Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals

Artificial intelligence (AI) and machine learning (ML) impact our lives in many ways. From mundane tasks to critical decision-making processes, AI's role is becoming more central. As a result, the need for transparency and interpretability of these systems is growing. This is why we need to field of Expianable AI (XAI) also known as interpretable machine learning (IML). We will take a brief look at what XAI aims to achieve and the various approaches at our disposal. These include intrinsically interpretable models, model agnostic methods, causal models, counterfactuals, adversarial examples and non-agnostic methods. 🚀 Free Course 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-... SHAP course: https://adataodyssey.com/courses/shap... 🚀 Companion Article (no-paywall link): 🚀   / what-is-interpretable-machine-learning   🚀 Useful playlists 🚀 XAI:    • Explainable AI (XAI)   SHAP:    • SHAP   Algorithm fairness:    • Algorithm Fairness   🚀 Get in touch 🚀 Medium:   / conorosullyds   Threads: https://www.threads.net/@conorosullyds Twitter:   / conorosullyds   Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 02:15 What is XAI? 03:16 Inrinsically interpretable models 05:14 Black-box models 06:38 Model agnostic methods 07:52 Causal models 08:31 Counterfactual explanations 09:14 Adversarial examples 10:03 Model-specific methods 10:54 A note on terminology