Girlset — AI at Your Fingertips

A short, hands-on crash course on Responsible AI created for Girlset (Concordia).

Contents

• slides/ — workshop slide deck
• activities/ and facilitation notes

Workshop goals

• Demystify where AI shows up in everyday life
• Introduce key risk areas (healthcare bias, hiring algorithms, facial recognition)
• Facilitate reflection on who builds AI and why that matters
• End with a quick activity inspired by Affecting Machines to humanize algorithm design

How to run this workshop (60–75 min)

• Audience: high-school / undergrad beginners
• Group size: 10–40
• Room: projector + speakers + sticky notes
• Materials: printed “If I built AI…” slips (Slide 28), trading cards (Slide 21, optional)
• Flow:
  1. Icebreaker & “Can you trust AI?” (Slides 1–5, 10 min)
  2. Everyday AI + GAN demo (Slides 6–16, 10–12 min)
  3. Case studies & small reflections (Slides 17–20, 15–20 min)
  4. Targeted ads + design-a-fix (Slides 24–25, 10–12 min)
  5. Airport screening fairness (Slides 26–27, 8–10 min)
  6. Personal pledges (Slides 28–29, 5–8 min)

Slides

• 👉 Download the deck

References

• Facial recognition & wrongful arrest: ACLU case page + settlement (Robert Williams).
  • https://www.aclu.org/cases/williams-v-city-of-detroit-face-recognition-false-arrest
  • Settlement docs + updates.
• Hiring algorithm bias: Reuters on Amazon’s resume screener (2018).
  • https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/
• Healthcare triage bias: Obermeyer et al., Science (2019) + explainer.
  • https://www.science.org/doi/10.1126/science.aax2342
  • https://pubmed.ncbi.nlm.nih.gov/31649194/

License

• Original Girlset materials © 2025 Sara Jameel — licensed under CC BY 4.0.
• Adapted content from third parties is used with permission and may carry different terms.