Fair, Data-efficient, and Trusted Computer Vision Call for Papers
Call for Papers: CVPR 2021 Workshop on
Fair, Data-efficient, and Trusted Computer Vision
As the computer vision research community makes rapid progress in
producing algorithms with human-level performance, it is extremely
critical that we take a step back and assess, and consequently promise
to the consumer world, what this objective performance reported in
academic literature means in the context of real-world systems and
applications. As a concrete example, it is one thing for a social
media organization to use an algorithm to automatically identify a
person of interest in pictures uploaded to its platform. On the other
hand, the use of algorithms in making life-changing decisions in areas
such as healthcare (e.g., should a certain treatment be administered?)
or jurisprudence (e.g., should this person be released from prison?)
is a totally different ballgame. At the very least, the following
questions will be asked of the algorithm/system by the user (e.g.,
doctors or lawyers in the example above):
-Why is the algorithm predicting X?
-How sure is the algorithm of this prediction/decision?
-Why should I trust the algorithm?
-How can I be sure the algorithm has been fair in the process leading
up to its prediction/decision?
-Is the algorithm biased?
Answers to these questions can have profound consequences depending on
the application (e.g., accidents and autonomous vehicles, life/death
for a patient, incarceration/freedom for an accused). Consequently, as
artificial intelligence (AI) is seeing increasing adoption in a
variety of daily-life applications, addressing the underlying themes
of the questions above has become a matter of urgent importance. In
light of these issues, we seek to provide a focused venue for academic
and industry researchers and practitioners to discuss research
challenges and solutions associated with learning computer vision
models with the overarching requirements of fairness, data efficiency,
and trustworthiness. In particular, we ask:
-How can we make our algorithms more explainable and trustworthy than they currently are?
-How can we make our algorithms more fair and less biased than they currently are?
-How can we train robust models under biased and scarce data?
-How can we detect bias or scarcity in data for a given objective function?
Topics for TCV 2021 include, but are not limited to:
-Algorithms and theories for explainable and interpretable computer
-Application-specific designs for explainable computer vision, e.g.,
healthcare, autonomous driving, etc
-Algorithms and theories for learning computer vision models under
bias and scarcity
-Performance characterization of vision algorithms and systems under
bias and scarcity.
-Algorithms for secure and privacy-aware machine learning for computer
-Algorithms and theories for trustworthy computer vision models
-The role of adjacent fields of study (e.g, computational social
science) in mitigating issues of bias and trust in computer vision
-Paper submission deadline: March 31 2021 11.59pm Pacific Time
-Notification to authors: April 7 2021 11.59pm Pacific Time
-Camera ready deadline: April 15 2021 11.59pm Pacific Time
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