Synthetic Ground-Truth framework evaluates XAI methods
The paper introduces a synthetic ground‑truth framework for evaluating explainable AI methods. It addresses the lack of reliable evaluation procedures and the absence of ground‑truth explanations by using controlled interventions to generate synthetic datasets where the importance of input components is known by design.
Key points
- Synthetic ground truth framework uses controlled interventions to generate datasets with known feature importance
- Framework tested on binary images, tabular data, and time series
- Evaluation of nine XAI methods reveals significant limitations
The framework is instantiated across binary images, tabular data, and time series, enabling assessment in heterogeneous settings. The authors evaluated nine widely used XAI methods on these datasets, finding significant limitations in current techniques and highlighting the need for synthetic, intervention‑based benchmarks.
The work underscores that fidelity scores alone can be misleading, as different explanations can achieve similar fidelity while misrepresenting the model's decision process. By providing ground‑truth explanations aligned with model behavior, the framework offers a more reliable metric for explanation quality, which is crucial for building trustworthy AI systems.
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