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W01Full seminar3 hours

Introduction to AI Ethics

ANI, AGI, and the moral landscape

What kind of thing is AI—and who gets to say?

We begin by refusing the idea that an AI system is only a model. Students map the stories shaping AI discourse, locate values in technical choices, and use four ethical frameworks on the same cases.

Join the live room
Four ethical lenses arranged around an AI decision
Visual field note · Week 1 · use this diagram to keep the model inside its surrounding system.

Before class

Read one narrative camp charitably. Bring one current AI headline and a two-sentence account of the policy your author would derive from it.

After class

Write the seminar charter in your own words: one norm you expect to find easy, one you expect to find difficult, and how the room should hold you to it.

The promise

By the end of this room…

  1. 01Distinguish a technical artifact from the socio-technical system around it.
  2. 02Identify how cultural narratives change the policy derived from the same evidence.
  3. 03Apply consequentialist, deontological, virtue, and justice-based reasoning.
  4. 04Separate fairness, accountability, transparency, privacy, and safety as operational questions.
  5. 05Read a canonical AI case against its primary evidence rather than its headline.

Why this week now

Signals, not scene-setting.

01

The normal-technology, power/empire, existential-risk, and bubble camps now produce distinct engineering and policy programs.

02

The state is no longer only regulating systems; EO 14319 turns a definition of ideological neutrality into procurement policy.

03

Stanford's 2026 evidence records rising incident counts while model transparency remains weak.

Run of show

Provoke → frame → work → argue → synthesize.

Open student room ↗
  1. 0:00
    provocation

    One word, then four corners

    Students submit one word for AI, then commit to the narrative camp closest to their current view: normal technology, empire, existential risk, or bubble.

    Deliverable · A room map we photograph and revisit in Week 13.Live activity · narrative camp
  2. 0:15
    frame

    The model is not the system

    Definitions in tension; the socio-technical layer model; the neutrality fallacy; and the course's recurring move from an output to the institution that made it consequential.

  3. 0:45
    discussion

    One headline, four policies

    Pairs receive the same current headline, identify its implicit narrative, and state the policy each camp would derive from it.

    Deliverable · A 100-word pair post with one evidence link.Live activity · headline policy
  4. 1:10
    frame

    Ethics as engineering questions

    Consequences as a loss function, duties as hard constraints, virtue as institutional habit, and Rawlsian justice as a distributional stress test.

  5. 1:35
    break

    Break

    Ten minutes. Leave the room's unresolved question visible.

  6. 1:45
    forensics

    Framework speed-dating

    Four groups run one assigned framework across COMPAS, Gender Shades, Uber ATG, and the Air Canada chatbot, then apply it to one shared current case.

    Deliverable · A framework card: duty, evidence, affected party, action, blind spot.Live activity · framework friction
  7. 2:25
    controversy

    Can neutrality be specified?

    Assigned sides debate whether the federal procurement order is bias mitigation, switch sides, then draft a defensible neutrality test—or explain why one cannot exist.

    Live activity · neutrality sac
  8. 2:50
    synthesis

    What would move you?

    Ratify the seminar norms and name the evidence that could move you out of your opening narrative camp.

    Live activity · week 1 exit

Case room

Evidence before opinion.

Canonical

COMPAS

Which definition of fairness is hiding inside the headline?

ProPublica
Current

Air Canada chatbot

Can a deployer disclaim the system through which it speaks?

CanLII
Anchor

EO 14319

Can the state operationalize ideological neutrality without choosing an ideology?

The White House

Reading stack

Read the tension, not the bibliography.

  1. 01
    CoreDo Artifacts Have Politics?

    Langdon Winner

  2. 02
    CurrentAI as Normal Technology

    Arvind Narayanan & Sayash Kapoor

  3. 03
    CurrentResponsible AI — AI Index 2026

    Stanford HAI

  4. 04
    ReferenceRecommendation on the Ethics of AI

    UNESCO

Evidence ledger

Every case has a receipt.

10 primary, scholarly, or first-party sources

OECD.AI2024

Updated definition of an AI system

A functional definition built around inputs, inferred outputs, objectives, autonomy, and adaptiveness.

Definition & policy note
NIST2023

Artificial Intelligence Risk Management Framework 1.0

The primary source for Govern–Map–Measure–Manage and lifecycle risk ownership.

Risk-management framework
UNESCO2021

Recommendation on the Ethics of Artificial Intelligence

Connects AI governance to dignity, rights, justice, diversity, participation, and remedy.

International recommendation
The title and byline of AI as Normal Technology on the Knight Institute website
Knight First Amendment Institute2025

AI as Normal Technology

Narayanan and Kapoor argue that AI can be transformative while remaining governable as a normal technology.

Current argument
Stanford AI Index text reporting 362 documented AI incidents in 2025
Stanford HAI2026

AI Index 2026 — Responsible AI

Current evidence on AI incidents, transparency, evaluations, and institutional accountability.

Evidence report
The White House page for Executive Order 14319
The White House2025

Preventing Woke AI in the Federal Government

A live case in the state defining ideological neutrality and bias for procured language models.

Executive order
CanLII2024

Moffatt v. Air Canada, 2024 BCCRT 149

The primary record for a company being held to information delivered by its customer-service chatbot.

Tribunal decision
The ProPublica methodology article titled How We Analyzed the COMPAS Recidivism Algorithm
ProPublica2016

How we analyzed the COMPAS recidivism algorithm

The evidence and cohort construction behind the canonical COMPAS fairness dispute.

Investigative methodology
Proceedings of Machine Learning Research2018

Gender Shades: Intersectional Accuracy Disparities

The foundational intersectional audit of commercial gender-classification systems.

Peer-reviewed audit
U.S. National Transportation Safety Board2019

Tempe automated vehicle crash investigation

An official record for tracing harm across perception, interface, operations, oversight, and organization.

Safety investigation