Agile and Responsible AI Product Design
Live Registration
BATCH #24
03Seats Left
92% Booked
02
Days
:
00
Hrs
:
00
Min
:
00
Sec
Course Overview
Table of Contents
Program Syllabus
Module 1
About
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.
Module 2
Aim
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.
Lead Instructor
Dr. Sarah Chen
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.
Limited SeatsClosing Soon
Agile and Responsible AI Product Design
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 3
Program Objectives
-
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
Module 4
Program Structure
Module 5
Module 1: Introduction to Agile Product Design for AI
-
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
Module 6
Module 2: Principles of Responsible AI
-
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
Module 7
Module 3: Agile Planning and Experimentation with 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
Module 8
Module 4: Human-Centered AI Product Design
-
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
Module 9
Module 5: Governance and Responsible Delivery
-
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
Module 10
Module 6: Strategy, Roadmaps, and Capstone
-
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
Module 11
Video Module TOC
~Video content aligned with weekly modules
Module 12
Week 1 Videos
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
Module 13
Week 2 Videos
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
Module 14
Week 3 Videos
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
Module 15
Lecture 1 (Week 1)
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
Module 16
Lecture 2 (Week 2)
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
Module 17
Lecture 3 (Week 3)
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
Module 18
Intended For
-
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
Module 19
Program Outcomes
-
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
Module 21
Fee Structure
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 CurrenciesModule 22
FOR QUERIES, FEEDBACK OR ASSISTANCE
Module 23
Key Takeaways
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Module 24
Future Career Prospects
-
AI Product Design & Strategy
-
Responsible AI Governance
-
AI UX & Trustworthy System Design
-
Regulatory Technology (RegTech) for AI
Module 25
Job Opportunities
-
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
Module 27
Enter the Hall of Fame!
Take your research to the next level!
Publication Opportunity
Potentially earn a place in our coveted Hall of Fame.
Centre of Excellence
Join the esteemed Centre of Excellence.
Networking and Learning
Network with industry leaders, access ongoing learning opportunities.
Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).
Achieve excellence and solidify your reputation among the elite!
×
Module 28
![]()
![]()
![]()
![]()
Related Courses
Systems Thinking for …
AI for Waste-to-Energy Systems …
Predictive Analytics for …
Effective Data Labeling for AI …
Module 29
Machine Learning for Optimizing Lipid Nanoparticles (LNPs) in mRNA & Gene Delivery
Machine Learning for Optimizing Lipid Nanoparticles (LNPs) in mRNA & Gene Delivery
Surveillance and Data Analytics of Antimicrobial Resistance (AMR) in Public Health
View All Feedbacks
Recent Feedbacks In Other Workshops
none
Instant Access
Need Guidance?
Not sure if this course is right for you? Schedule a free 15-minute consultation with our academic advisors.