Fw: LTI Colloquium: Friday, 7-Sep-2018 - Kai-Wei Chang, "Men Also Like Shopping: Reducing Societal Bias in Natural Language Processing Models."

[Litman, Diane J]

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From: Emily Ahn <emil...@gmail.com>
Sent: Thursday, September 6, 2018 11:05 AM
To: Han, Na-Rae; Litman, Diane J; Hwa, Rebecca
Subject: Fwd: LTI Colloquium: Friday, 7-Sep-2018 - Kai-Wei Chang, "Men Also Like Shopping: Reducing Societal Bias in Natural Language Processing Models."

 

Thank you for sharing this with folks at Pitt!

---------- Forwarded message ---------
From: Tessa Samuelson <tes...@andrew.cmu.edu>
Date: Thu, Sep 6, 2018 at 9:05 AM
Subject: LTI Colloquium: Friday, 7-Sep-2018 - Kai-Wei Chang, "Men Also Like Shopping: Reducing Societal Bias in Natural Language Processing Models."
To: <lti-s...@cs.cmu.edu>

Please join us for LTI's first colloquium of the semester. 

When: Friday, 9/7/18

Where: Porter Hall 100

Time: 2:30

Who: Kai-Wei Chang

Title: 

Men Also Like Shopping: Reducing Societal Bias in Natural

Language Processing Models

Abstract:

Machine learning techniques play important roles in our daily life.

Despite these methods being successful in various applications, they

run the risk of exploiting and reinforcing the societal biases (e.g.

gender bias) that are present in the underlying data. For instance, an

automatic resume filtering system may inadvertently select candidates

based on their gender and race due to implicit associations between

applicant names and job titles, causing the system to perpetuate

unfairness potentially. In this talk, I will describe a collection of

results that quantify and reduce biases in vision and language models,

including word embeddings, coreference resolution, and visual semantic

role labeling.

Bio:

Kai-Wei Chang is an assistant professor in the Department of Computer

Science at the University of California Los Angeles.  His research

interests include designing robust machine learning methods for large

and complex data and building language processing models for social

good applications. Kai-Wei has published broadly in machine learning,

natural language processing, and artificial intelligence and has been

involved in developing machine learning libraries (e.g., LIBLINEAR,

Vowpal Wabbit, and Illinois-SL) that are being used widely by the

research community. His awards include the EMNLP Best Long Paper Award (2017),  the KDD Best Paper Award (2010), the Yahoo! Key Scientific

Challenges Award (2011), and the Okawa Research Grant Award (2018).

Additional information is available at http://kwchang.net.

Refreshments will be served at 4:00 in the 5th floor kitchen-area of LTI. 

Instructor: Yulia Tsvetkov

Teaching Assistant: Emily Ahn