conftrace_
← Core AI
Artificial Intelligence › Core AI ›

Interpretability

7,318 papers

Papers per year

Papers

Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties AAAI 2024
U-trustworthy Models. Reliability, Competence, and Confidence in Decision-Making AAAI 2024
Backward Responsibility in Transition Systems Using General Power Indices AAAI 2024
An Interpretable Approach to the Solutions of High-Dimensional Partial Differential Equations AAAI 2024
Paths, Proofs, and Perfection: Developing a Human-Interpretable Proof System for Constrained Shortest Paths AAAI 2024
A Framework for Data-Driven Explainability in Mathematical Optimization AAAI 2024
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods AAAI 2024
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction AAAI 2024
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning AAAI 2024
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space AAAI 2024
π-Light: Programmatic Interpretable Reinforcement Learning for Resource-Limited Traffic Signal Control AAAI 2024
On the Concept Trustworthiness in Concept Bottleneck Models AAAI 2024
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks Using Bernstein Polynomial Activations and Precise Bound Propagation AAAI 2024
Exponent Relaxation of Polynomial Zonotopes and Its Applications in Formal Neural Network Verification AAAI 2024
Promoting Counterfactual Robustness through Diversity AAAI 2024
Q-SENN: Quantized Self-Explaining Neural Networks AAAI 2024
Robust Stochastic Graph Generator for Counterfactual Explanations AAAI 2024
Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance AAAI 2024
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise AAAI 2024
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations AAAI 2024
Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention AAAI 2024
Identifying Reasons for Bias: An Argumentation-Based Approach AAAI 2024
Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks AAAI 2024
LR-XFL: Logical Reasoning-Based Explainable Federated Learning AAAI 2024
UMA: Facilitating Backdoor Scanning via Unlearning-Based Model Ablation AAAI 2024