arXiv:2407.12950v1 Announce Type: new
Abstract: We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustworthy, similar inputs should yield similar explanations, reflecting a consistent semantic understanding. By leveraging XAI techniques, we assess semantic continuity in the task of image recognition. We conduct experiments to observe how incremental changes in input affect the explanations provided by different XAI methods. Through this approach, we aim to evaluate the models’ capability to generalize and abstract semantic concepts accurately and to evaluate different XAI methods in correctly capturing the model behaviour. This paper contributes to the broader discourse on AI interpretability by proposing a quantitative measure for semantic continuity for XAI methods, offering insights into the models’ and explainers’ internal reasoning processes, and promoting more reliable and transparent AI systems.
Introducing a Novel Metric for Semantic Continuity in Explainable AI
This study presents a novel metric that aims to measure semantic continuity in Explainable AI (XAI) methods and machine learning models. The authors argue that for models to be truly interpretable and trustworthy, they should consistently provide similar explanations for similar inputs, indicating a consistent semantic understanding.
The multi-disciplinary nature of this concept is evident as it requires expertise in both AI and linguistics. The assessment of semantic continuity involves evaluating the models’ capability to generalize and abstract semantic concepts accurately, which draws on the fields of semantics and natural language processing.
To assess semantic continuity, the researchers leverage XAI techniques in the task of image recognition. They conduct experiments where they introduce incremental changes in input and observe how different XAI methods provide explanations in response to these changes. This approach allows them to evaluate the models’ ability to generalize and abstract semantic concepts accurately.
The study also aims to evaluate different XAI methods in correctly capturing the model behavior. This evaluation of XAI methods involves analyzing their internal reasoning processes, which requires expertise in explainable AI and model interpretability techniques.
Contributions to AI Interpretability and Transparency
This paper makes an important contribution to the broader discourse on AI interpretability. By proposing a quantitative measure for semantic continuity for XAI methods, the authors provide a way to assess the consistency and reliability of AI models in their interpretation of similar inputs. This metric can help researchers and developers ensure that AI systems produce accurate and trustworthy explanations.
Furthermore, the study offers insights into the internal reasoning processes of both the models and the explainers. By analyzing the explanations provided by different XAI methods, researchers can gain a better understanding of how these methods capture and represent the model behavior. This understanding can lead to improvements in XAI techniques and help researchers design more reliable and transparent AI systems.
In conclusion, this study highlights the importance of semantic continuity in XAI methods and machine learning models. By introducing a novel metric and conducting experiments in the field of image recognition, the authors contribute to the advancement of AI interpretability, transparency, and the development of more reliable AI systems.