AI
Can AI essay graders be fooled? New research shows many automatic essay scoring (AES) systems reward “word soups” over real writing. Using interpretability tools, the authors found that a few keywords drive most of the score, while flow, grammar, and coherence matter far less. The wild part: because these models
UrbanPlanning
Cities need AI that can explain itself Most planning AI spots patterns in data. The next leap is AI that helps choose sites, allocate budgets, and balance trade-offs—while showing its work. This paper introduces the Agentic Urban Planning AI Framework built on new reasoning methods like Chain-of-Thought, ReAct, and
AI
Interpreting what large language models “think” is slow and expensive. Each new model often needs custom tools and lots of manual labeling. Atlas-Alignment offers a shortcut. Instead of rebuilding everything, it lines up a new model’s hidden activity with a shared, human-labeled Concept Atlas using only overlapping inputs and
ExplainableAI
Meet “Modules of Influence”: groups of features that drive AI decisions Most explainers like SHAP or LIME tell you which single features mattered for one prediction. This paper goes a step further: it builds an explanation graph across many predictions and uses community detection to reveal feature groups that act