Datasets (particularly benchmarks) are infrastructure: a foundation for other tools & tech, tending to seep into the background, shaped by specific aims, seeming natural from one perspective but jarring from another
@cephaloponderer @alexhanna @amironesei
arxiv.org/abs/2007.07399
@cephaloponderer @alexhanna @amironesei
arxiv.org/abs/2007.07399
Focusing on *transparency* of a ML system without plausible actions of being able to change aspects of that system
are a Pyrrhic victory. *Contestability*, however, allows us
to critically engage within the system.
are a Pyrrhic victory. *Contestability*, however, allows us
to critically engage within the system.
Let's move beyond insufficient training data as
the sole "solution" to discriminatory outcomes.
Gathering
more training data from populations which are already extensively surveilled ignores how data-gathering operations can serve as another
form of "predatory inclusion"
the sole "solution" to discriminatory outcomes.
Gathering
more training data from populations which are already extensively surveilled ignores how data-gathering operations can serve as another
form of "predatory inclusion"
These quotes are from "Bringing the People Back In: Contesting Benchmark Machine Learning
Datasets" by @cephaloponderer @alexhanna @amironesei @andrewthesmart Hilary Nicole @morganklauss #PAML2020 #ICML2020 arxiv.org/abs/2007.07399
Datasets" by @cephaloponderer @alexhanna @amironesei @andrewthesmart Hilary Nicole @morganklauss #PAML2020 #ICML2020 arxiv.org/abs/2007.07399
