AI says no — but why? Use LIME, SHAP values, and counterfactuals to crack open the black box and demand explanations from any AI decision!
🎱 The Problem
🔦 LIME
📊 SHAP Values
🔄 Counterfactuals
🏆 Badge
Why Explainability Matters
🎱
The Black Box
Complex models (deep nets, random forests) make great predictions but can't explain themselves. Accuracy ≠ trustworthiness.
🔦
LIME
Local Interpretable Model-agnostic Explanations. Perturb the input slightly and see which changes flip the output.
📊
SHAP Values
SHapley Additive exPlanations. Game theory tells us each feature's exact contribution to the final prediction.
⚖️
Legal Right
EU AI Act 2024 + GDPR Article 22 give citizens a legal right to explanation for automated AI decisions.
🎱
Wizzy the AI Tutor
Imagine applying for a loan and the bank's AI says "DENIED" — with no explanation! 😠 This happens millions of times a day worldwide. The AI has 200 million parameters and no one knows why it said no. This is the black box problem. Let's crack it open!
Step 1 — The Black Box Problem
🎱 AI Loan Officer
200 million parameters · No explanation
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Adjust the applicant's profile:
The AI makes its decision but tells you nothing about why. Change the inputs — can you figure out what it cares about?
📊 Decision History
Current decision—
Approved tests0
Denied tests0
Your guessed reasonUnknown
⚖️ Real Case: In 2019, Apple Card was found to give women lower credit limits than men with identical financial profiles. The bank said "the algorithm decided" — no explanation possible.
🎱
Wizzy the AI Tutor
LIME works by poking the AI! 🔦 It creates hundreds of slightly different versions of the input (perturbing values up and down) and asks the AI each time. Then it finds which changes flipped the decision — those are the important features! Click "Perturb" to see LIME in action.
Step 2 — LIME: Perturbing the Input
// Press Run LIME Analysis to start perturbation...
Perturbation results (which changes flip the decision):
🔦 LIME Results
Run LIME to see results
LIME creates a simple, interpretable model that approximates the black box locally — around the specific input being explained.
🎱
Wizzy the AI Tutor
SHAP values use game theory! 📊 Imagine the features as players in a team. SHAP asks: "How much did each player contribute to the win (or loss)?" Positive SHAP = pushed toward approval. Negative SHAP = pushed toward denial. Every prediction can be decomposed into feature contributions!
Step 3 — SHAP Values: Feature Contributions
Base prediction + feature contributions = final score
Base: 0.50=—
📊 SHAP Summary
SHAP values sum to the difference between the actual prediction and the base rate prediction.
Game Theory: SHAP is based on Shapley values from cooperative game theory. It's the only method that satisfies efficiency, symmetry, dummy, and additivity axioms.
🎱
Wizzy the AI Tutor
Counterfactuals answer: "What would have to change for the AI to say YES?" 🔄 This is the most human-friendly explanation — it gives actionable advice. "If your credit score was 50 points higher, you'd be approved." Click Generate Counterfactual to find the minimum change needed!
Step 4 — Counterfactual Explanations
❌ Original Application — DENIED
Counterfactuals find the minimum-edit version of the input that flips the decision. They give actionable, human-understandable advice.
🔄 Counterfactual Paths
EU AI Act Article 86: High-risk AI systems must provide "meaningful information about the logic involved, as well as the significance and the envisaged consequences" of automated decisions.
🎱
Wizzy the AI Tutor
🎊 You can now explain any AI decision! You understand LIME (local perturbation), SHAP (feature attribution via game theory), and counterfactuals (minimum-edit explanations). These are the tools AI engineers use every day to make AI trustworthy!
🎱
XAI Expert Badge!
You mastered Explainable AI — LIME, SHAP, and counterfactuals!
Optional. Stays on this device only — not sent to WhizzStep.
🎱 WhizzStep AI Lab
Activity completion card for
Student Name
has explored Explainable AI — LIME, SHAP & Counterfactuals
This records completion of a browser activity only. It is not an accredited certificate or proof of mastery.
XAI Expert
SHAP Master
AI Auditor
whizzstep.in
Key Concepts Mastered
Black Box
🎱 The Problem
Complex models like deep nets can't explain their own decisions. High accuracy comes at the cost of interpretability.
LIME
🔦 Local Explanation
Perturb the input, observe output changes, fit a simple interpretable model locally. Works on any ML model — model-agnostic.
SHAP
📊 Feature Attribution
Shapley values from game theory. Distributes the "credit" for a prediction fairly across all input features.
Counterfactual
🔄 Actionable Advice
"If X was different, the decision would change." The minimum edit needed to flip the output. Human-friendly and actionable.
EU AI Act
⚖️ Legal Requirement
High-risk AI (loans, hiring, justice) must explain decisions. XAI is now a legal requirement in the EU, not a nice-to-have.
Trust
🤝 Why It Matters
Humans won't use AI they don't trust. Explanations build trust — and catch bias and errors before they cause harm.
About this lab
AI-05AI Literacy and JudgementClasses 8-1015 minFoundation
Learning objective: Probe a hidden simplified model with test inputs to see how much its reasoning can, and cannot, be inferred from outputs alone.
What this simplifies: The model inside is intentionally simple; real black-box systems are far larger and harder to interpret.
Privacy: No learner input leaves the device.
Teacher prompt: Ask the class why this simulation might mislead someone who takes it too literally.
Reflect: What is one thing this activity showed you that you did not expect?
