Superheros of Product Discovery: The Product Discovery Canvas and Gen AI

Get ready to embark on a product discovery journey like never before! In 2015, I open-sourced the Product Discovery Canvas (PDC), a one-page document that draws on the operational principles of the Business Model Canvas and Lean Canvas, incorporating product innovation insights from the thought leadership of David Hussman, Jeff Patton, Alberto Savoia, and Eric Ries. But guess what? The PDC has evolved, bringing along some AI superheroes!

Picture this: Gen AI and the PDC joining forces, like Batman and Robin, to save the day for product discovery. They're the dynamic duo of ideation with context-aware dialogue and multimodal-aware wizardry. And let's not forget about the Retrieval Augmented Generation MindMeld - it's like reading minds! This isn't old-school product brainstorming; this is the future! So buckle up and get ready to discover products like never before with this unstoppable team.

Let’s revolutionize your product ideation – because why settle for ordinary when you can have SUPER extraordinary? Bring your A-game, your product team, product ideas, and, of course, your trusty laptop or tablet. We're diving deep into Collaborative Product Chartering, Story Mapping, Pretotyping, and Lean Startup. It's not just a workshop; it's a playground for innovation.

Target Audience

This workshop is suitable for every member of an agile team , from learners to experts, of product owners, developers, business analysts, managers, developers, and quality assurance that are interested in communicating product needs more effectively.

Leveraging Gen AI in Agile Teams: Becoming High Performing

Artificial Intelligence (AI) is seen as a transformative opportunity in the fast-changing world of technology. This workshop will explore the transformative impact of Generative AI on various aspects of agile team operations:

From the Developers' perspective, we'll examine how Gen AI can revolutionize coding practices. Imagine having an intelligent assistant that helps you code and test more effectively to optimize your development workflow.

For Product Owners and Dev Managers, Gen AI's ability to analyze complex requirements and manage traceability between code, tests, and requirements will be a key focus area.

Scrum Masters aren't left out. We'll explore how Gen AI can assist in creating Collaborative Charters, User Story Maps, and Behavior-Driven Development scenarios to orchestrate dynamic understandings for the team.

This workshop includes role-based breakout sessions where participants will use a GPT, digital whiteboard, and an AI code editor to perform everyday agile team tasks. No prior AI experience is required - participants need only bring their enthusiasm and a phone, tablet, or laptop to participate in guided exercises that provide immediate, practical value for their agile teams.

Join us for this transformative opportunity to discover how Gen AI can empower your agile team to become High-Performing.

Target Audience

This talk is ideal for anyone at the intersection of Agile and AI.

Learning Outcomes

By attending this talk, you will gain the following insights:

1) Utilize Generative AI to Enhance Coding and Testing Practices
• Attendees will learn how developers can integrate Gen AI tools, such as Cursor or bolt.new, to enhance coding efficiency, improve testing processes, and optimize overall workflows.

2) Enhance Requirement Analysis and Traceability with Gen AI
• Participants will discover methods for Product Owners and Dev Managers to utilize custom chatGPT prompts to analyze complex requirements and maintain robust traceability between code, tests, and requirements.

3) Leverage AI for Advanced Agile Facilitation Techniques
• Scrum Masters will gain insights into using the Atlassian AI Scrum Assistant to create Collaborative Charters, develop User Story Maps, and craft Behavior-Driven Development scenarios.

4) Identify and Mitigate Challenges of Implementing Gen AI in Agile Frameworks
• Participants will be equipped to recognize potential obstacles and learn effective approaches to overcome them.

Time Breakdown for 90-minute workshop

Introduction and Objectives (5 minutes)
• Brief introduction to Generative AI and its relevance to Agile teams
• Workshop objectives and outcomes

Scrum Masters' Role (25 minutes)
• Group exercise exercise: Use Atlassian AI Scrum Assistant and Miro to create Collaborative Charters, User Story Maps and Behavior-Driven Development scenarios for a simple web app.

Developers' Perspective (15 minutes)
• Group exercise: Use bolt.new to generate code, test, and deploy a simple web app.

