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Agile and Responsible AI Product Design is a cutting-edge program that blends agile product development strategies with ethical AI design principles. As AI becomes embedded in products and services across sectors, it is crucial not only to build fast—but also to build responsibly. This program equips learners to manage AI product lifecycles iteratively while ensuring fairness, accountability, transparency, and safety in AI-enabled solutions. Ideal for product managers, designers, engineers, and innovation leaders.
To empower professionals with the skills to design, develop, and deploy AI-powered products using agile methodologies—while ensuring ethical, transparent, inclusive, and human-centered AI design practices.
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 integrate ethical, transparent AI practices with agile innovation
To equip teams with actionable strategies for AI product delivery
To reduce the risks of AI misuse or unintended consequences
To prepare participants for leadership in responsible AI innovation
Chapter 1.1: Overview of Agile in AI Product Development
Chapter 1.2: Why AI Requires a Different Agile Mindset
Chapter 1.3: Agile Frameworks Adapted for AI: Scrum vs. Kanban vs. CRISP-DM
Chapter 2.1: Fairness, Accountability, Transparency, and Ethics (FATE)
Chapter 2.2: Guidelines from OECD, IEEE, and EU AI Act
Chapter 2.3: Common Failures in AI Ethics and Their Impact
Chapter 2.4: The PM’s Role in Ensuring Responsible AI
Chapter 3.1: Iterative Learning with AI: Data Loops and Feedback
Chapter 3.2: User Story Mapping for AI Features
Chapter 3.3: Defining Acceptance Criteria for Non-Deterministic Systems
Chapter 3.4: Designing Ethical Sprint Cycles
Chapter 4.1: Inclusive Design in AI Products
Chapter 4.2: Explainability and User Trust in AI Interfaces
Chapter 4.3: Human-in-the-Loop Systems
Chapter 4.4: Testing AI Products with Real Users
Chapter 5.1: Risk Management and Model Governance
Chapter 5.2: Data Provenance, Consent, and Privacy Compliance
Chapter 5.3: Documentation: Model Cards, Data Sheets, Fact Sheets
Chapter 5.4: Monitoring and Retraining as Agile Extensions
Chapter 6.1: Product Roadmapping with Responsible AI Principles
Chapter 6.2: Communicating Ethical Impact to Stakeholders
Chapter 6.3: Capstone Project – Design a Responsible AI Feature
Chapter 6.4: Review, Peer Critique, and Final Presentation
~Video content aligned with weekly modules
Theme: Foundations of Agile and Responsible AI
Introduction to Agile for AI Product Design
Traditional Agile vs. Agile for AI Systems
CRISP-DM, Scrum, and Lean Applied to ML
What Is Responsible AI? Key Principles and Risks
FATE: Fairness, Accountability, Transparency, Ethics
Ethical Failures in Real-World AI Products
The AI PM’s Responsibility in Ethical Design
Global Standards: OECD, IEEE, and EU AI Act
Week 1 Summary and Reflection Exercise
Theme: Human-Centered and Iterative AI Product Design
Iteration and Learning in AI: Feedback Loops and Risk
Designing AI User Stories and Backlogs
Creating Acceptance Criteria for AI Features
UX in Responsible AI: Building Trust Through Transparency
Explainability and Model Behavior at the Interface
Human-in-the-Loop System Design
Prototyping with Ethical Guidelines in Mind
Case Study: Redesigning a Biased AI Recommendation Tool
Week 2 Project Demo: Ethical Sprint Plan Walkthrough
Theme: Governance, Delivery, and Strategy
AI Product Governance: What to Track and Why
Risk Assessments for AI Features
Model Cards, Data Sheets, and Transparency Docs
Monitoring and Auditing AI in Production
Roadmapping with Responsible AI Metrics
Stakeholder Communication and Ethical Alignment
Capstone Project Overview and Examples
Peer Feedback and Presentation Tips
Closing Reflections and Responsible AI in Your Career
Title: Rethinking Agile for AI: Iterating with Uncertainty
Duration: 60 minutes
Focus: How to adapt agile principles to unpredictable AI behavior and data-driven development
Guest: AI Product Lead / Agile Coach with ML experience
Interactive: Live backlog design session for a predictive model feature
Title: Designing for Trust: Responsible AI in Everyday Products
Duration: 75 minutes
Focus: Practical UX and system design for explainability, fairness, and inclusivity
Guest: AI Ethicist + UX Researcher
Interactive: Group critique of real AI interfaces and wireframes
Title: Governance Meets Delivery: Scaling AI with Accountability
Duration: 90 minutes
Focus: How to implement model governance, documentation, and risk frameworks at scale
Guest Panel: Responsible AI Advisor + MLOps Engineer + Legal/Compliance Officer
Interactive: Capstone review panel with live Q&A and feedback on student AI design projects
Product managers, UX designers, AI/ML developers, innovation leads
Policy professionals, tech ethicists, or those working on AI implementation
No coding required, but basic familiarity with AI concepts is beneficial
Master agile development processes tailored for AI systems
Implement ethical design frameworks in AI product workflows
Bridge technical development with user trust and societal value
Build prototypes and iterate with agile, human-centered loops
Ensure compliance, safety, and inclusiveness in AI deployment
Fee: INR 21499 USD 249
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AI Product Design & Strategy
Responsible AI Governance
AI UX & Trustworthy System Design
Regulatory Technology (RegTech) for AI
AI Product Manager (Responsible AI)
Trustworthy AI Design Specialist
Ethical AI Policy Advisor
Responsible AI Officer (Tech Companies & Startups)
Agile Lead for AI Programs
AI Governance Analyst
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