69 citations · 203 across the 11 of their papers we have counts for
4 papers · 1 filter
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan +1
Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter pr…
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, Amit Sharma
To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to…
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfact…
Learning Fair Representations via an Adversarial Framework
Rui Feng, Yang Yang, Yuehan Lyu +3
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approv…