HippoCampus
PhilosophySpiralIntermediate15 chapters

The ethics of AI

HippoCampus
· starter course
Explore the ethics of artificial intelligence: bias and fairness, privacy, autonomy, accountability when systems fail, effects on work, and the main arguments on each side.

What you'll learn

15 chapters
  1. 1

    Why AI Ethics Emerged

    • Describe why ethical reflection became a central part of AI development and deployment.
    • Identify the social conditions that turned AI from a technical topic into a public ethical issue.
    • Recognize the broad categories of harms and benefits that frame AI ethics debates.
  2. 2

    History of AI and Shifting Ethical Concerns

    • Outline major phases in the development of AI and how ethical concerns changed over time.
    • Connect earlier automation debates to current AI ethics issues.
    • Explain why modern data-driven AI produced new kinds of ethical concern.
  3. 3

    Core Ethical Frameworks for Thinking About AI

    • Use basic ethical frameworks to organize arguments about AI.
    • Compare outcome-focused, duty-focused, rights-based, and justice-oriented approaches.
    • Recognize how different frameworks can support different conclusions about the same AI system.
  4. 4

    AI in Society: Everyday Uses and Stakes

    • Identify the main social domains where AI is used.
    • Explain why ethical stakes differ across contexts such as healthcare, policing, education, and hiring.
    • Distinguish low-stakes and high-stakes AI applications at an overview level.
  5. 5

    Bias, Fairness, and Discrimination

    • Define bias, fairness, and discrimination in AI contexts.
    • Explain common sources of unfair outcomes in AI systems.
    • Compare broad approaches to reducing unfairness without going into technical detail.
  6. 6

    Privacy, Surveillance, and Data Rights

    • Explain why AI raises privacy concerns beyond ordinary data collection.
    • Identify key ethical issues involving consent, surveillance, and data use.
    • Compare competing arguments about convenience, security, and privacy.
  7. 7

    Autonomy, Manipulation, and Human Agency

    • Describe how AI systems can shape choices, behavior, and self-direction.
    • Explain ethical concerns about manipulation, dependence, and loss of agency.
    • Distinguish assistance from undue influence in AI-mediated decision environments.
  8. 8

    Accountability When AI Systems Fail

    • Explain why responsibility becomes difficult when AI systems cause harm.
    • Identify the main actors who may share accountability for AI outcomes.
    • Survey broad models of assigning responsibility across design, deployment, and oversight.
  9. 9

    Human Oversight, Transparency, and Explainability

    • Define transparency, explainability, and human oversight in broad terms.
    • Explain why people often demand explanations from AI systems, especially in high-stakes settings.
    • Recognize the limits of transparency as a complete ethical solution.
  10. 10

    Safety, Reliability, and Risk

    • Describe why safe and reliable AI behavior is an ethical as well as technical issue.
    • Identify different kinds of AI risk, from ordinary errors to broader systemic harms.
    • Explain the relationship between uncertainty, testing, and responsible deployment.
  11. 11

    Work, Automation, and Economic Justice

    • Summarize major ethical concerns about AI's effects on work and livelihoods.
    • Compare arguments that AI will create opportunity with arguments that it will deepen inequality.
    • Explain why labor impacts are also questions of justice and power.
  12. 12

    Democracy, Power, and Social Order

    • Explain how AI can affect public discourse, institutions, and power relationships.
    • Identify ethical concerns about concentration of power in governments and large firms.
    • Recognize the broader societal stakes beyond individual harms.
  13. 13

    AI Governance, Regulation, and Standards

    • Survey the main ways societies try to govern AI.
    • Explain the differences among law, regulation, professional standards, and organizational policy.
    • Compare high-level arguments for stronger versus lighter regulation.
  14. 14

    Main Arguments Across AI Ethics Debates

    • Summarize the strongest recurring arguments on multiple sides of AI ethics issues.
    • Compare innovation-focused, rights-focused, justice-focused, and safety-focused positions.
    • Practice weighing trade-offs without assuming simple one-sided answers.
  15. 15

    Looking Ahead: Emerging Questions in AI Ethics

    • Identify major frontier questions likely to shape future AI ethics discussions.
    • Connect present-day ethical themes to newer forms of AI capability and deployment.
    • Prepare for deeper future study in later spiral passes.

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