[Colloquium] REMINDER: 9/25 TTIC Colloquium: Moritz Hardt, UC Berkeley

[Mary Marre via Colloquium]

When:     Monday, September 25th at 11:00 a.m.

Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526

Who:       Moritz Hardt, UC Berkeley


Title:       Biases Beyond Observation

Abstract:
Most proposed fairness measures for machine learning are
observational: They depend only on the joint distribution of the
features, predictor, and outcome. I will highlight a few useful
observational criteria, before arguing why observational criteria in
general are unable to resolve questions of fairness conclusively.
Moving beyond observational criteria, I will outline a causal
framework for reasoning about discrimination based on sensitive
characteristics.

Short bio:
Moritz Hardt is an Assistant Professor in the Department of Electrical
Engineering and Computer Sciences at the University of California,
Berkeley. After obtaining a PhD in Computer Science from Princeton
University in 2011, he was a postdoctoral scholar and research staff
member at IBM Research Almaden, followed by two years as a research
scientist at Google Research and Google Brain. Hardt’s research aims
to make the practice of machine learning more reliable, transparent,
and effective. Recognizing the societal impact of algorithms early on,
Hardt’s dissertation contributed to the foundations of differential
privacy and algorithmic fairness. In 2014, he co-founded FATML, a
highly successful recurring workshop on Fairness, Accountability, and
Transparency in Machine Learning.


Host: Nathan Srebro <nati at ttic.edu>


For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php



Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*





On Mon, Sep 18, 2017 at 5:06 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Monday, September 25th at 11:00 a.m.
>
> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Moritz Hardt, UC Berkeley
>
>
> Title:       Biases Beyond Observation
>
> Abstract:
> Most proposed fairness measures for machine learning are
> observational: They depend only on the joint distribution of the
> features, predictor, and outcome. I will highlight a few useful
> observational criteria, before arguing why observational criteria in
> general are unable to resolve questions of fairness conclusively.
> Moving beyond observational criteria, I will outline a causal
> framework for reasoning about discrimination based on sensitive
> characteristics.
>
> Short bio:
> Moritz Hardt is an Assistant Professor in the Department of Electrical
> Engineering and Computer Sciences at the University of California,
> Berkeley. After obtaining a PhD in Computer Science from Princeton
> University in 2011, he was a postdoctoral scholar and research staff
> member at IBM Research Almaden, followed by two years as a research
> scientist at Google Research and Google Brain. Hardt’s research aims
> to make the practice of machine learning more reliable, transparent,
> and effective. Recognizing the societal impact of algorithms early on,
> Hardt’s dissertation contributed to the foundations of differential
> privacy and algorithmic fairness. In 2014, he co-founded FATML, a
> highly successful recurring workshop on Fairness, Accountability, and
> Transparency in Machine Learning.
>
>
> Host: Nathan Srebro <nati at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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