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Evaluating Models and their Explanations

MBZUAI · Invalid Date

This article discusses the increasing concerns about the interpretability of large deep learning models. It highlights a talk by Danish Pruthi, an Assistant Professor at the Indian Institute of Science (IISc), Bangalore, who presented a framework to quantify the value of explanations and the need for holistic model evaluation. Pruthi's talk touched on geographically representative artifacts from text-to-image models and how well conversational LLMs challenge false assumptions. Why it matters: Addressing interpretability and evaluation is crucial for building trustworthy and reliable AI systems, particularly in sensitive applications within the Middle East and globally.

Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings

arXiv · Jan 3

This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.