AI and data literacy empowering citizens of data science
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 re...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2023]
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| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009786701406719 |
Tabla de Contenidos:
- Cover
- Copyright
- Endorsements
- Contributor
- Table of Contents
- Preface
- Chapter 01: Why AI and Data Literacy?
- History of literacy
- Understanding AI
- Dangers and risks of AI
- AI Bill of Rights
- Data + AI: Weapons of math destruction
- Importance of AI and data literacy
- What is ethics?
- Addressing AI and data literacy challenges
- The AI and Data Literacy Framework
- Assessing your AI and data literacy
- Summary
- References
- Chapter 02: Data and Privacy Awareness
- Understanding data
- What is big data?
- What is synthetic data?
- How is data collected/captured?
- Sensors, surveillance, and IoT
- Third-party data aggregators
- Understanding data privacy efforts and their efficacy
- Data privacy ramifications
- Data privacy statements
- How organizations monetize your personal data
- Summary
- References
- Chapter 03: Analytics Literacy
- BI vs. data science
- What is BI?
- What is data science?
- The differences between BI and data science
- Understanding the data science development process
- The critical role of design thinking
- Navigating the analytics maturity index
- Level 1: Operational reporting
- Level 2: Insights and foresight
- Statistical analytics
- Exploratory analytics
- Diagnostic analytics
- Machine learning
- Level 3: Augmented human intelligence
- Neural networks
- Regression analysis
- Recommendation engines
- Federated learning
- Level 4: Autonomous analytics
- Reinforcement learning
- Generative AI
- Artificial General Intelligence
- Summary
- Chapter 04: Understanding How AI Works
- How does AI work?
- What constitutes a healthy AI utility function?
- Defining "value"
- Understanding leading vs. lagging indicators
- How to optimize AI-based learning systems
- Understand user intent
- Build diversity
- Summary.
- Chapter 05: Making Informed Decisions
- Factors influencing human decisions
- Human decision-making traps
- Trap #1: Over-confidence bias
- Trap #2: Anchoring bias
- Trap #3: Risk aversion
- Trap #4: Sunk costs
- Trap #5: Framing
- Trap #6: Bandwagon effect
- Trap #7: Confirmation bias
- Trap #8: Decisions based on averages
- Avoiding decision-making traps
- Exploring decision-making strategies
- Informed decision-making framework
- Decision matrix
- Pugh decision matrix
- OODA loop
- Critical thinking in decision making
- Summary
- References
- Chapter 06: Prediction and Statistics
- What is prediction?
- Understanding probabilities and statistics
- Probabilities are still just probabilities, not facts
- Introducing the confusion matrix
- False positives, false negatives, and AI model confirmation bias
- Real-world use case: AI in the world of job applicants
- Summary
- References
- Chapter 07: Value Engineering Competency
- What is economics? What is value?
- What is nanoeconomics?
- Data and AI Analytics Business Model Maturity Index
- Stages
- Inflection points
- Value Engineering Framework
- Step 1: Defining value creation
- Step 2: Realizing value creation via use cases
- Step 3: Scale value creation
- What are the economies of learning?
- Monetizing analytic "insights," not data
- Summary
- Chapter 08: Ethics of AI Adoption
- Understanding ethics
- Ethics is proactive, not passive
- Redefining ethics in the age of AI
- The intersection of ethics, economics, and societal well-being
- Ethical behaviors make for good economics
- The difference between financial and economic metrics
- The role of laws and regulations on ethics
- Achieving a responsible and ethical AI implementation
- The Ethical AI Pyramid
- Ensuring transparent AI
- Understanding unintended consequences.
- Identifying unintended consequences
- Mitigating unintended consequences
- Summary
- References
- Chapter 09: Cultural Empowerment
- A history lesson on team empowerment
- Tips for cultivating a culture of empowerment
- #1: Internalize your mission
- #2: Walk in the shoes of your stakeholders
- #3: Nurture organizational improvisation
- #4: Embrace an "AND" mentality
- #5: Ensure everyone has a voice
- #6: Unleash the curiosity-creativity-innovation pyramid
- Driving AI and data literacy via cultural empowerment
- Reassessing your AI and data literacy
- Summary
- Chapter 10: ChatGPT Changes Everything
- What are ChatGPT and GenAI?
- How does ChatGPT work?
- Beginner level 101
- Capable level 201
- Proficient level 301
- Critical ChatGPT-enabling technologies
- LLM
- Transformers
- Role-based personas
- Reinforcement Learning from Human Feedback
- ChatGPT concerns and risks
- Thriving with GenAI
- AI, data literacy, and GenAI
- Summary
- References
- Glossary
- Data Economics
- Design Thinking
- Data Science and Analytics
- Other Books You May Enjoy
- Index.
