Explainable AI (XAI)

Explainable AI (XAI) helps humans to understand the outputs from AI models and comes handy where understanding AI decisions become critical. XAI is transforming industries such as healthcare, finance and legal systems by providing transparency and interpretability to AI models. This advancement allows us to understand the decision-making process, ultimately boosting trust and accountability.

In biomedical research, the field of XAI is important for improving the interpretability of deep learning models in medical imaging. The continuous advancements in XAI techniques provide deep insights, ultimately maintaining optimal model performance. I came across an interesting abstract in PubMed on explanation strategies in humans vs current XAI.

In this context, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) techniques helps us to understand the working of the models and decision-making. There are additional libraries for AI explainability.

This field will continue to evolve, increasing trust and transparency in AI systems.

Disclaimer: All views expressed by Santosh are personal and?should not be considered as attributable to his employer.