Daniel Hendrycks

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-133

May 16, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.pdf

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. To address the growing need for safe ML systems, I first discuss works towards making systems perform reliably. Thereafter I discuss works towards making systems act in accordance with human values. In closing I discuss open problems in making ML systems safer.

Advisors: Dawn Song and Jacob Steinhardt

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BibTeX citation:

@phdthesis{Hendrycks:EECS-2022-133,
    Author= {Hendrycks, Daniel},
    Title= {Machine Learning Safety},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.html},
    Number= {UCB/EECS-2022-133},
    Abstract= {Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. To address the growing need for safe ML systems, I first discuss works towards making systems perform reliably. Thereafter I discuss works towards making systems act in accordance with human values. In closing I discuss open problems in making ML systems safer.},
}

EndNote citation:

%0 Thesis
%A Hendrycks, Daniel 
%T Machine Learning Safety
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 16
%@ UCB/EECS-2022-133
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.html
%F Hendrycks:EECS-2022-133