Leilani H. Gilpin

Latest

• Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
• Dynamically Introspective Action Advising
• Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMs
• Frame-Based Scenes Understanding: Structured Representations for Introspective Perception in Autonomous Driving
• Slug Mobile: Test-Bench for RL Testing
• How We Test Self-Driving Cars, and How We Explain Their Failures
• Explainable AI for Fairness and Accountability
• Accountability layers: explaining complex system failures by parts
• Can large language models explain themselves? a study of llm-generated self-explanations
• Convolutional neural network model for diabetic retinopathy feature extraction and classification
• Semi-Automated Synthesis of Driving Rules
• DANGER: A Framework of Danger-Aware Novel Dataset Generator Extension for Robustness Test of Machine Learning
• Knowledge-based commonsense reasoning and explainability
• Accountability Layers
• Accountability Layers
• DANGER: A Framework for Generating Dangerous Scenes for Testing Robustness of Machine Learning
• Explaining Errors in Complex Systems
• Explaining Errors in Autonomous Driving: A Diagnosis Tool and Testing Framework for Robust Decision Making
• Perception Challenge for Autonomous Vehicles
• Explaining Multimodal Errors in Autonomous Vehicles
• I'm Recruiting PhD Students
• Anomaly Detection Through Explanations
• Explaining Explanations in AI
• A Framework for the Design and Evaluation of Machine Learning Applications
• Identifying Multimodal Errors Through Explanations
• A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation
• Anomaly Detection Through Explanations
• Anomaly Detection Through Explanations
• Explaining Explanations
• Explanation-based Anomaly Detection
• CSAIL Student Profile
• Explaining Possible Futures for Robust Autonomous Decision-Making
• Learning From Explanations for Robust Autonomous Driving.
• An Adaptable Self-Monitoring Framework for Opaque Machines
• Monitoring Opaque Learning Systems
• Automotive FOSS Panel
• Self-Explanation and Self-Driving
• Explaining Explanations to Society
• Explaining explanations: An overview of interpretability of machine learning
• Monitoring Scene Understanders with Conceptual Primitive Decomposition and Commonsense Knowledge
• Perception of Speaker Personality Traits Using Speech Signals
• Reasonable perception: Connecting vision and language systems for validating scene descriptions
• Reasonableness Monitors
• Getting Up to Speed on Vehicle Intelligence