Fair machine learning methods seek to train models that balance model
pe...
We study a new class of estimands in causal inference, which are the
sol...
The success of DNNs is driven by the counter-intuitive ability of
over-p...
Algorithmic fairness is an increasingly important field concerned with
d...
Many popular algorithmic fairness measures depend on the joint distribut...
Methods for building fair predictors often involve tradeoffs between fai...
We conduct an audit of pricing algorithms employed by companies in the
I...
Cognitive diagnosis models (CDMs) are a popular tool for assessing stude...
Motivated by Breiman's rousing 2001 paper on the "two cultures" in
stati...
Algorithmic fairness is a topic of increasing concern both within resear...
Since the events of the Arab Spring, there has been increased interest i...