activity
20222026
most citedWhy we do need Explainable AI for Healthcare

14 citations · 17 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

Jiaqi Qiu, Rob Goedhart, Jannis Kurtz +1

Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings. Among these approaches, AI-based stat…

cs.HC2026

Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations

Tabea E. Röber, Paul Festor, Rob Goedhart +2

Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study…

cs.LG2024

Clinicians' Voice: Fundamental Considerations for XAI in Healthcare

T. E. Röber, R. Goedhart, S. İ. Birbil

Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However,…

cs.LG2023★ 1 cited

Fixing confirmation bias in feature attribution methods via semantic match

Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte +6

Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods…

cs.AI2023★ 1 cited

Semantic match: Debugging feature attribution methods in XAI for healthcare

Giovanni Cinà, Tabea E. Röber, Rob Goedhart +1

The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explai…

cs.LG2023★ 1 cited

Finding Regions of Counterfactual Explanations via Robust Optimization

Donato Maragno, Jannis Kurtz, Tabea E. Röber +3

Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…

cs.HC2022★ 14 cited

Why we do need Explainable AI for Healthcare

Giovanni Cinà, Tabea Röber, Rob Goedhart +1

The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explai…