Zobrazeno 1 - 10
of 274
pro vyhledávání: '"Celis, L."'
This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the inst
Externí odkaz:
http://arxiv.org/abs/2409.04897
In many predictive contexts (e.g., credit lending), true outcomes are only observed for samples that were positively classified in the past. These past observations, in turn, form training datasets for classifiers that make future predictions. Howeve
Externí odkaz:
http://arxiv.org/abs/2402.11338
Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true u
Externí odkaz:
http://arxiv.org/abs/2310.17489
We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset. Score functions from the multiwinner voting literature have been used to aggregate rankings into quali
Externí odkaz:
http://arxiv.org/abs/2306.09835
Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings in which no oracle exists to assess the quality of human decisions and
Externí odkaz:
http://arxiv.org/abs/2305.19864
Elections are the central institution of democratic processes, and often the elected body -- in either public or private governance -- is a committee of individuals. To ensure the legitimacy of elected bodies, the electoral processes should guarantee
Externí odkaz:
http://arxiv.org/abs/2205.15394
Autor:
Keswani, Vijay, Celis, L. Elisa
In real-world classification settings, such as loan application evaluation or content moderation on online platforms, individuals respond to classifier predictions by strategically updating their features to increase their likelihood of receiving a p
Externí odkaz:
http://arxiv.org/abs/2205.10842
Autor:
Keswani, Vijay, Celis, L. Elisa
Assessing the diversity of a dataset of information associated with people is crucial before using such data for downstream applications. For a given dataset, this often involves computing the imbalance or disparity in the empirical marginal distribu
Externí odkaz:
http://arxiv.org/abs/2107.07393
We study fair classification in the presence of an omniscient adversary that, given an $\eta$, is allowed to choose an arbitrary $\eta$-fraction of the training samples and arbitrarily perturb their protected attributes. The motivation comes from set
Externí odkaz:
http://arxiv.org/abs/2106.05964
Autor:
Mehrotra, Anay, Celis, L. Elisa
Subset selection algorithms are ubiquitous in AI-driven applications, including, online recruiting portals and image search engines, so it is imperative that these tools are not discriminatory on the basis of protected attributes such as gender or ra
Externí odkaz:
http://arxiv.org/abs/2011.04219