On February 12, 2021 Dr. Ming Jack Po joined Virginia Tech to
discuss the impact of bias on machine learning.
Machine learning is a
branch of artificial intelligence that uses the idea that systems can
learn from the data that they collect. These systems can use what they
have learned to identify patterns and make decisions with minimal human
intervention. Machine learning is used in an increasing number of
products and technologies from search engines to video surveillance
analysis. Despite the utility of machine learning, bias in the
development of these systems can impact the effectiveness of online
advertising, the accuracy of facial recognition algorithms, and the
validity of research data. Recognizing the potential for bias in machine
learning can help universities mitigate the impact of bias on future
systems.
Speaker Description:
Dr. Po is product manager at Google
working in healthcare and machine learning. At Google, he has led teams
in health, research, cloud, and search. Prior to joining Google, Po
spent a decade working in different senior operating and venture capital
roles in areas related to medical devices, healthcare delivery, and
global health.
Learning Objectives:
- Discuss the impact of that bias
can have on the accuracy of machine learning algorithms
- Describe the
detrimental effects that inaccurate or offensive search results,
produced by machine learning algorithms, can have on people and
communities
- Examine the role diversity plays in developing more
effective machine learning algorithms