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arXiv · 2601.18678

Counterfactual Explanations on Robust Perceptual Geodesics

Eslam Zaher·Maciej Trzaskowski·Quan Nguyen·Fred Roosta
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Abstract

Latent-space optimization methods for counterfactual explanations - framed as minimal semantic perturbations that change model predictions - inherit the ambiguity of Wachter et al.'s objective: the choice of distance metric dictates whether perturbations are meaningful or adversarial. Existing approaches adopt flat or misaligned geometries, leading to off-manifold artifacts, semantic drift, or adversarial collapse. We introduce Perceptual Counterfactual Geodesics (PCG), a method that constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric induced from robust vision features. This geometry aligns with human perception and penalizes brittle directions, enabling smooth, on-manifold, semantically valid transitions. Experiments on three vision datasets show that PCG outperforms baselines and reveals failure modes hidden under standard metrics.

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Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta. 2026-03-01. Counterfactual Explanations on Robust Perceptual Geodesics. https://arxiv.org/abs/2601.18678

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