Dave Braines

Explainable AI: A Review of Machine Learning Interpretability Methods

Cellpose 2.0: how to train your own model

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

DARPA's Explainable Artificial Intelligence Program

The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process

Artificial intelligence and speedy trial in the judiciary: Myth, reality or need? A case study in the Brazilian Supreme Court (STF)

Deep Learning in Mobile and Wireless Networking: A Survey

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

A Survey of Deep Active Learning

Unlocking the value of artificial intelligence in human resource management through AI capability framework

Definitions, methods, and applications in interpretable machine learning

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Deep learning for situational understanding

Interpretability of deep learning models: A survey of results