AI & Data Literacy

AI & Data Literacy

by Bill Schmarzo
Released July 2023
Publisher(s): Packt Publishing
ISBN: 9781835083505

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Book description

Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world. Purchase of the print or Kindle book includes a free PDF eBook

Key Features

  • Prepare for a future dominated by AI and big data
  • Enhance your AI and data literacy with real-world examples
  • Learn how to leverage AI and data to address current and future challenges

Book Description

AI is undoubtedly a game-changing tool with immense potential to improve human life.

This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you’ll need to exploit to thrive amid the current and future developments in the AI landscape.

We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions.

Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed.

Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it.

What you will learn

  • Get to know the fundamentals of data literacy, privacy, and analytics
  • Find out what makes AI tick and the role of the AI utility function
  • Make informed decisions using prominent decision-making frameworks
  • Understand relevant statistics and probability concepts
  • Create new sources of value by leveraging and applying AI and data
  • Apply ethical parameters to AI development with real-world examples
  • Find out how to get the most out of ChatGPT and its peers

Who this book is for

This book is designed to benefit everyone from students to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their AI and Data literacy.

Table of contents

  1. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Get in touch
  2. Why AI and Data Literacy?
    1. History of literacy
    2. Understanding AI
      1. Dangers and risks of AI
      2. AI Bill of Rights
    3. Data + AI: Weapons of math destruction
    4. Importance of AI and data literacy
    5. What is ethics?
    6. Addressing AI and data literacy challenges
      1. The AI and Data Literacy Framework
      2. Assessing your AI and data literacy
    7. Summary
    8. References
  3. Data and Privacy Awareness
    1. Understanding data
      1. What is big data?
      2. What is synthetic data?
    2. How is data collected/captured?
      1. Sensors, surveillance, and IoT
      2. Third-party data aggregators
    3. Understanding data privacy efforts and their efficacy
      1. Data protection and privacy laws
      2. Data privacy statements
    4. How organizations monetize your personal data
    5. Summary
    6. References
  4. Analytics Literacy
    1. BI vs. data science
      1. What is BI?
      2. What is data science?
      3. The differences between BI and data science
    2. Understanding the data science development process
      1. The critical role of design thinking
    3. Navigating the analytics maturity index
      1. Level 1: Operational reporting
      2. Level 2: Insights and foresight
        1. Statistical analytics
        2. Exploratory analytics
        3. Diagnostic analytics
        4. Machine learning
      3. Level 3: Augmented human intelligence
        1. Neural networks
        2. Regression analysis
        3. Recommendation engines
        4. Federated learning
      4. Level 4: Autonomous analytics
        1. Reinforcement learning
        2. Generative AI
        3. Artificial General Intelligence
    4. Summary
  5. Understanding How AI Works
    1. How does AI work?
    2. What constitutes a healthy AI utility function?
      1. Defining “value”
      2. Understanding leading vs. lagging indicators
    3. How to optimize AI-based learning systems
      1. Understand user intent
      2. Build diversity
    4. Summary
  6. Making Informed Decisions
    1. Factors influencing human decisions
    2. Human decision-making traps
      1. Trap #1: Over-confidence bias
      2. Trap #2: Anchoring bias
      3. Trap #3: Risk aversion
      4. Trap #4: Sunk costs
      5. Trap #5: Framing
      6. Trap #6: Bandwagon effect
      7. Trap #7: Confirmation bias
      8. Trap #8: Decisions based on averages
    3. Avoiding decision-making traps
    4. Exploring decision-making strategies
      1. Informed decision-making framework
      2. Decision matrix
      3. Pugh decision matrix
      4. OODA loop
    5. Critical thinking in decision making
    6. Summary
    7. References
  7. Prediction and Statistics
    1. What is prediction?
    2. Understanding probabilities and statistics
    3. Probabilities are still just probabilities, not facts
    4. Introducing the confusion matrix
    5. False positives, false negatives, and AI model confirmation bias
      1. Real-world use case: AI in the world of job applicants
    6. Summary
    7. References
  8. Value Engineering Competency
    1. What is economics? What is value?
    2. What is nanoeconomics?
    3. Data and AI Analytics Business Model Maturity Index
      1. Stages
      2. Inflection points
    4. Value Engineering Framework
      1. Step 1: Defining value creation
      2. Step 2: Realizing value creation via use cases
      3. Step 3: Scale value creation
    5. What are the economies of learning?
    6. Monetize analytic “insights,” not data
    7. Summary
  9. Ethics of AI Adoption
    1. Understanding ethics
      1. Ethics is proactive, not passive
      2. Redefining ethics in the age of AI
    2. The intersection of ethics, economics, and societal well-being
      1. Ethical behaviors make for good economics
      2. The difference between financial and economic metrics
      3. The role of laws and regulations on ethics
    3. Achieving a responsible and ethical AI implementation
      1. The Ethical AI Pyramid
      2. Ensuring transparent AI
    4. Understanding unintended consequences
      1. Identifying unintended consequences
      2. Mitigating unintended consequences
    5. Summary
    6. References
  10. Cultural Empowerment
    1. A history lesson on team empowerment
    2. Tips for cultivating a culture of empowerment
      1. #1: Internalize your mission
      2. #2: Walk in the shoes of your stakeholders
      3. #3: Nurture organizational improvisation
      4. #4: Embrace an “AND” mentality
      5. #5: Ensure everyone has a voice
      6. #6: Unleash the curiosity-creativity-innovation pyramid
    3. Driving AI and data literacy via cultural empowerment
    4. Reassessing your AI and data literacy
    5. Summary
  11. ChatGPT Changes Everything
    1. What are ChatGPT and GenAI?
    2. How does ChatGPT work?
      1. Beginner level 101
      2. Capable level 201
      3. Proficient level 301
    3. Critical ChatGPT-enabling technologies
      1. LLM
      2. Transformers
      3. Role-based personas
      4. Reinforcement Learning from Human Feedback
    4. ChatGPT concerns and risks
    5. Thriving with GenAI
    6. AI, data literacy, and GenAI
    7. Summary
    8. References
  12. Glossary
    1. Data economics
    2. Design thinking
    3. Data science and analytics
  13. Other Books You May Enjoy
  14. Index

Product information

  • Title: AI & Data Literacy
  • Author(s): Bill Schmarzo
  • Release date: July 2023
  • Publisher(s): Packt Publishing
  • ISBN: 9781835083505