We consider the problem of subset selection where one is given multiple
...
Automatically assigning tasks to people is challenging because human
per...
Elections are the central institution of democratic processes, and often...
In real-world classification settings, individuals respond to classifier...
Assessing the diversity of a dataset of information associated with peop...
We study fair classification in the presence of an omniscient adversary ...
Subset selection algorithms are ubiquitous in AI-driven applications,
in...
A robust body of evidence demonstrates the adverse effects of implicit b...
Extractive summarization algorithms can be used on Twitter data to retur...
Due to the growing deployment of classification algorithms in various so...
Implicit bias is the unconscious attribution of particular qualities (or...
Social bias in machine learning has drawn significant attention, with wo...
One reason for the emergence of bias in AI systems is biased data -- dat...
Online advertising platforms are thriving due to the customizable audien...
Online advertising platforms are thriving due to the customizable audien...
Motivated by concerns that machine learning algorithms may introduce
sig...
Case studies, such as Kay et al., 2015 have shown that in image
summariz...
We present a prototype for a news search engine that presents balanced
v...
Developing classification algorithms that are fair with respect to sensi...
Personalization is pervasive in the online space as it leads to higher
e...
Sampling methods that choose a subset of the data proportional to its
di...
Coordinate descent methods minimize a cost function by updating a single...
We study multiwinner voting problems when there is an additional require...
Many stochastic optimization algorithms work by estimating the gradient ...
Determinantal Point Processes (DPPs) are probabilistic models that arise...
One goal of online social recommendation systems is to harness the wisdo...