Theme: The Three Pillars

• Bias & Prediction: Identify algorithmic bias, understand fairness definitions
• Privacy & Consent: Data collection, consent, transparency, and responsible use
• Copyright & Creative Work: Who owns AI inputs and outputs? - Are we going to use copyright as the 3rd pillar? do we have enough content?

Pre-Reading

Participants should arrive with a working sense of the three pillars and at least one real example from their own context in mind.

Bias & Fairness

• [Tool] AI Fairness 360 — Interactive Demo (IBM) — Spend 10 minutes exploring how fairness metrics work in practice.
• [Tool] Data Ethics Canvas (Open Data Institute) — A plain-language worksheet for ethical review of data projects. Skim the canvas before the session.
• Name the Bias — A practitioner's lens on identifying and naming AI bias in real contexts. (Kris Krug, 2026)
• UK welfare algorithm case — explainer — A real-world example of what goes wrong when algorithmic outputs go unexamined. (The Guardian, 2024)
• Human attention and AI — Exmaple of interactive data visualization tools that prioritize human agency. (MIT News, 2026) [source research]
• When AI Use Leads to Brain Fry — Study on intensive AI oversight related to acute cognitive fatigue. (Harvard Business Review, 2026)

Privacy & Consent

• [Tool] Canada's Algorithmic Impact Assessment — 65-question self-assessment for AI systems. Skim the structure to understand the risk-tier approach.