Week 15 — Readings & Resources · AI, Ethics, Privacy & the Future of Work

Course: Using Artificial Intelligence (AI 101) · Silver Oak University (fictional sample) · Prof. Quinn
Objective covered: Objective 7 — Apply responsible-AI practices — data privacy and what not to paste, terms of service and data retention, content ownership / IP, bias and fairness, and academic & professional integrity — and build a personal ethical framework for the AI age.

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How to use this page

Everything here is a link to an external resource — open it in your browser. Nothing needs to be downloaded.

This week's resources are grouped by lecture topic. Read or watch one item per group and you will be ready for the quiz; doing all of them will make you well-prepared for the Studio and the Discussion. Total time is roughly 40–55 minutes if you do everything; much less if you pick one per group.

Order that matches the lecture: ① Privacy & what not to paste → ② ToS & data retention → ③ Content ownership / IP / copyright → ④ Bias & fairness → ⑤ Academic & professional integrity → ⑥ The future of work → ⑦ Troubleshooting

A verification habit for this week: some of these resources link to rapidly evolving legal and policy areas. Before applying any legal or regulatory guidance to a real situation, verify against the most current official source and consult qualified counsel if needed.

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① Privacy & What Not to Paste

Maps to Lecture Segment 2. HIPAA, FERPA, PCI, confidential/proprietary material; the billboard test; anonymizing inputs.

U.S. Department of Health & Human Services — HIPAA Overview
🔗 https://www.hhs.gov/hipaa/for-professionals/index.html
Why it's here: the authoritative primary source for HIPAA requirements, maintained by the federal agency responsible for enforcement. Skim the "For Professionals" landing page to understand what HIPAA covers and who it applies to.
⏱ ~8 min (skim)

Federal Trade Commission — Protecting Consumer Privacy
🔗 https://www.ftc.gov/business-guidance/privacy-security
Why it's here: the FTC's business guidance on privacy and data security — the regulatory agency perspective on data handling. Relevant to understanding why consumer tools have privacy policies at all.
⏱ ~8 min (skim)

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② Terms of Service & Data Retention

Maps to Lecture Segment 3. What consumer AI tools may do with your inputs; how to find and read the key sections of a ToS; enterprise vs. consumer tiers.

OpenAI Privacy Policy (official)
🔗 https://openai.com/en-US/policies/privacy-policy
Why it's here: a major consumer AI provider's actual privacy policy. Look specifically for: what data is collected, how it may be used to improve models, and how to opt out. Skim — you do not need to read every clause, but knowing where the key sections live is the skill.
⏱ ~10 min (skim)

Anthropic Privacy Policy (official)
🔗 https://www.anthropic.com/legal/privacy
Why it's here: Anthropic's privacy policy for Claude users. Compare to the OpenAI policy — notice similarities and differences. This is the kind of comparison you should do when choosing between tools for sensitive work.
⏱ ~8 min (skim)

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③ Content Ownership / IP / Copyright

Maps to Lecture Segment 4, Part A. Copyright status of AI-generated content; the human-authorship requirement; why AI is not a legal authority on this. Reminder: not legal advice — consult qualified counsel for specific situations.

U.S. Copyright Office — Copyright and Artificial Intelligence (official)
🔗 https://www.copyright.gov/ai/
Why it's here: the U.S. Copyright Office's official page on AI and copyright, including published guidance and formal decisions on AI-generated works. This is the authoritative source — not a chatbot summary of it.
⏱ ~10 min

U.S. Copyright Office — Compendium of U.S. Copyright Office Practices: AI authorship guidance
🔗 https://www.copyright.gov/comp3/
Why it's here: the Copyright Office's formal practices document, which includes the agency's position on works requiring human authorship. Referenced in published guidance on AI-generated works.
⏱ ~5 min (relevant sections)

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④ Bias & Fairness in AI

Maps to Lecture Segment 4, Part B. Why AI is not neutral or unbiased by default; bias as a product of training data; what to do about it.

NIST AI Risk Management Framework — Overview
🔗 https://www.nist.gov/system/files/documents/2023/01/26/AI RMF Playbook.pdf
Why it's here: the National Institute of Standards and Technology's framework for managing AI risks, including bias and fairness. NIST is a federal standards body; this is the official government guidance on the topic. Skim the executive summary and the "GOVERN" and "MAP" sections.
⏱ ~10 min (skim)

Alternative — NIST AI 100-1: Artificial Intelligence Risk Management Framework (main page)
🔗 https://www.nist.gov/artificial-intelligence/ai-risk-management-framework
Why it's here: if the PDF link above is slow, this page gives you access to the same materials via the NIST website.
⏱ ~5 min (navigate to bias/fairness sections)

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⑤ Academic & Professional Integrity

Maps to Lecture Segment 5. Disclosure standards; when AI use is and is not acceptable; institutional policies.

International Center for Academic Integrity — Fundamental Values of Academic Integrity
🔗 https://academicintegrity.org/resources/fundamental-values
Why it's here: the framework used by most academic integrity policies in the U.S. — honesty, trust, fairness, respect, responsibility, courage. Read through the fundamental values to understand the principled foundation of academic integrity before applying it to AI.
⏱ ~8 min

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⑥ The Future of Work and AI

Maps to Lecture Segment 7. Competing views; what to watch; how to adapt.

MIT Work of the Future — Research Briefs
🔗 https://workofthefuture.mit.edu/research-post/
Why it's here: MIT's research initiative on technology and work. Their published briefs present nuanced, evidence-based analysis of AI's labor-market impact — notably more cautious and multidimensional than either the "AI will take all jobs" or "AI creates only good jobs" extremes.
⏱ ~10 min (browse available briefs)

World Economic Forum — Future of Jobs Report (official)
🔗 https://www.weforum.org/publications/the-future-of-jobs-report-2025/
Why it's here: the WEF's regularly updated analysis of technology's impact on employment globally. Presents data and forecasts from a broad range of industries. Read the executive summary.
⏱ ~10 min (executive summary)

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⑦ Troubleshooting — Skill 13

Maps to Lecture Segment 6. Starting over, managing context, switching models, using AI to teach AI.

There are no assigned readings for Skill 13 — the four troubleshooting moves are best learned by doing. Use your Lecture Tutorial 15 and Practice Exercises to work through them in a real conversation.

Optional reference — Claude's official guidance on effective use:
🔗 https://claude.ai/new
Why it's here: the Claude interface itself; open a fresh conversation and practice the "start over" move by contrast with a long conversation. Notice how context resets.

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Pick-one quick path (≈18 min total)

In a hurry? Do exactly these three and you will be ready for the quiz:
1. Skim the HHS HIPAA Overview (group ①) — know what HIPAA covers.
2. Skim either the OpenAI or Anthropic privacy policy (group ②) — find the data-use and opt-out sections.
3. Read the U.S. Copyright Office AI page (group ③) — understand the human-authorship requirement.

Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, go directly to the domain (hhs.gov, copyright.gov, nist.gov, ftc.gov) and search for the topic. Nothing here is downloaded or redistributed — all resources stay as links to their original sources.