1k citations · 1.4k across the 27 of their papers we have counts for
8 papers · 1 filter
On the Privacy Risks of Algorithmic Recourse
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to…
Decomposing Counterfactual Explanations for Consequential Decision Making
Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci
The goal of algorithmic recourse is to reverse unfavorable decisions (e.g., from loan denial to approval) under automated decision making by suggesting actionable feature changes (…
I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data
Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle +1
We examine machine learning models in a setup where individuals have the choice to share optional personal information with a decision-making system, as seen in modern insurance pr…
Language Models are Realistic Tabular Data Generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann +2
Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data's characteristics remains a significant chal…
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
Martin Pawelczyk, Tobias Leemann, Asia Biega +1
As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One…
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Chirag Agarwal, Dan Ley, Satyapriya Krishna +6
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introd…
Rethinking Stability for Attribution-based Explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk +4
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are st…
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel +2
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely…