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Explainable AI for Industry is a specialized training program focused on interpreting and understanding the decision-making processes of AI/ML models. As AI adoption accelerates across sectors, industries demand more interpretable, fair, and auditable systems. This course addresses the “black box” problem by teaching frameworks, algorithms, and best practices for integrating explainability into AI workflows—helping professionals enhance trust, meet compliance standards, and make responsible AI-driven decisions.
To equip professionals with the knowledge and tools to implement Explainable AI (XAI) techniques, enabling transparency, trust, and regulatory compliance in real-world industrial applications.
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Professional Certification Program
To promote transparent and responsible AI in critical sectors
To bridge the gap between model performance and stakeholder trust
To enable organizations to meet legal and ethical standards
To integrate explainability as a core part of AI system development
Chapter 1.1: What is Explainable AI?
Chapter 1.2: Why Explainability Matters in Industry
Chapter 1.3: Regulatory and Ethical Drivers (GDPR, AI Act)
Chapter 2.1: Model-Specific vs. Model-Agnostic Techniques
Chapter 2.2: Global vs. Local Explanations
Chapter 2.3: Common Techniques: SHAP, LIME, Anchors, Partial Dependence
Chapter 3.1: Integrating SHAP in Tree-based Models
Chapter 3.2: Using LIME for Text and Image Classification
Chapter 3.3: Visualizing Feature Importance and Decision Boundaries
Chapter 3.4: Performance vs. Interpretability Trade-Offs
Chapter 4.1: Overview of XAI Libraries (SHAP, LIME, ELI5, Captum)
Chapter 4.2: Explainability in Deep Learning with Captum
Chapter 4.3: Explainability in Production with MLflow and dashboards
Chapter 4.4: Case: Interpreting Drift and Model Updates in Production
Chapter 5.1: Financial Services – Credit Risk, Fraud Detection
Chapter 5.2: Healthcare – Diagnostics, Clinical Decision Support
Chapter 5.3: Manufacturing – Predictive Maintenance, QA Automation
Chapter 6.1: Model Auditing and Documentation Practices
Chapter 6.2: Explaining AI to Non-Technical Stakeholders
Chapter 6.3: Building Trustworthy AI Pipelines
Chapter 6.4: Capstone – Develop an XAI Dashboard for Business Decision Makers
Introduction to Explainable AI (15 min)
History and Motivation for XAI (30 min)
Global vs. Local Explanations (30 min)
Model-Specific vs. Model-Agnostic Methods (30 min)
SHAP and LIME Basics (30 min)
Anchors, PDP, ICE Plots Overview (30 min)
Ethics, Bias, and Compliance in AI (45 min)
GDPR and AI Act: Regulatory Impacts on ML Pipelines (45 min)
Visualizing Feature Importance (30 min)
SHAP in Tree Models (Hands-On) (45 min)
LIME for Text Classification (Hands-On) (45 min)
XAI for Image Classification with Grad-CAM & LIME (45 min)
Using Captum for Deep Learning Models (30 min)
Trade-Offs: Performance vs. Interpretability (30 min)
Production Tools: MLflow, ELI5, Explainability Dashboards (45 min)
Monitoring for Model Drift and Explanation Validity (45 min)
Interpretable Pipelines for Regulated Environments (30 min)
Healthcare XAI: Clinical Use Case Deep Dive (45 min)
Manufacturing XAI: QA and Maintenance Models (30 min)
Communicating AI Decisions to Executives (30 min)
Creating an XAI Report for Audit and Stakeholders (45 min)
Capstone Guide: Building an XAI Dashboard (60 min)
Capstone Showcase Examples (30 min)
Final Review and Next Steps in Responsible AI (30 min)
Future Trends: Glass-Box Models and Causal Explainability (15 min)
Title: Beyond the Black Box: The Why and How of Explainable AI
Duration: 60 minutes
Focus: Fundamentals of XAI, business drivers, and regulatory expectations
Guest: AI Ethicist / Responsible AI Lead
Interactive: Live group activity using SHAP and LIME visualizations on a simple ML model
Title: Interpretable Models in Practice: From Prediction to Justification
Duration: 75 minutes
Focus: Practical integration of explainability tools into ML workflows
Guest: Machine Learning Engineer / XAI Researcher
Interactive: Model debugging session with SHAP, LIME, and Captum applied to tabular and image data
Title: Explainability in Production: Communicating AI to Stakeholders
Duration: 90 minutes
Focus: Real-world deployment, stakeholder communication, and compliance documentation
Guest Panel: Data Scientist + UX Designer + Policy Analyst
Interactive: Case-based presentation critique + live Q&A on explainability reports and dashboards
Data scientists, ML/AI engineers, domain experts, and analytics professionals
Industry leaders and managers responsible for AI projects
Compliance, ethics, or governance officers in tech-driven organizations
Prior understanding of machine learning concepts is recommended
By the end of this program, learners will:
Understand and apply core XAI algorithms and visualization tools
Evaluate and improve the interpretability of ML models
Address ethical, regulatory, and trust challenges in AI deployments
Communicate AI decisions effectively across technical and business teams
Fee: INR 21499 USD 249
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of Currencies
This program enhances readiness for high-impact roles such as:
AI Governance Lead
Responsible AI Strategist
AI Compliance Officer
ML Interpretability Researcher
Ethical AI Consultan
Explainable AI Engineer
AI Auditor or Policy Analyst
ML Researcher in Trustworthy AI
Data Scientist (with XAI expertise)
AI Product Owner for Regulated Industries
Risk Analyst in FinTech or MedTech AI
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