RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
arXiv:2606.27948v1 Announce Type: new 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 REconstruction via Counterfactual-Aware waSserstein
The increasing complexity and opacity of AI models necessitate robust methods for auditing and understanding their decision-making processes, especially as their deployment becomes widespread.
This research offers a path towards more transparent and auditable AI systems, which is critical for trust, regulatory compliance, and responsible AI deployment across sensitive applications.
The development of more effective methods for reconstructing black-box models will enhance third-party oversight capabilities, moving beyond current limitations that hinder comprehensive AI auditing.
- · AI auditors
- · Regulatory bodies
- · Companies deploying 'black box' AI
- · Researchers in explainable AI
- · Malicious actors exploiting opaque AI
- · Companies resistant to AI transparency
- · AI systems with poor explainability
Improved methods for reconstructing black-box models will lead to more effective AI auditing tools.
Increased transparency requirements for AI models could accelerate the adoption of these reconstruction techniques across industries.
Enhanced understanding of AI decision-making may foster greater public trust in AI, potentially accelerating its integration into critical infrastructure and decision-making processes.
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Read at arXiv cs.LG