ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings

LIME explanations become unreliable with limited data: The paper identifies a critical problem where LIME, a popular tool for explaining AI decisions, generates unrealistic variations when training data is scarce, causing it to misrepresent how the model actually makes decisions.

Transfer learning improves explanation trustworthiness: ITL-LIME addresses this by borrowing real examples from related datasets rather than creating artificial ones, resulting in more stable and accurate explanations—an important consideration for organizations deploying AI in data-limited domains like healthcare or specialized industries.

Real examples outperform random variations: Instead of randomly perturbing data to understand model behavior, the method retrieves actual instances from similar domains, which the paper demonstrates produces more faithful approximations of the model's decision boundaries.

Stability matters for responsible AI deployment: The research emphasizes that explanations shouldn't fluctuate unpredictably—consistent, reliable explanations are essential for building trust in AI systems and meeting regulatory requirements for model interpretability.

Practical solution for resource-constrained settings: This approach is particularly relevant for organizations that cannot collect large amounts of labeled data, enabling them to still provide meaningful explanations of their AI systems' decisions to stakeholders and regulators.