Course: CS 136: Economics and Computation
Course Level: Upper-level undergraduate
Course Description: “This is a class about the digital economy, specifically the interplay between economic thinking and computational thinking as it relates to electronic commerce, incentives engineering, and networked systems. Topics covered vary each year, but include a subset of:
Emphasis will be given to core methodologies, and the class involves the discussion of theoretical, algorithmic and empirical results. We hope to convince you that incentives matter in many computational settings, and that computation matters in many economic ones.(Course description )"
Module Topic: The Ethics of Recommender Systems
Module Author: Heather Spradley
Semesters Taught: Spring 2020
In this module, we discuss whether or not social media sites should use recommender systems that optimize for engagement. We unpack the notions of autonomous belief formation, which refers to forming beliefs for ourselves, and reasonable belief formation, which refers to forming beliefs in accordance with evidence. In order to understand the implications of recommender systems for both kinds of belief formation, we focus on news recommendations on YouTube as a case study. Recent research reveals that YouTube’s recommender system slowly recommends increasingly extreme videos regardless of what was searched for. When recommender systems filter information based on the user’s preferences they appear to: (1) provide the user with information that she herself is interested in; and (2) bias the information to which the user is exposed. We discuss what this means for both the autonomous and reasonable belief formation of the user. As a comparison case, we consider the shipowner from Clifford’s “The Ethics of Belief”, who intentionally ignores relevant evidence and seeks confirming evidence according to his preferences. We discuss the ethical considerations for creators of social media sites like YouTube (which might be the future role of computer science students), as well as for users and content creators (which any student might currently be).
Connection to Course Technical Material:In the lecture just prior to the module, the CS professor presents technical material about recommender systems and leads the class in a discussion of the economic costs and benefits to various kinds of recommender systems. The module starts by asking what might be missed by a narrow economic analysis. We look at ethical, social, and epistemic costs, both to users and creators, challenging what seemed like a simple market exchange—convenience for profit.
Key Philosophical Questions:
Key Philosophical Concepts:
“YouTube, The Great Radicalizer”, by Zeynep Tufekci
This reading quickly introduces students to empirical research on the social and ethical consequences of YouTube’s current recommender system.
“What is Enlightenment?”, by Immanuel Kant
Reading selections of this essay helps students begin to understand the notion of autonomous belief formation, giving a jumping off point for discussion of what appropriate autonomy in belief formation is.
“The Ethics of Belief”, by William Clifford
This reading forms a focal point for the class discussion. It helps introduce students to the notion of reasonable belief formation. It also provides a foil for the case study in the form of a thought experiment about a shipowner who ignores worries that his ship is unseaworthy. The essay explores what it is that the shipowner does wrong and why, which provides a clear comparison to various elements of what might be going wrong when we choose to get out news from recommender systems. We discuss whether getting news in this fashion makes us relevantly like the shipowner, as well as what would need to change if we want to be relevantly unlike the shipowner.
Sample Class Activity:
At the beginning of the session, students are given a list of analogies that link professions to genders, including ballerina/dancer, hostess/bartender, vocalist/guitarist, among others. They are asked to mark those analogies that reflect gender stereotypes. When they finish, the lecturer polls students to find out how they responded to four analogies: one that is clearly stereotypical (homemaker/computer scientist), one that is not (Queen/King), and two that are debatable (Diva/Rockstar, and Interior Designer/Architect). The Embedded Ethics fellow then leads a discussion about the distinctive features of gender stereotypes, which serves as a starting point to discuss the ethical problems raised by gender biases in word embeddings.