168 citations · 205 across the 6 of their papers we have counts for
9 papers · 1 filter
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen +2
As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function e…
Feature Attributions and Counterfactual Explanations Can Be Manipulated
Dylan Slack, Sophie Hilgard, Sameer Singh +1
As machine learning models are increasingly used in critical decision-making settings (e.g., healthcare, finance), there has been a growing emphasis on developing methods to explai…
Counterfactual Explanations Can Be Manipulated
Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju +1
Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical…
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
We typically compute aggregate statistics on held-out test data to assess the generalization of machine learning models. However, statistics on test data often overstate model gene…
Differentially Private Language Models Benefit from Public Pre-training
Gavin Kerrigan, Dylan Slack, Jens Tuyls
Language modeling is a keystone task in natural language processing. When training a language model on sensitive information, differential privacy (DP) allows us to quantify the de…
Fair Meta-Learning: Learning How to Learn Fairly
Dylan Slack, Sorelle Friedler, Emile Givental
Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-lear…