What’s coming up at #AAAI2026?

A new study has introduced a multimodal and explainable web application designed to detect misogyny in code-mixed Hindi and English, utilizing advanced artificial intelligence models like XLM-RoBERTa. This application aims to enhance the interpretability of hate speech detection, which is crucial in the context of increasing online misogyny.

A novel approach to explainable artificial intelligence (AI) has been proposed, leveraging Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs) to enhance decision-making transparency. This framework utilizes gradient-based saliency maps and SHAP for feature attribution, addressing the critical challenge of explainability in high-stakes domains like healthcare and finance.

Artificial intelligence (AI) is being increasingly utilized to analyze complex data across various fields, including medical imaging and materials science. However, many AI systems face challenges when real-world data diverges from ideal conditions, leading to issues with accuracy and reliability due to varying measurement qualities.