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INFO 1260 / CS 1340: Choices and Consequences in Computing
Jon Kleinberg and Karen Levy
Spring 2021

Course description

Computing requires difficult choices that can have serious implications for real people. This course covers a range of ethical, societal, and policy implications of computing and information. It draws on recent developments in digital technology and their impact on society, situating these in the context of fundamental principles from computing, policy, ethics, and the social sciences. A particular emphasis will be placed on large areas in which advances in computing have consistently raised societal challenges: privacy of individual data; fairness in algorithmic decision-making; dissemination of online content; and accountability in the design of computing systems. As this is an area in which the pace of technological development raises new challenges on a regular basis, the broader goal of the course is to enable students to develop their own analyses of new situations as they emerge at the interface of computing and societal interests.

A more extensive summary of the material can be found in the overview of course topics at the end of this page.

Course staff

  • Instructors:
    • Jon Kleinberg jmk6
    • Karen Levy kl838
  • TA staff:
    • Ishaan Bakhle ib257
    • Christian Baran ckb74
    • Chelsie Beavers cdb95
    • Daphne Blakey dab487
    • Katy Blumer keb297
    • Christine Chau cac469
    • Jenny Fu xf89
    • Aadi Kulkarni ak2354
    • Fatima Mahmoud fam76
    • Haley Mathews hcm58
    • Kaela Olsen kzo2
    • Manish Raghavan mr959
    • Claire Ramirez crr95
    • Jennifer Reed jhr254
    • Jack Schluger jes543
    • Walter Szczech wls64
    • Emily Tseng et397
    • Cyrus West cjw297
    • Sterling Williams-Ceci scw222
    • Kimberly Williamson khw44
    • Stephen Yang sy364
    • Yanchen Zhan yz366
    • Karen Zhou kz265
    • Olivia Zhu oz28


Office Hours

Zoom links for the office hours are listed in schedule of all Zoom events on the Canvas page.

  • Monday 4pm-5pm: Haley Mathews, Claire Ramirez
  • Tuesday 9am-10am: Yanchen Zhan, Jenny Fu
  • Tuesday 10am-11am: Karen Levy
  • Tuesday 1pm-2pm: Jon Kleinberg
  • Tuesday 4pm-5pm: Kaela Olsen, Christian Baran
  • Wednesday 10am-11am: Stephen Yang, Jack Schluger
  • Wednesday 11am-12pm: Olivia Zhu, Christine Chau
  • Wednesday 5pm-6pm: Ishaan Bakhle, Chelsie Beavers
  • Thursday 2pm-3pm: Cyrus West, Aadi Kulkarni
  • Thursday 5pm-6pm: Katy Blumer, Jenn Reed
  • Thursday 8pm-9pm: Walter Szczech, Karen Zhou
  • Thursday 10pm-11pm: Manish Raghavan, Kimberly Williamson
  • Friday 10am-11am: Sterling Williams-Ceci, Fatima Mahmoud
  • Friday 11am-12pm: Emily Tseng, Daphne Blakey


There are no formal pre-requisites for this course. It is open to students of all majors.

For Information Science majors, the course may substitute for INFO 1200 to fulfill major requirements. Students may receive credit for both INFO 1200 and INFO 1260, as the scopes of the two courses are distinct.


  • Homework: 6 assignments, each worth 12.5% of the course grade. You will be responsible for 6 homework assignments, which must be submitted via the class Canvas page by the start of class on the day they are due. Each assignment will consist of a variety of different types of questions, including questions that draw on mathematical models and quantitative arguments using basic probability concepts, and questions that draw on social science, ethics, and policy perspectives.

    The planned due dates for the homework assignment are as follows: HW 1 (due 2/26), HW 2 (due 3/19), HW 3 (due 4/2), HW 4 (due 4/16), HW 5 (due 4/30), HW 6 (due 5/14).

  • Final Exam: take-home, worth 25% of the course grade. The final exam for the course will be a take-home exam that you will have several days to complete. More information about the final exam will be available later in the semester.

Academic Integrity

You are expected to observe Cornell’s Code of Academic Integrity in all aspects of this course.

You are allowed to collaborate on the homework to the extent of formulating ideas as a group. However, you must write up the solutions to each homework completely on your own, and understand what you are writing. You must also list the names of everyone with whom you discussed the homework. Collaboration is not allowed on the final exam.

Among other duties, academic integrity requires that you properly cite any idea or work product that is not your own, including the work of your classmates or of any written source. If in any doubt at all, cite! If you have any questions about this policy, please ask a member of the course staff.


Overview of Topics

(Note on the readings: The readings listed in the outline are also available on the class Canvas page, and for students enrolled in the class, this is the most direct way to get them. The links below are to lists of publicly available versions, generally through Google Scholar. We will add readings for each section of the course prior to starting that section.)

