30 citations · 41 across the 8 of their papers we have counts for
8 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…
Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act
Lucas G. Uberti-Bona Marin, Bram Rijsbosch, Kristof Meding +3
Technical and legal debates frequently suggest that "accuracy" is an objective, measurable, and purely technical property. We challenge this view, showing that evaluating AI perfor…
Using predictive multiplicity to measure individual performance within the AI Act
Karolin Frohnapfel, Mara Seyfert, Sebastian Bordt +2
When building AI systems for decision support, one often encounters the phenomenon of predictive multiplicity: a single best model does not exist; instead, one can construct many m…
Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences
Alina Wernick, Kristof Meding
The EU has become one of the vanguards in regulating the digital age. A particularly important regulation in the Artificial Intelligence (AI) domain is the 2024 enacted EU AI Act.…
Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data
Henrik Nolte, Michèle Finck, Kristof Meding
Does GPT know you? The answer depends on your level of public recognition; however, if your information was available on a website, the answer could be yes. Most Large Language Mod…
Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
Kristof Meding, Thilo Hagendorff
Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of…
Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research
Thilo Hagendorff, Kristof Meding
Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. F…