Connectionists: Announcing Trustworthy ML Initiative and Virtual Seminar Series (starts Oct 29)

[Chhavi Yadav]

(Apologies for cross posting)

Hi all,

We wanted to share an initiative we launched recently: Trustworthy ML
Initiative
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(TrustML).
A major focus of the initiative is a bi-weekly virtual seminar series where
speakers discuss their work in various subfields of Trustworthy ML, such as
explainability, fairness, differential privacy, causality, robustness, etc.

The first seminar starts next Thursday Oct 29, 9am PT / 5pm London / 7pm
Addis Ababa. *We would like to invite you to attend the seminar and
participate in the post-seminar discussion!*

Our confirmed speakers include Percy Liang, Ayanna Howard, Irene Chen, Jenn
Wortman Vaughan, Cynthia Rudin, Pin-Yu Chen, Zachary Lipton, Steven Wu,
Shibani Santurkar, Celia Cintas, Katherine Heller, Gautam Kamath, Suresh
Venkatasubramanian, Sherri Rose, Alexander D'Amour, and more to come!

As part of our seminar series, we are also featuring students in Rising
Star Spotlight Talks. Please contact us at trustworthyml at gmail.com if you
are a student and want to present your work.

To enable easy access of fundamental resources in the field, we have also
collected links to courses, textbooks, videos, etc. on our website. We also
manage an active Twitter account @trustworthy_ml
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that
disseminates the latest work in trustworthy ML.

We welcome you to engage with our resources, attend our seminars, and send
us your work to be disseminated. All feedback is welcome!

On behalf of the Trustworthy ML Initiative organizers and advisors,
Hima Lakkaraju (Harvard)
Sara Hooker (Google Brain)
Sarah Tan (Facebook)
Subho Majumdar (AT&T Labs Research)
Chhavi Yadav (UCSD)
Jaydeep Borkar (Pune University)
Kamalika Chaudhuri (UCSD)
Tom Dietterich (Oregon State University)
Kush Varshney (IBM Research)


************************

*More about the Trustworthy ML initiative: *As machine learning (ML)
systems are increasingly being deployed in real-world applications, it is
critical to ensure that these systems are behaving responsibly and are
trustworthy. To this end, there has been growing interest from researchers
and practitioners to develop and deploy ML models and algorithms that are
not only accurate, but also explainable, fair, privacy-preserving, causal,
and robust. This broad area of research is commonly referred to as
trustworthy ML.

While it is incredibly exciting that researchers from diverse domains
ranging from machine learning to health policy and law are working on
trustworthy ML, this has also resulted in the emergence of critical
challenges such as information overload and lack of visibility for work of
early career researchers. Furthermore, the barriers to entry into this
field are growing day-by-day -- researchers entering the field are faced
with an overwhelming amount of prior work without a clear roadmap of where
to start and how to navigate the field.

To address these challenges, we are launching the Trustworthy ML Initiative
(TrustML) with the following goals:

- Enable easy access of fundamental resources to newcomers in the field.
- Provide a platform for early career researchers to showcase and
disseminate their work.
- Encourage discussion and debate on the latest work on trustworthy ML.
- Develop a community of researchers and practitioners working on topics
related to trustworthy ML.

Please check our website www.trustworthyml.org
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for
our ongoing efforts and programs!
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