research
∙
06/16/2023
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
We consider the problem of subset selection where one is given multiple ...
share
research
∙
05/31/2023
Designing Closed-Loop Models for Task Allocation
Automatically assigning tasks to people is challenging because human per...
research
∙
05/30/2022
Diverse Representation via Computational Participatory Elections – Lessons from a Case Study
Elections are the central institution of democratic processes, and often...
research
∙
05/22/2022
Addressing Strategic Manipulation Disparities in Fair Classification
In real-world classification settings, individuals respond to classifier...
research
∙
07/15/2021
Auditing for Diversity using Representative Examples
Assessing the diversity of a dataset of information associated with peop...
research
∙
06/10/2021
Fair Classification with Adversarial Perturbations
We study fair classification in the presence of an omniscient adversary ...
research
∙
11/09/2020
Mitigating Bias in Set Selection with Noisy Protected Attributes
Subset selection algorithms are ubiquitous in AI-driven applications, in...
research
∙
10/21/2020
The Effect of the Rooney Rule on Implicit Bias in the Long Term
A robust body of evidence demonstrates the adverse effects of implicit b...
research
∙
07/15/2020
Dialect Diversity in Text Summarization on Twitter
Extractive summarization algorithms can be used on Twitter data to retur...
research
∙
06/08/2020
Fair Classification with Noisy Protected Attributes
Due to the growing deployment of classification algorithms in various so...
research
∙
01/23/2020
Interventions for Ranking in the Presence of Implicit Bias
Implicit bias is the unconscious attribution of particular qualities (or...
research
∙
11/04/2019
Assessing Social and Intersectional Biases in Contextualized Word Representations
Social bias in machine learning has drawn significant attention, with wo...
research
∙
06/05/2019
Fair Distributions from Biased Samples: A Maximum Entropy Optimization Framework
One reason for the emergence of bias in AI systems is biased data -- dat...
research
∙
01/29/2019
Toward Controlling Discrimination in Online Ad Auctions
Online advertising platforms are thriving due to the customizable audien...
research
∙
01/29/2019
Fair Online Advertising
Online advertising platforms are thriving due to the customizable audien...
research
∙
01/29/2019
Improved Adversarial Learning for Fair Classification
Motivated by concerns that machine learning algorithms may introduce sig...
research
∙
01/29/2019
Implicit Diversity in Image Summarization
Case studies, such as Kay et al., 2015 have shown that in image summariz...
research
∙
06/24/2018
Balanced News Using Constrained Bandit-based Personalization
We present a prototype for a news search engine that presents balanced v...
research
∙
06/15/2018
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
Developing classification algorithms that are fair with respect to sensi...
research
∙
02/23/2018
An Algorithmic Framework to Control Bias in Bandit-based Personalization
Personalization is pervasive in the online space as it leads to higher e...
research
∙
02/12/2018
Fair and Diverse DPP-based Data Summarization
Sampling methods that choose a subset of the data proportional to its di...
research
∙
12/08/2017
Stochastic Dual Coordinate Descent with Bandit Sampling
Coordinate descent methods minimize a cost function by updating a single...
research
∙
10/27/2017
Group Fairness in Multiwinner Voting
We study multiwinner voting problems when there is an additional require...
research
∙
08/08/2017
Stochastic Optimization with Bandit Sampling
Many stochastic optimization algorithms work by estimating the gradient ...
research
∙
08/01/2016
On the Complexity of Constrained Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models that arise...
research
∙
03/14/2016
