Explainable AI (XAI) refers to the concept of designing AI systems that can provide understandable explanations for their decision-making processes. It aims to demystify the "black box" nature of complex AI algorithms and models, increasing transparency and accountability. XAI techniques, such as rule-based systems or visualization tools, enable users to comprehend how and why AI systems arrive at specific predictions or recommendations. By fostering trust, Explainable AI encourages adoption and acceptance of AI technologies in critical domains such as healthcare and finance, where interpretability is crucial. It also facilitates human-AI collaboration, allowing users to validate and refine AI models for improved performance and fairness. 🧠🔍💡
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Harvard | - | Explainable Artificial Intelligence | Spring 2023 | ⭐ | Course Website |
| Kaggle | Machine Learning Explainability | Extract human-understandable insights from any model. | URL | ||
| Stanford | Workshop ML Explainability by Professor Hima Lakkaraju | N/A | ⭐⭐ | Youtube | |
| Harvard | Explainable AI - From simple predictors to Complex Generative Models | N/A | ⭐⭐ | Website |
- "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" by Cynthia Rudin who won a $1 million AI price : https://arxiv.org/abs/1811.10154
- 21 feature Importance methods and packages: by Theophano Mitsa, Ph.D.
- Explain your model with SHAP values
- AI Stories podcast with Christoph Molnar, expert in the field
- Super data science podcast by Jon Krohn with Serg Masi
- TWIML AI podcast by Sam Charrington with Been Kim, Research Scientist at Google DeepMind