Model-Free Counterfactual Subset Selection at Scale
This work addresses the need for real-time, unbiased explanations in AI systems, offering a streaming solution that avoids synthetic data pitfalls, though it is incremental in improving existing counterfactual techniques.
The paper tackles the problem of generating interpretable counterfactual explanations for AI decisions by introducing a scalable, model-free method that selects diverse examples directly from observed data, achieving O(log k) update complexity and demonstrating superior performance over baselines in empirical evaluations.
Ensuring transparency in AI decision-making requires interpretable explanations, particularly at the instance level. Counterfactual explanations are a powerful tool for this purpose, but existing techniques frequently depend on synthetic examples, introducing biases from unrealistic assumptions, flawed models, or skewed data. Many methods also assume full dataset availability, an impractical constraint in real-time environments where data flows continuously. In contrast, streaming explanations offer adaptive, real-time insights without requiring persistent storage of the entire dataset. This work introduces a scalable, model-free approach to selecting diverse and relevant counterfactual examples directly from observed data. Our algorithm operates efficiently in streaming settings, maintaining $O(\log k)$ update complexity per item while ensuring high-quality counterfactual selection. Empirical evaluations on both real-world and synthetic datasets demonstrate superior performance over baseline methods, with robust behavior even under adversarial conditions.