7 papers
CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
Sneha Rao, Shaina Raza, Dhanesh Ramachandram
Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only parti…
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
Dhanesh Ramachandram
Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks, thou…
From Features to Actions: Explainability in Traditional and Agentic AI Systems
Sindhuja Chaduvula, Jessee Ho, Kina Kim +6
Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed…
Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario
Dhanesh Ramachandram, Anne Loefler, Surain Roberts +5
Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learni…
CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
Dhanesh Ramachandram, Ananya Raval
Competing risks are crucial considerations in survival modelling, particularly in healthcare domains where patients may experience multiple distinct event types. We propose CRISP-N…
Transparent AI: The Case for Interpretability and Explainability
Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu +3
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation.…