Improving Robustness Estimates in Natural Language Explainable AI though Synonymity Weighted Similarity Measures
This work addresses the reliability of explanations in XAI, which is crucial for users and developers of AI systems, but it is incremental as it modifies existing measures rather than introducing a new paradigm.
The paper tackles the problem of unreliable robustness estimates in natural language explainable AI (XAI) by showing that standard similarity measures are poorly suited for adversarial examples and amending them with synonymity weighting, resulting in more accurate estimates of XAI weaknesses.
Explainable AI (XAI) has seen a surge in recent interest with the proliferation of powerful but intractable black-box models. Moreover, XAI has come under fire for techniques that may not offer reliable explanations. As many of the methods in XAI are themselves models, adversarial examples have been prominent in the literature surrounding the effectiveness of XAI, with the objective of these examples being to alter the explanation while maintaining the output of the original model. For explanations in natural language, it is natural to use measures found in the domain of information retrieval for use with ranked lists to guide the adversarial XAI process. We show that the standard implementation of these measures are poorly suited for the comparison of explanations in adversarial XAI and amend them by using information that is discarded, the synonymity of perturbed words. This synonymity weighting produces more accurate estimates of the actual weakness of XAI methods to adversarial examples.