
• Unable to examine performance improvements over longer periods of time. •
It is unclear if constant exposure and learning-by-doing by workers would cause the relative differences
between the groups to grow or shrink over time.
Generalizability: MBA students vs. highly skilled individuals. •
Finding the silver bullet was relatively uncommon. Finding the silver bullet is not a common task (so it can
brings absurds results)