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EVENT DETAILS |
Thank you for your interest this Dataiku SF Meetup! The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, weve decided to pivot towards online webinars via our partner platform, Brighttalk. The entire Meetup in the same format will be held virtually, which allows for a live presentation & Q&A session after.
IMPORTANT: In order to gain entry, you must RSVP through this BrightTalk link: https://www.brighttalk.com/webcast/17108/412085?utm_source=Dataiku&utm_medium=brighttalk&utm_campaign=412085
Tentative Schedule:
11:00am: Intro
11:05am: Reducing AI Bias & Optimizing Data Labeling Frameworks w/ Appen by Monchu Chen
11:45am: Q&A
Talk Abstract:
Bias in machine learning has become a significant concern as AI technology spreads to more application domains. While some bias is a consequence of limits in design & tooling, bias in the training data itself is much more common. Skewed training data often promotes AI models that reveal discrimination & amplify human prejudices.
In this talk, we present a framework, developed at Appen, to minimize bias. This framework operates by routing data labeling tasks to the right labelers to avoid introducing bias. It also optimizes the process by determining a proper distribution of labelers for a given task.
Our speaker, Monchu Chen, will review some use cases where this framework has been applied, & discuss results that show how the
optimization process minimizes skew in the training data. Chen will also discuss extending this approach to other use cases & review the implications of this work.
Speaker bios:
Monchu Chen has worked in human-computer interaction for more than two decades. He has helped corporations & startups apply user insights to product innovation in multiple application domains.
Monchu now focuses on the human aspects of AI as the Principal Data Scientist for Appen's ML team, building models & systems to improve
annotation quality, efficiency, & reducing AI bias. Dr. Chen holds
a PhD from Carnegie Mellon University. He previously held a tenured
faculty position at Carnegie Mellon Portugal & is the author of
more than 70 publications & patentsS
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