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.
Advisor: Dawn Song and Jacob Steinhardt
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