Product Owners and Dev Managers (25 minutes)
• Group exercise: Write custom ChatGPT prompts to inspect the traceability between code, tests, and requirements for the simple web app.

High-Performing Agile Teams (15 minutes)
• Discussion: Challenges and successes in implementing Gen AI

Conclusion and Next Steps (5 minutes)
• Q&A session

Setup and Equipment Required

A phone, tablet, or laptop that can run a browser and access the internet.

Detecting Bias in AI: Building Fair and Ethical Models

As AI increasingly shapes our reality, recognizing and mitigating potential biases in these technologies is crucial. Large language models (LLMs), despite being trained on vast datasets, can reflect and amplify existing societal biases present in the vast and diverse datasets used to train them.

We'll discuss how biases in training data can propagate through AI systems, potentially perpetuating societal inequalities. As Dr. Joy Buolamwini, founder of the Algorithmic Justice League, emphasizes, “When AI systems are used as the gatekeeper of opportunities, it is critical that the oversight of the design, development, and deployment of these systems reflect the communities that will be impacted by them.” [1]

While many emerging AI companies aim to ensure their technology benefits humanity, the rapid expansion of AI applications introduces new ethical challenges. This session explores practical techniques for implementing AI observability, governance, and responsible AI management aligned with industry standards.

This presentation will delve into the standard and technical practices necessary to detect and mitigate AI systems' bias. You'll learn how to:
- Identifying bias sources in AI systems
- Implementing fair AI practices throughout the development lifecycle
- Ensuring compliance with emerging AI regulations and ethics guidelines

Target Audience:

This presentation is designed for professionals across various stages of AI adoption who prioritize ethical AI implementation.

Learning Outcomes:

Upon completion of this session, attendees will be equipped to:
1. Assess and mitigate bias in AI training data
2. Identify and document performance disparities in AI models
3. Develop and implement comprehensive testing strategies for detecting unfair biases
4. Implement governance frameworks for responsible AI development and deployment

Info for the Speaker Selection Committee

[1] OpenAI’s technology is upending our everyday lives. It’s overseen exclusively by wealthy, White men

https://www.cnn.com/2023/12/15/tech/openai-board-diversity/index.html

___________________________________

Time Breakdown for 90-minute workshop

Introductions - 10 mins

Detecting examples of bias in AI - 65 mins
• Why biases are undesirable
• Evaluating model fairness
• Metrics for quantifying discrimination
• Bias mitigation metrics and toolkits
• Auditability, explainability, and accountability

Wrap up and Q&A - 15 minutes

___________________________________

Presentation History

https://speakerdeck.com/billagileinnovator

Becoming a Super Learner: AI in Education

In the fast-changing world of technology and education, Artificial Intelligence (AI) is seen as a transformative opportunity. This talk will explore the realm of AI and its significant impact on learning methods in higher education. My goal is to show how AI can turn students into 'Super Learners,' enabling them to absorb information with exceptional effectiveness.

We will explore the impacts of AI on the college experience from various perspectives:

From the perspective of undergraduates, we'll explore AI's role in personalizing and enhancing the learning experience. Imagine having a learning experience tailored just for you, with AI helping you grasp complex concepts and better retain information.

For graduate students, AI's ability to handle complex datasets and aid in groundbreaking research will be a focus area. We'll discuss how AI can be a powerful ally in research, data analysis, and publishing efforts.

Professors and educators aren't left out. We'll explore how AI can revolutionize teaching methodologies, reduce administrative burdens, and create more dynamic and engaging classrooms.

So, join us and discover how AI can empower you to become a Super Learner.

Target Audience

This talk is ideal for anyone at the intersection of education and AI, from students to administrators and faculty enhancing pedagogy.

Learning Outcomes

By attending this talk, you will gain the following insights:

1. Identify at least three ways AI can personalize and enhance learning for undergraduate students.
2. Articulate how AI can assist graduate students in handling complex datasets and conducting research.
3. Describe at least two ways AI can revolutionize teaching methodologies.
4. Outline a plan for integrating AI tools into their own educational practices to become "super learners."

