19 citations · 23 across the 5 of their papers we have counts for
6 papers · 1 filter
Inducing Group Fairness in Prompt-Based Language Model Decisions
James Atwood, Nino Scherrer, Preethi Lahoti +3
Classifiers are used throughout industry to enforce policies, ranging from the detection of toxic content to age-appropriate content filtering. While these classifiers serve import…
FRAPPE: A Group Fairness Framework for Post-Processing Everything
Alexandru Tifrea, Preethi Lahoti, Ben Packer +3
Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited com…
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Ad…
Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti, Alex Beutel, Jilin Chen +5
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fai…
Operationalizing Individual Fairness with Pairwise Fair Representations
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of…
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairne…