Lectures
You can download the lectures here. We will try to upload lectures prior to their corresponding classes. Initial versions of the slides (from previous years) may be updated the days preceding the lecture.
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Lecture 10 - Algorithmic Pricing complications 2
tl;dr: Algorithmic pricing: capacity, price differentiation, and competition
[slides]
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Lecture 2 - Data challenges
tl;dr: Common challenges in data collection.
[slides]
Suggested Readings:
- Lessons from measurement [only need to read measurement section]
- When You Hear the Margin of Error Is Plus or Minus 3 Percent, Think 7 Instead
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NO CLASS -- Labor day
tl;dr:
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Lecture 4 - Weighting 2
tl;dr: More on weighting, Uncertainty quantification with selection on unknown covariates
[slides]
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Lecture 6 - Recommendations introduction
tl;dr: Introduction to Recommendations, including data challenges and collaborative filtering
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Suggested Readings:
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Lecture 7 - Recommendations, from predictions to decisions
tl;dr: From predicting ratings to making decisions: capacity constraints and multi-sided recommendations
[slides]
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Special Lecture (by Gabriel, Evan, Sidhika) -- Challenges in real-world data
tl;dr: Gabriel, Evan, and Sidhika will discuss challenges in real-world data
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Guest Lecture -- Deb Raji
tl;dr: Deb Raji (UC Berkeley).
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Lecture 9 - Algorithmic Pricing complications
tl;dr: Algorithmic pricing: capacity, price differentiation, and competition
[slides]
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Lecture 11 - Algorithmic Pricing practice -- ride-hailing
tl;dr: Algorithmic pricing: ride-hailing case study
[slides]
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NO CLASS -- FALL BREAK
tl;dr:
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Lecture 13 - Experimentation -- Peeking and Interference
tl;dr: Experimentation challenges in online marketplaces: peeking and interference
[slides]
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In class discussion - Ethics in (Algorithmic) Pricing
tl;dr: What makes a pricing decision unfair?
Suggested Readings:
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Guest Lecture -- Allison Koenecke
tl;dr: Allison Koenecke (Cornell).
Title: Equitable Decision-Making in Public Resource Allocation
Abstract: Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. In this talk, I audit the equity of decision-making in two public resource allocation settings. First, I present a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits – specifically, considering budget-constrained trade-offs between ad conversions for English-speaking and Spanish-speaking SNAP applicants. Second, I discuss sensitivity analyses on public funding allocation algorithms such as CalEnviroScreen, an algorithm used to promote environmental justice by aiding disadvantaged census tracts – which we find to encode bias against tracts with high immigrant populations. In both case studies, we will discuss methods to mitigate allocative harm and to foster equitable outcomes using accountability mechanisms.
Required reading: CalEnviroScreen
Optional reading: SNAP,
Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell University. Her research on algorithmic fairness applies computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health. Koenecke is regularly quoted as an expert on disparities in automated speech-to-text systems. She previously held a postdoctoral researcher role at Microsoft Research and received her PhD from Stanford’s Institute for Computational and Mathematical Engineering. Awards won include the NSF Graduate Research Fellowship and Forbes 30 under 30 in Science.
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CANCELLED CLASS
tl;dr:
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Guest Lecture -- Roshni Sahoo
tl;dr: Roshni Sahoo (Stanford).
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Special Lecture (by Erica, Kenny, Sophie) -- various aspects of recommenders
tl;dr: Erica, Kenny, Sophie Sidhika will discuss various aspects of recommenders
