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Xenia Heilmann

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3 papers

2 author rows

NAI Journal 2026 Journal Article

A Neurosymbolic Approach to Counterfactual Fairness

Xenia Heilmann
Chiara Manganini
Mattia Cerrato
Leonhard Kestel
Vaishak Belle

Integrating fairness into machine learning models has been an important consideration for the last decade. Here, neurosymbolic models offer a valuable opportunity, as they allow the specification of symbolic, logical constraints that are often guaranteed to be satisfied. However, research on neurosymbolic applications to algorithmic fairness is still in an early stage. In this work, we bridge this gap by integrating counterfactual fairness into the neurosymbolic framework of logic tensor networks (LTN). We use LTN to express accuracy and counterfactual fairness constraints in first-order logic and employ them to achieve desirable levels of both performance and fairness at training time. Our approach is agnostic to the underlying causal model and data generation technique; for this reason, it may be easily integrated into existing pipelines that generate and extract counterfactual examples. We show, through concrete examples on three benchmark datasets, that logical reasoning about counterfactual fairness has some important advantages, among which its intrinsic interpretability, and its flexibility in handling subgroup fairness. Compared to three recent methodologies in counterfactual fairness, our experiments show that a neurosymbolic, LTN-based approach attains better levels of counterfactual fairness.

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NeSy Conference 2025 Conference Paper

A Neurosymbolic Approach to Counterfactual Fairness

Xenia Heilmann
Chiara Manganini
Mattia Cerrato
Vaishak Belle

Integrating fairness into machine learning models has been an important consideration for the last decade. Here, neurosymbolic models offer a valuable opportunity, as they allow the specification of symbolic, logical constraints that are often guaranteed to be satisfied. However, research on neurosymbolic applications to algorithmic fairness is still in an early stage. With our work, we bridge this gap by integrating counterfactual fairness into the neurosymbolic framework of Logic Tensor Networks (LTN). We use LTN to express accuracy and counterfactual fairness constraints in first-order logic and employ them to achieve desirable levels of both performance and fairness at training time. Our approach is agnostic to the underlying causal model and data generation technique; as such, it may be easily integrated into existing pipelines that generate and extract counterfactual examples. We show, through concrete examples on three real-world datasets, that logical reasoning about counterfactual fairness has some important advantages, among which its intrinsic interpretability, and its flexibility in handling subgroup fairness. Compared to three recent methodologies in counterfactual fairness, our experiments show that a neurosymbolic, LTN-based approach attains better levels of counterfactual fairness.

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ICML Conference 2024 Conference Paper

Differentially Private Sum-Product Networks

Xenia Heilmann
Mattia Cerrato
Ernst Althaus

Differentially private ML approaches seek to learn models which may be publicly released while guaranteeing that the input data is kept private. One issue with this construction is that further model releases based on the same training data (e. g. for a new task) incur a further privacy budget cost. Privacy-preserving synthetic data generation is one possible solution to this conundrum. However, models trained on synthetic private data struggle to approach the performance of private, ad-hoc models. In this paper, we present a novel method based on sum-product networks that is able to perform both privacy-preserving classification and privacy-preserving data generation with a single model. To the best of our knowledge, ours is the first approach that provides both discriminative and generative capabilities to differentially private ML. We show that our approach outperforms the state of the art in terms of stability (i. e. number of training runs required for convergence) and utility of the generated data.

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