activity
20222026
most citedFair Interpretable Representation Learning with Correction Vectors

5 citations · 6 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CY2026

Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making

Maike Lindermayr, Mattia Cerrato, Luisa Hübner +1

While fairness has become a central concern in research on algorithmic systems, the field remains predominantly shaped by Computer Science, resulting in a strong emphasis on formal…

cs.CY2026

Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse

Mattia Cerrato, Otto Sahlgren, Xenia Heilmann

Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. Thi…

cs.CY2026

Are Algorithm Registers Transparent? Perspectives from Germany

Iman Peljto, Xenia Heilmann, Mattia Cerrato

Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe…

cs.CL2026

From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation

Minh Duc Bui, Xenia Heilmann, Mattia Cerrato +2

Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, ex…

cs.LG2026

Rashomon Sets and Model Multiplicity in Federated Learning

Xenia Heilmann, Luca Corbucci, Mattia Cerrato

The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision boundaries. Understanding the di…

cs.CR2025

N-Parties Private Structure and Parameter Learning for Sum-Product Networks

Xenia Heilmann, Ernst Althaus, Mattia Cerrato +3

A sum-product network (SPN) is a graphical model that allows several types of probabilistic inference to be performed efficiently. In this paper, we propose a privacy-preserving pr…