CSE 163

Ethics Case Studies�

Suh Young Choi�

🎶 Listening to: Max LL�💬 Before Class: Do we do the right thing because it’s good, or do we do the right thing because it’s expected of us? (Euthyphro paraphrased)

Please sit where you can talk with others : )

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Open these slides: tinyurl.com/cse163su25-ethics-slides (you will need to log in with your UW credentials)

Announcements

Next week is the last week of the quarter!

• 2 more lessons available for completion credit (including this one)

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Project Video due Sunday 8/17

• Turned in via Google Form
• No late work accepted, no resubmissions

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One more resubmission period remaining, unless…

• 60% of the class fills out the course eval by 11:59pm 8/19!
• (Right now, we’re at 33%)
• You may submit any one THA during this “bonus” period (8/20 – 8/22)

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This Time

• Let’s apply what we’ve learned to new case studies!

Last Time(s)

• Defining fairness
• Worldviews
• Defining privacy
• Explaining statistics

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Fairness Recap

What we call “fair” depends on the groups we want to protect

• Individual fairness vs. group fairness

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Tradeoff between fairness and accuracy

• The Pareto frontier can help us assess how models perform with respect to the two metrics
• The “sweet spot” depends on what you prioritize

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Two worldviews

• What You See Is What You Get (WYSIWYG)
• Measurements accurately reflect the real world
• We’re All Equal (WAE)
• Flawed systems or institutions

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Privacy Recap

Anonymity as a proxy for privacy

• k-anonymity
• Fuzzing the data

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Differential privacy

• Data privacy vs. Data security
• Jittering
• Randomized response

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Statistics Recap

We can describe the data that we have collected

• Summary statistics
• Distributions

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We can also make inferences about the state of the world

• Hypothesis testing
• Type I and Type II errors
• Some pitfalls:
• p-hacking
• Multiple hypothesis testing
• HARKing

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Discussion�Guidelines

• Open this document on your computers: tinyurl.com/cse163su25-ethics-slides
• You will be assigned a discussion group and at least one of the case studies covered in the lesson.
• Introduce yourself to your groupmates!
• Pick a note-taker and a presenter.
• Use the questions in the document as conversation starter, but feel free to come up with other questions to discuss!
• If you are done discussing your assigned case studies, you can move on to other readings.
• After 25 minutes of discussion, we will bring everyone back and invite the presenter from each group to share a brief summary of what your group had talked about.

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Discussion�Topics

3 new case studies!

• Facial recognition technology
• Generative AI in creative spaces
• ChatGPT and mental labor

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Feel free to review any of the other case studies from the week—they are all provided in the Lesson 23 slides.

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Discussion�Questions

1. What is the data that is being used in your case study? What assumptions are being made of it?
2. What worldview from the Fairness slides is assumed in your case study? Is it difficult to tell?
3. What groups are affected by the application in your case study, and in what way? (This can be positive or negative!)
4. What privacy concerns might arise from using the data in your case study?
5. Have you used the technology in the case study (or one that is similar)? What did you know about it before using it? Did you learn anything new from the case study?
6. How can you relate the application from your case study to the ones from Lessons 21 or 22 (also linked in Lesson 23)?
7. Come up with questions of your own!

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Before Next Time

• CP 4 + LR 7 due on Monday!
• Project videos due on Sunday!
• Fill out the course eval!

Next Time

• Video showcase!

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