High Explainability
High explainability in artificial intelligence (AI) aims to make the decision-making processes of complex models, such as large language models and deep neural networks, more transparent and understandable. Current research focuses on developing both intrinsic (built-in) and post-hoc (added after training) explainability methods, often employing techniques like attention mechanisms, feature attribution, and counterfactual examples to interpret model outputs across various modalities (text, images, audio). This pursuit is crucial for building trust in AI systems, particularly in high-stakes domains like medicine and finance, and for ensuring fairness, accountability, and responsible AI development.
Papers
Gradient based Feature Attribution in Explainable AI: A Technical Review
Yongjie Wang, Tong Zhang, Xu Guo, Zhiqi Shen
A Question on the Explainability of Large Language Models and the Word-Level Univariate First-Order Plausibility Assumption
Jeremie Bogaert, Francois-Xavier Standaert
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek, Marvin N. Wright
Explainability through uncertainty: Trustworthy decision-making with neural networks
Arthur Thuy, Dries F. Benoit
Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models
Laura Fernández-Becerra, Miguel Ángel González-Santamarta, Ángel Manuel Guerrero-Higueras, Francisco Javier Rodríguez-Lera, Vicente Matellán Olivera
Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability
João Manoel Herrera Pinheiro, Marcelo Becker
What Sketch Explainability Really Means for Downstream Tasks
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Tao Xiang, Yi-Zhe Song
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity
Xiongri Shen, Zhenxi Song, Zhiguo Zhang