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Workshop on Dataset Curation and Security
Nathalie Baracaldo Angel · Yonatan Bisk · Avrim Blum · Michael Curry · John Dickerson · Micah Goldblum · Tom Goldstein · Bo Li · Avi Schwarzschild

Fri Dec 11 06:00 AM -- 11:00 AM (PST) @
Event URL: https://securedata.lol/ »

Classical machine learning research has been focused largely on models, optimizers, and computational challenges. As technical progress and hardware advancements ease these challenges, practitioners are now finding that the limitations and faults of their models are the result of their datasets. This is particularly true of deep networks, which often rely on huge datasets that are too large and unwieldy for domain experts to curate them by hand. This workshop addresses issues in the following areas: data harvesting, dealing with the challenges and opportunities involved in creating and labeling massive datasets; data security, dealing with protecting datasets against risks of poisoning and backdoor attacks; policy, security, and privacy, dealing with the social, ethical, and regulatory issues involved in collecting large datasets, especially with regards to privacy; and data bias, related to the potential of biased datasets to result in biased models that harm members of certain groups. Dates and details can be found at securedata.lol

Author Information

Nathalie Baracaldo Angel (IBM Research AI)
Yonatan Bisk (LTI @ CMU)
Avrim Blum (Toyota Technological Institute at Chicago)
Michael Curry (University of Maryland)
John Dickerson (University of Maryland)
Micah Goldblum (UMD)
Tom Goldstein (University of Maryland)
Bo Li (UIUC)
Avi Schwarzschild (University of Maryland)

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