Schmarzo AI & Data Literacy

Empowering Citizens of Data Science
1. Auflage 2023
ISBN: 978-1-83508-794-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 238 Seiten

No detailed description available for "AI & Data Literacy".

Autoren/Hrsg.

Schmarzo, Bill

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Table of Contents - Why AI and Data Literacy?
- Data and Privacy Awareness
- Analytics Literacy
- Understanding How AI Works
- Making Informed Decisions
- Prediction and Statistics
- Value Engineering Competency
- Ethics of AI Adoption
- Cultural Empowerment
- ChatGPT Changes Everything

Leseproben

Contents
Preface Who this book is for What this book covers To get the most out of this book Get in touch 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 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 protection and privacy laws Data privacy statements How organizations monetize your personal data Summary References 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 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 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 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 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? Monetize analytic “insights,” not data Summary 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 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 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 ...

Über Autor(innen)

Schmarzo Bill:

Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses “Big Data MBA” and “Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.