see the blog posts and podcast of this guy "Neel Nanda"

Explainable AI and Trustworthy Machine Learning focus on transparency, fairness, and accountability in AI systems.

Hey Abu Noman Md Sakib.....

Great question..... Here’s a summary of the latest trends in Explainable AI (XAI) and Trustworthy Machine Learning (TML):

1. Latest Methods

• Post-hoc Explainability:SHAP is now more scalable, and Counterfactual Explanations focus on "what if" scenarios. Causal Explainability links cause-effect relationships for deeper insights.
• Intrinsic Models:Models like ProtoPNet use prototypes for transparency, and hybrids combine neural networks with decision trees.
• Foundation Models: Tools are emerging to explain massive models like GPT-4 (modular frameworks)

2. Evaluation Metrics

• Fidelity: How well explanations match model behavior (e.g., LIME, SHAP).
• Human-Centric: Tested with user trust scores and surveys.
• Robustness & Stability: Ensures explanations hold under perturbations and provide consistent outputs.
• Task-Specific: Customized for fields like healthcare or finance.

3. Key Papers

• "Explainable AI for Foundation Models" (2023): Explores explainability for large models.
• "Trustworthy ML: Beyond Explainability" (2024): Combines fairness, robustness, and explainability.
• "Causal Approaches to XAI" (2024): Adds causal inference to XAI methods.

Article Explainable Artificial Intelligence (XAI): What we know and ...

Chapter Current Advances, Trends and Challenges of Machine Learning ...

GPT model and Hugging face model with LLM and Lima