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08/08/2023
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
Machine learning models are increasingly utilized across impactful domai...
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07/27/2023
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
With the increased deployment of machine learning models in various real...
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07/26/2023
Efficient Estimation of the Local Robustness of Machine Learning Models
Machine learning models often need to be robust to noisy input data. The...
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07/25/2023
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
Chain-of-thought (CoT) prompting has been shown to empirically improve t...
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06/11/2023
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
The Right to Explanation is an important regulatory principle that allow...
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06/09/2023
Consistent Explanations in the Face of Model Indeterminacy via Ensembling
This work addresses the challenge of providing consistent explanations f...
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02/08/2023
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
The Right to Explanation and the Right to be Forgotten are two important...
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11/10/2022
On the Privacy Risks of Algorithmic Recourse
As predictive models are increasingly being employed to make consequenti...
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09/18/2022
Towards Robust Off-Policy Evaluation via Human Inputs
Off-policy Evaluation (OPE) methods are crucial tools for evaluating pol...
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08/19/2022
Evaluating Explainability for Graph Neural Networks
As post hoc explanations are increasingly used to understand the behavio...
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07/08/2022
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues
Machine Learning (ML) models are increasingly used to make critical deci...
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06/22/2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
While several types of post hoc explanation methods (e.g., feature attri...
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06/14/2022
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks
The highly non-linear nature of deep neural networks causes them to be s...
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06/06/2022
A Human-Centric Take on Model Monitoring
Predictive models are increasingly used to make various consequential de...
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06/02/2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
Despite the plethora of post hoc model explanation methods, the basic pr...
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05/15/2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
As post hoc explanation methods are increasingly being leveraged to expl...
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03/14/2022
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establ...
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03/13/2022
Algorithmic Recourse in the Face of Noisy Human Responses
As machine learning (ML) models are increasingly being deployed in high-...
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02/03/2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
As practitioners increasingly deploy machine learning models in critical...
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02/03/2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
As various post hoc explanation methods are increasingly being leveraged...
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06/24/2021
What will it take to generate fairness-preserving explanations?
In situations where explanations of black-box models may be useful, the ...
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06/23/2021
Feature Attributions and Counterfactual Explanations Can Be Manipulated
As machine learning models are increasingly used in critical decision-ma...
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06/18/2021
On the Connections between Counterfactual Explanations and Adversarial Examples
Counterfactual explanations and adversarial examples have emerged as cri...
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06/16/2021
Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations
As Graph Neural Networks (GNNs) are increasingly employed in real-world ...
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06/04/2021
Counterfactual Explanations Can Be Manipulated
Counterfactual explanations are emerging as an attractive option for pro...
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03/29/2021
Learning Under Adversarial and Interventional Shifts
Machine learning models are often trained on data from one distribution ...
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02/26/2021
Towards Robust and Reliable Algorithmic Recourse
As predictive models are increasingly being deployed in high-stakes deci...
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02/25/2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning
As the representations output by Graph Neural Networks (GNNs) are increa...
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02/21/2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in criti...
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12/22/2020
Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses
As predictive models are being increasingly deployed to make a variety o...
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12/01/2020
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Ranking algorithms are being widely employed in various online hiring pl...
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11/12/2020
Robust and Stable Black Box Explanations
As machine learning black boxes are increasingly being deployed in real-...
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11/12/2020
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
As machine learning (ML) models are increasingly being employed to assis...
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11/12/2020
Ensuring Actionable Recourse via Adversarial Training
As machine learning models are increasingly deployed in high-stakes doma...
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10/21/2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Domains where supervised models are deployed often come with task-specif...
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09/15/2020
Interpretable and Interactive Summaries of Actionable Recourses
As predictive models are increasingly being deployed in high-stakes deci...
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08/11/2020
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
As local explanations of black box models are increasingly being employe...
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06/14/2020
Fair Influence Maximization: A Welfare Optimization Approach
Several social interventions (e.g., suicide and HIV prevention) leverage...
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11/15/2019
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
As machine learning black boxes are increasingly being deployed in criti...
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11/06/2019
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
As machine learning black boxes are increasingly being deployed in domai...
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07/04/2017
Interpretable & Explorable Approximations of Black Box Models
We propose Black Box Explanations through Transparent Approximations (BE...
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10/28/2016
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Predictive models deployed in the real world may assign incorrect labels...
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10/21/2016