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X-WR-CALNAME:Exposing Shortcomings and Improving the Reliability of Machine
Learning Explanations
X-WR-TIMEZONE:America/Los_Angeles
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UID:2022-01-31-dylan-slack@cml.ics.uci.edu
DTSTAMP:20220131T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20220131T130000
DTEND;TZID=America/Los_Angeles:20220131T140000
SUMMARY:[CML Seminar] Dylan Slack: Exposing Shortcomings and Improving the
Reliability of Machine Learning Explanations
LOCATION:Online (live stream)
DESCRIPTION:Dylan Slack\, PhD Student\, Department of Computer Science\, Un
iversity of California\, Irvine\n\nTitle: Exposing Shortcomings and Improv
ing the Reliability of Machine Learning Explanations\n\nAbstract: For doma
in experts to adopt machine learning models in high-stakes settings such a
s health care and law\, they must understand and trust model predictions.
Researchers have proposed numerous ways to explain complex ML models\, but
these approaches suffer from critical drawbacks such as vulnerability to
adversarial attacks\, instability\, and inconsistency. This talk describes
the shortcomings of explanations\, demonstrates how they are vulnerable t
o adversarial attacks\, and presents recent work on explanations that leve
rage uncertainty estimates to overcome several critical explanation shortc
omings.\n\nhttps://cml.ics.uci.edu/seminars/2022-01-31-dylan-slack
X-ALT-DESC;FMTTYPE=text/html:Dylan Slack\, PhD Student\,
Department of Computer Science\, University of California\, Irvine
Title: Exposing Shortcomings and Improving the Reliability of Mach
ine Learning Explanations
Abstract: For domain experts to ad
opt machine learning models in high-stakes settings such as health care an
d law\, they must understand and trust model predictions. Researchers have
proposed numerous ways to explain complex ML models\, but these approache
s suffer from critical drawbacks such as vulnerability to adversarial atta
cks\, instability\, and inconsistency. This talk describes the shortcoming
s of explanations\, demonstrates how they are vulnerable to adversarial at
tacks\, and presents recent work on explanations that leverage uncertainty
estimates to overcome several critical explanation shortcomings.
<
a href="https://cml.ics.uci.edu/seminars/2022-01-31-dylan-slack">https://c
ml.ics.uci.edu/seminars/2022-01-31-dylan-slack
URL:https://cml.ics.uci.edu/seminars/2022-01-31-dylan-slack
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