[AI Seminar] Online AI Seminar on Sep 01 (Zoom) -- Timnit Gebru -- Computer vision: who is harmed and who benefits? -- AI seminar is sponsored by Fortive.

[Aayush Bansal]

Timnit Gebru (Google) will be giving an online seminar on "Computer vision:
who is harmed and who benefits?" from *12:00 - 01:00 PM* on Sep 01.

*Zoom Link*:
https://cmu.zoom.us/j/92984236320?pwd=dFRhNUVNQXA0b2dKYWlqYjRFSUVWQT09

CMU AI Seminar is sponsored by Fortive.

Following are the details of the talk:

*Title: *Computer vision: who is harmed and who benefits?

*Abstract: *Computer vision has ceased to be a purely academic endeavor.
>From law enforcement to border control to employment, healthcare
diagnostics, and assigning trust scores, computer vision systems have
started to be used in all aspects of society. This last year has also seen
a rise in public discourse regarding the use of computer-vision based
technology by companies such as Google, Microsoft, Amazon, and IBM. In
research, there exists work that purports to determine a person’s sexuality
from their social network profile images, or claims to classify “violent
individuals” from drone footage.

On the other hand, recent works have shown that commercial gender
classification systems have high disparities in error rates by skin-type
and gender, the existence of the gender bias contained in current image
captioning based works, and biases in the widely used CelebA dataset and
proposes adversarial learning-based methods to mitigate its effects.
Policymakers and other legislators have cited some of these seminal works
in their calls to investigate the unregulated usage of computer vision
systems. In this talk, I will highlight research on uncovering and
mitigating issues of unfair bias and historical discrimination that trained
machine learning models to learn to mimic and propagate.


*Bio*: Timnit Gebru is an Eritrean American computer scientist and the
technical co-lead of the Ethical Artificial Intelligence Team at Google.
She works on algorithmic bias and data mining. She is an advocate for
diversity in technology and is the co-founder of *Black in AI*, a community
of black researchers working in artificial intelligence.  Prior to this,
she did a postdoc at Microsoft Research, New York City in the FATE
(Fairness Transparency Accountability and Ethics in AI) group, where she
studied algorithmic bias and the ethical implications underlying any data
mining project (see this New York Times article
<https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html>
for
an example). She received her Ph.D. from the Stanford Artificial
Intelligence Laboratory, studying computer vision under Prof. Fei-Fei Li.
Her thesis pertains to data mining large scale publicly available images to
gain sociological insight and working on computer vision problems that
arise as a result. The Economist
<http://www.economist.com/news/science-and-technology/21717804-millions-images-public-streets-offer-cheap-sweeping-view-americas>
, The New York Times
<https://www.nytimes.com/2017/12/31/technology/google-images-voters.html>, and
others have covered part of this work. Prior to joining Fei-Fei's lab,
she worked at Apple designing circuits and signal processing algorithms for
various Apple products including the first iPad.


To learn more about the seminar series, please visit the website:
http://www.cs.cmu.edu/~aiseminar/


-- 
Aayush Bansal
http://www.cs.cmu.edu/~aayushb/
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