Strategic management

1. Machine learning and human capital complementarities: Experimental
evidence on bias mitigation
presentation by:
Hananeh KOMEITI •AUTHORS:
•PRITHWIRAJ CHOUDHURY
•EVAN STARR
•RAJSHREE AGARWAL

2. The use of machine learning requires considerations of important biases that may arise from ML
predictions.
A new source of bias related to incompleteness in real time inputs which may result from strategic
behavior by agents
Domain expertise of users can complement ML by mitigating this bias
For ensuring 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.

3. Whether Artificial intelligence (AI) and machine
learning (ML) would substitute or complement
human capital. Despite the promise of ML in
increasing productivity, many firms have
encountered significant challenges due to biases
in predictions. The combination of strategically
generated inputs and imperfect adversarial
training of ML creates biased predictions that
stem from what we term input incompleteness.

4. RESEARCH
QUESTION
❖ How can firms mitigate such bias to unlock
the potential of ML?
❖ How may human capital complement ML to
do so?

5. ❖ Individuals who possess domain expertise—the skills and knowledge accumulated through prior learning within a
domain are complementary to ML in mitigating bias stemming from input incompleteness, because domain experts
bring relevant outside information to correct for strategically altered inputs.
❖ We also posit that individuals with vintage-specific skills _ the skills and knowledge accumulated through prior
familiarity of tasks with the technology—will be more productive because they will have higher absorptive capacity to
handle the complexities in ML technology interfaces.

6. Gap research
How human capital may
serve as a complement to ML
as a potential solution to bias
arising from input
incompleteness.

7. Scholars have focused on two related issues critical to
how ML interfaces with humans: Comparative
advantage of humans and machines in cognitive tasks,
and relative cognitive biases of humans and machines.

8. We theorize that domain expertise of users can complement
ML by mitigating this bias. We also document the
importance of vintage-specific skills, and discuss the
implications for artificial intelligence and strategic
management of human capital.

9. Scholars have focused on two related issues critical to how
ML interfaces with humans: Comparative advantage of
humans and machines in cognitive tasks, and relative
cognitive biases of humans and machines.

10. Observational: support two assumptions
1. firstly, Dynamic change of patent language over
time.
Secondly, more experienced examiners bring
additional knowledge to the adjudication process.
RESEARCH METHODS

11. ML directs examiners to a narrower set of knowledge
ML technology directs examiners to patents more similar
to the focal patent application, but less similar to the silver
bullet patent.
domain expertise shifts the narrow distribution closer to
the most relevant prior art.
Those with CS&E backgrounds perform better on ML
technology
support the propositions

12. ✔ Authors focus on the early stages of the evolution of ML
technologies in a one-shot experiment.
✔ Their reliance on an experimental design and choice of MBA
students as subjects was motivated by the need for a sample
of highly skilled but heterogeneously specialized labor force
of “novice users” of process technology.
✔ Research context and technology vintages are very specific—applicable to
USPTO's development of the ML tool Sigma relative to Boolean search. ML
technologies are themselves heterogeneous, as are the contexts in which they
are deployed and the way they are deployed (i.e., with or without complex
user interfaces).

13. The paper demonstrated the experts in specialization are
complementary to Machine learning (ML) in mitigating bias.
Based on the study, future research may explore the balance
between ML and talent cultivation for firms.
Which is the most efficient way to deploy the task with ML and
human capital integration?

14. Our study is motivated by potential biases that limit the effectiveness of ML process
technologies and the scope for human capital to be complementary in reducing such
biases.
Our findings related to the importance of domain and vintage-specific human capital.
we have highlighted the complementary role of two critical human capital
attributes—domain-specific expertise and vintage specific skills. With these in
conjunction, ML technologies may well be able to deliver on the optimism for the
future.