  • Course introduction. We begin by discussing some of the broad forces that laid the foundations for this course, particularly the ways in which applications of computing developed in the online domain have come to impact societal institutions more generally, and the ways in which principles from the social sciences, law, and policy can be used to understand and potentially to shape this impact.
    • Course mechanics
    • Overview of course themes
      • The relationship of computational models to the world
      • The on-line world changes the frictions that determine what’s easy and what’s hard to do
      • The contrast between policy challenges and implementation challenges
      • The contrast between “Big-P Policy” and “Little-P policy”
      • The non-neutrality of technical choices
      • The challenge of anticipating the consequences of technical developments
      • Digital platforms can create diffuse senses of responsibility and culpability
      • Computing as synecdoche: the problem in computing serves acts as a mirror for the broader societal problem
      • Issues with significant implications for people’s everyday lives
  • Content creation and platform policies. One of the most visible developments in computing over the past two decades has been the growth of enormous social platforms on the Internet through which people connect with each other and share information. We look at some of the profound challenges these platforms face as they set policies to regulate these behaviors, and how those decisions relate to longstanding debates about the values of speech.
  • Data collection, data aggregation, and the problem of privacy. Computing platforms are capable of collecting vast amounts of data about their users, and can analyze those data to make inferences about users' characteristics and behaviors. Data collection and analysis have become central to platforms' business models, but also present fundamental challenges to users' privacy expectations. Here, we describe the difficult choices that platforms must make about how they gather, store, combine, and analyze users' information, and what social and political impacts those practices can have.
    • Privacy as a fundamental concept:
    • Digital Data and the Limits of Anonymization.
    • Constitutional right to privacy
    • Privacy in non-constitutional law
    • Differential privacy
    • Privacy from whom?
    • Collection and use of data
    • Secure communication
    • Surveillance of work and workers
      • Scientific management and the history of workplace observation
      • Legal protections
      • New frontiers of workplace data collection
      • Readings (4/12):
    • Experimentation in the design of user-facing algorithms. When computing platforms evaluate new features and functionality, a common paradigm is to try out different versions and measure user response. This means that platforms are engaging in long-running sets of experiments with human participants, and so it is important to ask how principles developed for reasoning about such experiments more broadly should be applied in the online domain.
      • Why do we do experiments?
        • Establishing causality
        • Contrasts with observational data
        • Small effects and how they lead to big differences for on-line platforms
      • Models for the design of experiments
        • Bayesian networks
        • Principles and practice of A/B testing
        • Spillover between individuals
        • Explore/exploit trade-offs
      • Research ethics frameworks
        • The principle of equipoise
        • Evaluating the duty to experiment
        • The Belmont and Menlo reports
        • IRBs and human subjects research
      • Critiques of experiments
        • Aversion from experimental subjects
        • Limitations in relying on experiments
    • Algorithmic decision-making, fairness, and bias. Algorithms trained using machine learning are increasingly being deployed to evaluate people in a a range of different contexts, including employment, education, credit, healthcare, and the legal system. We consider the ways in which these kinds of algorithmic evaluations may incorporate biases that are present in the human decisions they're trained on, and what mechanisms might be available to counteract these forms of bias.
      • Domains where algorithms are making decisions about people
        • Credit
        • Hiring
        • Education
        • Healthcare
        • Criminal justice
      • Inequality in philosophy and social science
        • Social stratification, structural embeddedness, intersectionality
        • Historical perspectives on structural discrimination.
        • Empirical research on implicit bias
        • Empirical research on intergenerational mobility
        • Distributive justice, equal opportunity, Rawlsian principles for choosing objective functions in allocation
      • Discrimination law
        • Disparate treatment vs. disparate impact.
        • Theories of disparate impact and affirmative action
        • Differential levels of regulation of different domains (including Title VII, ADA, 14th Amendment, FCRA)
      • Sources of bias in algorithms
        • Choice of features
        • Choice of objective function
        • Training procedures, accuracy disparities
        • Using constructs as prediction targets
      • Algorithmic fairness guarantees
        • Formal basis
        • False positives and false negatives
        • Inherent trade-offs between different guarantees
        • Evaluating the connections between fairness and causality
      • Feedback loops
        • Self-fulfilling predictions
        • The formation of stereotypes
        • Long-term impacts of interventions
      • Fairness in commerce
        • Pricing and selling
        • Advertising
      • Critiques of fairness
        • Power and its reinforcement
        • Automating inequality
    • Computing in the physical world. A number of the important applications of computing are embedded in the physical world -- robots, autonomous vehicles, and networks of sensors are some basic instances. Many of the issues we consider in the course have direct analogues in physical settings; in the final lecture of the class, we'll give a brief overview of how these physical manifestations reflect important dimensions of the questions we've been studying.
      • Consequences of automating physical processes
        • A taxonomy of cyber-physical systems, including robots, drones, and sensors
        • Autonomous vehicles
        • Autonomous weapons and their relation to theories of warfare
        • The role of sensing in surveillance
        • The energy cost of large-scale computation