Time Breakdown (90 minutes)

5 minutes: Introduction
Brief overview of the workshop
Quick poll to gauge attendees' familiarity with AI in education

10 minutes: Current State of AI in Education
Overview of AI applications in universities today
Interactive discussion on observed trends

30 minutes: AI for Undergraduates
Personalized learning experiences
AI-assisted knowledge retention techniques
Group exercise: Prompt Engineering

20 minutes: AI for Graduates
Advanced AI applications in research
Group exercise: Prompt Engineering

15 minutes: AI for Faculty
AI integration into education
Group exercise: Prompt Engineering

10 minutes: Q&A
Next Steps

Is My Agile Toolchain Free from AI Bias?

Many emerging AI companies strive to ensure their technologies benefit humanity. However, the rapid growth of AI applications presents new ethical challenges. As AI technologies are increasingly applied to the Agile development ecosystem—impacting everything from defining requirements to completing code and conducting automated testing—agile teams must be vigilant against the unintentional embedding of biases. Large Language Models (LLMs) offer remarkable capabilities; however, their training on vast datasets from the internet can reflect and amplify societal biases. This may negatively affect end-user satisfaction, user interface interactions, and team dynamics.

This hands-on workshop will arm Agile practitioners with essential strategies to audit their AI-enhanced toolchains for hidden biases. Participants will engage in practical, interactive exercises to:

• Diagnose points within their Agile workflows where AI tools are employed.
• Understand the origins and pathways of AI bias.
• Utilize state-of-the-art tools to uncover and address these biases.
• Foster productive cross-disciplinary dialogues that emphasize ethical product development.

Target Audience:

This workshop is tailored for Scrum Masters, Agile Coaches, Product Owners, Technical Leads, and Development Team Members who are committed to ethical AI integration within their practices.

Participants will leave with practical templates, checklists, and techniques they can immediately apply to make their Agile practices more bias-aware and responsible.

Learning Outcomes

Participants will exit the workshop equipped with actionable templates, checklists, and strategies to enhance their Agile methodologies with a keen awareness of bias.

They will:

• Map AI Bias within Agile Toolchains: Identify and document potential bias sources within their toolchains.

• Comprehend Bias Sources and Mechanisms: Gain insight into various AI bias types, including data, algorithmic, and human-induced biases, enhancing their ability to mitigate these in real-world scenarios.

• Apply Tools for Bias Detection: Explore and apply AI fairness tools and frameworks like Fairlearn and AI Fairness 360, along with demonstrations of industry-leading solutions like Google's AI Fairness toolkit.

• Enhance Cross-Functional Collaboration: Understand the importance of collaboration between developers, data scientists, ethicists, and business leaders in ensuring that products are developed and deployed ethically, and are aligned with organizational values and ethics guidelines.

Time Breakdown for 90-minute workshop

Introduction and Context (10 minutes)
• Brief overview of AI bias and its impact on society
• Opening exercise: Participants will work in small groups to map their current agile toolchain

Understanding AI Bias Sources and Propagation (20 minutes)
• How biases in training data can propagate through AI systems
• Group exercise: Identify and discuss real-world examples of AI bias in various industries

Low-tech and high-tech techniques for Bias Detection (30 minutes)
• Tool demonstration: Using multiple GPTs to detect bais in AI responses
• Tool demonstration of Open-source libraries: e.g., Fairlearn, AI Fairness 360
• Tool demonstration of Commercial tools: e.g., Google's AI Fairness, Microsoft's Fairness, Accountability, and Transparency (FAT)

Cross-Functional Collaboration for Ethical Product Development (20 minutes)
• Discussion on the importance of cross-functional collaboration when using AI-driven toolchains for product development. This outcome emphasizes the need for a holistic approach involving legal experts, data scientists, ethicists, and business leaders.
• Closing exercise: With knowledge gained, participants will work in small groups to revise their agile toolchain map

Q&A (10 minutes)
• Additional resources and next steps

Presentation History

https://speakerdeck.com/billagileinnovator

Agile 2024 - Technical Practices for Detecting Bias in AI: Building Fair and Ethical Models

Beer City Code - Technical Practices for Detecting Bias in AI: Building Fair and Ethical Models

BITCON2024 - Becoming a Super Learner - AI in Education