28 citations · 42 across the 6 of their papers we have counts for
7 papers
We Need Explanation Cards to Connect Explanation Algorithms to the Real World
Eric Günther, Balázs Szabados, Kristof Meding +3
Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, the meaning of algorithmic explan…
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…
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…
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
Timo Freiesleben, Gunnar König
Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts…
Efficient SAGE Estimation via Causal Structure Learning
Christoph Luther, Gunnar König, Moritz Grosse-Wentrup
The Shapley Additive Global Importance (SAGE) value is a theoretically appealing interpretability method that fairly attributes global importance to a model's features. However, it…
Improvement-Focused Causal Recourse (ICR)
Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Algorithmic recourse recommendations, such as Karimi et al.'s (2021) causal recourse (CR), inform stakeholders of how to act to revert unfavourable decisions. However, some actions…
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…