Saved in:
| Main Author: | Tempest-Walters, Kit |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15783 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards physician-centered oversight of conversational diagnostic AI
by: Vedadi, Elahe, et al.
Published: (2025)
by: Vedadi, Elahe, et al.
Published: (2025)
Predicting life satisfaction using machine learning and explainable AI
by: Khan, Alif Elham, et al.
Published: (2025)
by: Khan, Alif Elham, et al.
Published: (2025)
Monitoring fairness in machine learning models that predict patient mortality in the ICU
by: van Schaik, Tempest A., et al.
Published: (2024)
by: van Schaik, Tempest A., et al.
Published: (2024)
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
by: Bobek, Szymon, et al.
Published: (2024)
by: Bobek, Szymon, et al.
Published: (2024)
Regulating AI Adaptation: An Analysis of AI Medical Device Updates
by: Wu, Kevin, et al.
Published: (2024)
by: Wu, Kevin, et al.
Published: (2024)
BACON: A fully explainable AI model with graded logic for decision making problems
by: Bai, Haishi, et al.
Published: (2025)
by: Bai, Haishi, et al.
Published: (2025)
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
by: Wijk, Hjalmar, et al.
Published: (2024)
by: Wijk, Hjalmar, et al.
Published: (2024)
Dataset resulting from the user study on comprehensibility of explainable AI algorithms
by: Bobek, Szymon, et al.
Published: (2024)
by: Bobek, Szymon, et al.
Published: (2024)
Can I trust my anomaly detection system? A case study based on explainable AI
by: Rashid, Muhammad, et al.
Published: (2024)
by: Rashid, Muhammad, et al.
Published: (2024)
On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists
by: Kim, Seungone, et al.
Published: (2026)
by: Kim, Seungone, et al.
Published: (2026)
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
by: Punzo, Samuele, et al.
Published: (2025)
by: Punzo, Samuele, et al.
Published: (2025)
Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance
by: Ruffle, James K, et al.
Published: (2025)
by: Ruffle, James K, et al.
Published: (2025)
Capturing waste collection planning expert knowledge in a fitness function through preference learning
by: Díaz, Laura Fernández, et al.
Published: (2024)
by: Díaz, Laura Fernández, et al.
Published: (2024)
EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification
by: Showrav, Tushar Talukder, et al.
Published: (2025)
by: Showrav, Tushar Talukder, et al.
Published: (2025)
PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruning
by: García-Sigüenza, Javier, et al.
Published: (2025)
by: García-Sigüenza, Javier, et al.
Published: (2025)
Self-Improving AI Agents through Self-Play
by: Chojecki, Przemyslaw
Published: (2025)
by: Chojecki, Przemyslaw
Published: (2025)
Secret mixtures of experts inside your LLM
by: Boix-Adsera, Enric
Published: (2025)
by: Boix-Adsera, Enric
Published: (2025)
Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Study
by: Walters, Michael, et al.
Published: (2025)
by: Walters, Michael, et al.
Published: (2025)
Counterfactual explainability and analysis of variance
by: Gao, Zijun, et al.
Published: (2024)
by: Gao, Zijun, et al.
Published: (2024)
Symmetry in Neural Network Parameter Spaces
by: Zhao, Bo, et al.
Published: (2025)
by: Zhao, Bo, et al.
Published: (2025)
NeuroAI for AI Safety
by: Mineault, Patrick, et al.
Published: (2024)
by: Mineault, Patrick, et al.
Published: (2024)
Open Character Training: Shaping the Persona of AI Assistants through Constitutional AI
by: Maiya, Sharan, et al.
Published: (2025)
by: Maiya, Sharan, et al.
Published: (2025)
GLAI: GreenLightningAI for Accelerated Training through Knowledge Decoupling
by: Mestre, Jose I., et al.
Published: (2025)
by: Mestre, Jose I., et al.
Published: (2025)
AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines
by: Abbas, Saram, et al.
Published: (2025)
by: Abbas, Saram, et al.
Published: (2025)
Class-specific feature selection for classification explainability
by: Aguilar-Ruiz, Jesus S.
Published: (2024)
by: Aguilar-Ruiz, Jesus S.
Published: (2024)
The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts
by: Boix-Adsera, Enric, et al.
Published: (2025)
by: Boix-Adsera, Enric, et al.
Published: (2025)
From Generative AI to Innovative AI: An Evolutionary Roadmap
by: Mohammadabadi, Seyed Mahmoud Sajjadi
Published: (2025)
by: Mohammadabadi, Seyed Mahmoud Sajjadi
Published: (2025)
Everyday AR through AI-in-the-Loop
by: Suzuki, Ryo, et al.
Published: (2024)
by: Suzuki, Ryo, et al.
Published: (2024)
When AI Eats Itself: On the Caveats of AI Autophagy
by: Xing, Xiaodan, et al.
Published: (2024)
by: Xing, Xiaodan, et al.
Published: (2024)
Boosting Embodied AI Agents through Perception-Generation Disaggregation and Asynchronous Pipeline Execution
by: Zhang, Shulai, et al.
Published: (2025)
by: Zhang, Shulai, et al.
Published: (2025)
STRIDE: A Systematic Framework for Selecting AI Modalities -- Agentic AI, AI Assistants, or LLM Calls
by: Asthana, Shubhi, et al.
Published: (2025)
by: Asthana, Shubhi, et al.
Published: (2025)
GQVis: A Dataset of Genomics Data Questions and Visualizations for Generative AI
by: Walters, Skylar Sargent, et al.
Published: (2025)
by: Walters, Skylar Sargent, et al.
Published: (2025)
Graders should cheat: privileged information enables expert-level automated evaluations
by: Zhou, Jin Peng, et al.
Published: (2025)
by: Zhou, Jin Peng, et al.
Published: (2025)
Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution
by: Park, Jongseok, et al.
Published: (2026)
by: Park, Jongseok, et al.
Published: (2026)
Holistic Artificial Intelligence in Medicine; improved performance and explainability
by: Petridis, Periklis, et al.
Published: (2025)
by: Petridis, Periklis, et al.
Published: (2025)
DTOR: Decision Tree Outlier Regressor to explain anomalies
by: Crupi, Riccardo, et al.
Published: (2024)
by: Crupi, Riccardo, et al.
Published: (2024)
Learning the greatest common divisor: explaining transformer predictions
by: Charton, François
Published: (2023)
by: Charton, François
Published: (2023)
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
by: Liu, Zexi, et al.
Published: (2025)
by: Liu, Zexi, et al.
Published: (2025)
Scalable AI Inference: Performance Analysis and Optimization of AI Model Serving
by: Pham, Hung Cuong, et al.
Published: (2026)
by: Pham, Hung Cuong, et al.
Published: (2026)
Games for AI Control: Models of Safety Evaluations of AI Deployment Protocols
by: Griffin, Charlie, et al.
Published: (2024)
by: Griffin, Charlie, et al.
Published: (2024)
Similar Items
-
Towards physician-centered oversight of conversational diagnostic AI
by: Vedadi, Elahe, et al.
Published: (2025) -
Predicting life satisfaction using machine learning and explainable AI
by: Khan, Alif Elham, et al.
Published: (2025) -
Monitoring fairness in machine learning models that predict patient mortality in the ICU
by: van Schaik, Tempest A., et al.
Published: (2024) -
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
by: Bobek, Szymon, et al.
Published: (2024) -
Regulating AI Adaptation: An Analysis of AI Medical Device Updates
by: Wu, Kevin, et al.
Published: (2024)