Journal Article/Contribution to a conference proceedings/Contribution to a book

FZJ-2026-04956

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data

Zhao, X.FZJ* ; Krieger, L.FZJ* ; Cao, Z. ; Bangun, A.FZJ* ; Scharr, H.FZJ* ; Assent, I.FZJ*

2026

Abstract: Counterfactual explanations (CFs) help understand machine learning models by identifying minimal input changes that would lead to alternative model outcomes. Recent work demonstrates their utility for reconstructing black-box models, enabling third-party auditing of opaque decision systems for fairness and accountability. Still, CF-based reconstruction may suffer from decision boundary shifts, overfitting, and restrictive assumptions requiring online query access to target platforms.We propose \textbf{REconstruction via Counterfactual-Aware waSserstein opTimization (RECAST)} under limited data and restricted access, a behavioral surrogate model based on Wasserstein barycentric prototypes. Our approach addresses decision boundary shifts by incorporating CFs as informative, though less representative, samples for both classes, maintaining high surrogate fidelity in low-sample regimes without requiring online access during reconstruction. To enhance fairness auditing, our method enables systematic group fairness diagnostics.Experiments on real-world datasets and various setups show that \textbf{RECAST} effectively achieves high fidelity and query efficiency, as well as stable results even when the access is limited and noisy.

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2026

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