# Jay Alammar Publishes Explainable AI Cheat Sheet > Source: > Published: 2026-06-21 00:38:06.767371+00:00 # Jay Alammar Publishes Explainable AI Cheat Sheet Jay Alammar published the "Explainable AI Cheat Sheet" on his blog, a high-level guide to tools and methods that help humans understand AI/ML models and their predictions, per the post dated May 4, 2021. The post includes a short introductory video and invites translations and subscriptions, according to the page. The resource is presented as a concise reference for practitioners seeking an overview of explainability approaches rather than a deep methods paper. ### What happened Per Jay Alammar's blog post dated **May 4, 2021**, the **Explainable AI Cheat Sheet** is a high-level guide to the set of tools and methods that help humans understand AI/ML models and their predictions. The entry includes a brief introductory video and notes about translation and subscription on the page. ### Technical details Editorial analysis - technical context: The cheat-sheet format typically summarizes families of XAI techniques such as model-agnostic feature attribution, saliency and visualization methods, counterfactual explanations, and global versus local explanation trade-offs. For practitioners, such resources aggregate conceptual definitions, common use cases, and pointers to implementations in libraries (for example, packages that expose SHAP, LIME, or saliency-map utilities), enabling quicker selection of candidate methods for a given model class or task. ### Context and significance Editorial analysis: Concise, curated references like the Explainable AI Cheat Sheet reduce onboarding friction for engineers and data scientists who must evaluate interpretability options across projects. These guides also serve as boundary objects between technical teams and stakeholders who need understandable summaries of model behavior without deep technical immersion. ### What to watch Editorial analysis: Observers should watch for updated editions or companion notebooks that map methods to concrete code examples, integrations into MLOps toolchains that automate explainability audits, and community translations or forks that adapt the cheat sheet to domain-specific needs (healthcare, finance, fairness auditing). ## Scoring Rationale The cheat sheet is a useful, practical resource for ML practitioners but is not a novel research result. Its utility is steady but not industry-shaking; the age of the post reduces immediacy for cutting-edge work. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)