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stat.ML2026
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Timo Freiesleben, Kristof Meding, Gunnar König
Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can i…
stat.ML2025
Performative Validity of Recourse Explanations
Gunnar König, Hidde Fokkema, Timo Freiesleben +2
When applicants get rejected by an algorithmic decision system, recourse explanations provide actionable suggestions for how to change their input features to get a positive evalua…
stat.ML2024
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
Timo Freiesleben, Gunnar König, Christoph Molnar +1
To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful pre…