Prerequisites

Data, Society, and Responsibility is designed to be accessible to undergraduate students from any discipline. No single prerequisite is strictly required — the book builds its own foundations. However, the following background will enhance your experience.

Essential (Expected of All Readers)

  • Reading comprehension at the college level. You should be comfortable reading academic and journalistic writing, including articles with data, charts, and citations.
  • Basic digital literacy. You use the internet, social media, and digital services regularly. You have a general sense of how apps, websites, and online platforms work — even if you've never built one.
  • Willingness to engage with ambiguity. Many topics in this book involve genuine disagreements among experts. The goal is to develop your own informed position, not to find "the right answer."

Helpful but Not Required

  • Introductory statistics or research methods. Understanding concepts like correlation, sampling, and statistical significance will help with Parts 2-3. If you haven't had a stats course, the Research Spotlight boxes explain the methods used in each study discussed.
  • Introductory philosophy or ethics. Chapter 6 provides a self-contained introduction to ethical frameworks (utilitarianism, deontology, virtue ethics, care ethics, justice theory). Prior exposure will let you engage more deeply, but it is not assumed.
  • Basic awareness of current events in technology. If you follow news about AI, social media regulation, data breaches, or digital privacy, you will recognize many of the examples used. If not, the book provides sufficient context.
  • Introductory political science or public policy. Understanding how laws are made and enforced will help with Part 4 (Governance and Regulation). The chapters provide enough background for readers without this foundation.

For Python Chapters (Chapters 10, 14, 15, 22, 27, 29, 34 + Appendix G)

  • Introductory Python programming. You should be familiar with variables, functions, loops, conditionals, and basic data structures (lists, dictionaries).
  • Basic familiarity with pandas DataFrames. Most code examples use pandas for data manipulation. If you haven't used pandas before, Appendix G includes a brief orientation.
  • A working Python environment. Any setup — Jupyter notebooks, Google Colab, VS Code, a terminal — will work. The code uses only standard libraries plus pandas, numpy, matplotlib, and scikit-learn.

If you don't have Python experience, you can still fully engage with all 40 chapters. The Python code is supplementary — it demonstrates concepts concretely but is always accompanied by plain-language explanation. You will not miss the core arguments or frameworks by skipping the code.

What You Do NOT Need

  • A computer science degree
  • Legal training
  • Prior coursework in data science or machine learning
  • Technical knowledge of how algorithms work internally (we build this from scratch in Part 3)
  • Familiarity with any specific data protection regulation (we cover GDPR, EU AI Act, CCPA, and others from the ground up)

Diagnostic: Are You Ready?

Answer these questions honestly:

  1. Can you read a 10-page article about a technology policy issue and summarize the main argument?
  2. Can you distinguish between an opinion and an evidence-based claim?
  3. Are you willing to consider perspectives you currently disagree with?
  4. Can you write a coherent paragraph explaining your position on a controversial topic?

If you answered yes to all four, you are ready for this book. Everything else, we will build together.