[CMU AI Seminar] Nov 16 at 12pm (Zoom) -- Moritz Hardt (UC Berkeley) -- Retiring Adult: New Datasets for Fair Machine Learning -- AI Seminar sponsored by Morgan Stanley

[Shaojie Bai]

Hi all,

Just a reminder that the CMU AI Seminar <http://www.cs.cmu.edu/~aiseminar/> is
tomorrow *12pm-1pm*:
https://cmu.zoom.us/j/99599979949?pwd=YXdzek1ic1FTbkZ6RytaN09Vajdodz09.

*Professor Moritz Hardt (UC Berkeley) *will be giving a talk on the
fairness in ML and a new dataset they recently worked on.

(See (updated) talk abstract below)

Thanks,
Shaojie

-----------------------------------------
*Talk Abstract:* Although the fairness community has recognized the
importance of data, researchers in the area primarily rely on UCI Adult
when it comes to tabular data. Derived from a 1994 US Census survey, this
dataset has appeared in hundreds of research papers where it served as the
basis for the development and comparison of many algorithmic fairness
interventions. We reconstruct a superset of the UCI Adult data from
available US Census sources and reveal idiosyncrasies of the UCI Adult
dataset that limit its external validity. Our primary contribution is a
suite of new datasets derived from US Census surveys that extend the
existing data ecosystem for research on fair machine learning. We create
prediction tasks relating to income, employment, health, transportation,
and housing. The data span multiple years and all states of the United
States, allowing researchers to study temporal shift and geographic
variation. We highlight a broad initial sweep of new empirical insights
relating to trade-offs between fairness criteria, performance of
algorithmic interventions, and the role of distribution shift based on our
new datasets. Our findings inform ongoing debates, challenge some existing
narratives, and point to future research directions. Our datasets are
available at folktables.org. Joint work with Frances Ding, John Miller, and
Ludwig Schmidt.

On Fri, Nov 12, 2021 at 11:29 AM Shaojie Bai <shaojieb at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *next Tuesday (11/16)* from *1**2:00-1:00
> PM (U.S. Eastern time)* for the next talk of our *CMU AI Seminar*,
> sponsored by Morgan Stanley
> <https://www.morganstanley.com/about-us/technology/>.
>
> To learn more about the seminar series or see the future schedule, please
> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> On 11/16, *Moritz Hardt* (UC Berkeley) will be giving a talk on "*Retiring
> Adult: New Datasets for Fair Machine Learning*" and his latest research
> on fair machine learning.
>
> *Title:* Retiring Adult: New Datasets for Fair Machine Learning
>
> *Talk Abstract:* Although the fairness community has recognized the
> importance of data, researchers in the area primarily rely on UCI Adult
> when it comes to tabular data. Derived from a 1994 US Census survey, this
> dataset has appeared in hundreds of research papers where it served as the
> basis for the development and comparison of many algorithmic fairness
> interventions. We reconstruct a superset of the UCI Adult data from
> available US Census sources and reveal idiosyncrasies of the UCI Adult
> dataset that limit its external validity. Our primary contribution is a
> suite of new datasets derived from US Census surveys that extend the
> existing data ecosystem for research on fair machine learning. We create
> prediction tasks relating to income, employment, health, transportation,
> and housing. The data span multiple years and all states of the United
> States, allowing researchers to study temporal shift and geographic
> variation. We highlight a broad initial sweep of new empirical insights
> relating to trade-offs between fairness criteria, performance of
> algorithmic interventions, and the role of distribution shift based on our
> new datasets. Our findings inform ongoing debates, challenge some existing
> narratives, and point to future research directions. Our datasets are
> available at folktables.org.
>
> *Speaker Bio: *Moritz Hardt is an Assistant Professor in the Department
> of Electrical Engineering and Computer Sciences at the University of
> California, Berkeley. His work builds foundations of machine learning and
> algorithmic decision making with a focus on social context, interaction,
> and impact. Hardt obtained a PhD in Computer Science from Princeton
> University with a dissertation on privacy-preserving data analysis and
> fairness in classification. He then held research positions at IBM Research
> and Google. Hardt co-founded the Workshop on Fairness, Accountability, and
> Transparency in Machine Learning. He is a co-author of "Fairness and
> Machine Learning: Limitations and Opportunities" and "Patterns,
> Predictions, and Actions: A Story about Machine Learning". He has received
> an NSF CAREER award, a Sloan fellowship, and best paper awards at ICML 2018
> and ICLR 2017.
>
> *Zoom Link: *
> https://cmu.zoom.us/j/99599979949?pwd=YXdzek1ic1FTbkZ6RytaN09Vajdodz09
>
> Best,
> Shaojie Bai (MLD)
>
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