What is Explainability?Explainability kya hai?

🧸 Think of it likeAise samjho

Like a doctor who doesn't just say 'take rest', but explains 'your fever and these test results are why'.Jaise doctor sirf 'aaram karo' nahi kehta, balki samjhaata hai 'aapka bukhaar aur yeh test results iski wajah hain'.

📘 ExplainedVistaar se

In lending, healthcare and hiring, people and regulators need reasons. Tools like SHAP show which features pushed a prediction up or down; simpler models are easier to explain. For LLMs, showing sources (citations) is a practical form of explainability.Lending, healthcare aur hiring mein logon aur regulators ko wajah chahiye. SHAP jaise tools dikhate hain kaunse features ne prediction upar ya neeche kiya; simple models samjhaane mein aasaan hain. LLMs ke liye sources (citations) dikhana explainability ka practical roop hai.

💡 Real exampleAsli example

'Loan declined mainly because of 3 missed payments and high EMI to income ratio.''Loan mainly 3 miss hue payments aur zyaada EMI to income ratio ki wajah se reject hua.'

📖 Learn it step by stepStep by step seekho

• Feature engineering: turning raw data into useful inputsFeature engineering: raw data ko kaam ke inputs mein badalna
• Enterprise AI: integration, security and complianceEnterprise AI: integration, security aur compliance

🔗 Related termsJude hue shabd

• BiasWhen a model's results are systematically unfair or skewed, often because of skewed training data.Jab model ke results lagaataar ek taraf jhuke ya anyaayi hon, aksar jhuke hue training data ki wajah se.
• FeatureOne input the model uses to make a prediction, like age, income or number of logins.Ek input jo model prediction ke liye use karta hai, jaise umar, income ya logins ki ginti.