230 research outputs found
Can There be Art Without an Artist?
Generative Adversarial Network (GAN) based art has proliferated in the past
year, going from a shiny new tool to generate fake human faces to a stage where
anyone can generate thousands of artistic images with minimal effort. Some of
these images are now ``good'' enough to win accolades from qualified judges. In
this paper, we explore how Generative Models have impacted artistry, not only
from a qualitative point of view, but also from an angle of exploitation of
artisans --both via plagiarism, where models are trained on their artwork
without permission, and via profit shifting, where profits in the art market
have shifted from art creators to model owners or to traders in the unregulated
secondary crypto market. This confluence of factors risks completely detaching
humans from the artistic process, devaluing the labor of artists and distorting
the public perception of the value of art
FairCanary: Rapid Continuous Explainable Fairness
Machine Learning (ML) models are being used in all facets of today's society
to make high stake decisions like bail granting or credit lending, with very
minimal regulations. Such systems are extremely vulnerable to both propagating
and amplifying social biases, and have therefore been subject to growing
research interest. One of the main issues with conventional fairness metrics is
their narrow definitions which hide the complete extent of the bias by focusing
primarily on positive and/or negative outcomes, whilst not paying attention to
the overall distributional shape. Moreover, these metrics are often
contradictory to each other, are severely restrained by the contextual and
legal landscape of the problem, have technical constraints like poor support
for continuous outputs, the requirement of class labels, and are not
explainable.
In this paper, we present Quantile Demographic Drift, which addresses the
shortcomings mentioned above. This metric can also be used to measure
intra-group privilege. It is easily interpretable via existing attribution
techniques, and also extends naturally to individual fairness via the principle
of like-for-like comparison. We make this new fairness score the basis of a new
system that is designed to detect bias in production ML models without the need
for labels. We call the system FairCanary because of its capability to detect
bias in a live deployed model and narrow down the alert to the responsible set
of features, like the proverbial canary in a coal mine
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