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Fall 2020

Algorithms that Affect Lives

(HONR 1310, Section 21, CRN 18175)

 

General Information

·      Instructor: Professor Tina Eliassi-Rad, Khoury College of Computer Sciences

·      Lectures: Mondays & Wednesdays, 2:50 PM – 4:30 PM, online

·      Office hours: Mondays &Wednesdays, 4:30 PM – 5:30 PM, online

o   Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [fa20].

·      Course website on Canvas: https://northeastern.instructure.com/courses/19693

 

Overview

This is a 4-credit freshman honors inquiry seminar. The course covers many of the algorithms that one uses on a daily basis. Examples include algorithms for web search, online auctions, recommendation systems, crowdsourcing, and social networking. We will also discuss algorithms used in high-stakes decisions such as criminal justice, policing, hiring, and loan approvals. Additionally, the course covers individual and collective consequences of using these algorithms such as the loss privacy, algorithmic bias, and ethical dilemmas. This course is on SAIL. A short video describing the course is available here.

 

Prerequisites

This course does not have prerequisites. To excel in it, you do not need previous experience with programming, computer science, statistics, or mathematics.

 

Grading

·      Homework assignments (75% = 5 * 15%)

·      Discussion questions and in-class participation (25%)

 

Textbooks

This course does not have a designated textbook. The readings are assigned in the syllabus (see below). You are expected to have read the assigned material before each lecture. Here are some recommended books (all optional, in chronological order):

·      David Easley and Jon Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press. (free online)

·      Daniel Kahneman. 2013. Thinking Fast and Slow. Farrar, Straus and Giroux.

·      Anand Rajaraman, Jurij Leskovec, and Jeffrey Ullman. 2014. Mining Massive Data Sets. v2.1, Cambridge University Press. (free online) (Errata)

·      Brian Christian and Tom Griffiths. 2016. Algorithms to Live By: The Computer Science of Human Decisions. Henry Holt and Co.

·      Cathy O'Neil. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

·      Meredith Broussard. 2018. Artificial Unintelligence: How Computers Misunderstand the World. The MIT Press.

·      Virginia Eubanks. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.

·      Hannah Fry. 2018. Hello World: Being Human in the Age of Algorithms. W. W. Norton & Company.

·      Safiya Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce. NYU Press.

·      Michael Kearns and Aaron Roth. 2019. The Ethical Algorithm. Oxford University Press.

·      Gary Marcus and Ernest Davis. 2019. Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon.

·      Brad Smith and Carol Ann Brown. 2019. Tools and Weapons: The Promise and The Peril of The Digital Age. Penguin Press.

·      Ruha Benjamin. 2019. Race After Technology: Abolitionist Tools for the New Jim Code. Wiley.

·      Charlton D. McIlwain. 2020. Black Software. Oxford University Press.

 

Recommended Videos (in Chronological Order)

·      We need a “moral operating system” by Damon Horowitz (TEDxSiliconValley, May 2011)

·      Machine intelligence makes human morals more important by Zeynep Tufekci (TEDSummit, Jun 2016)

·      How I'm fighting bias in algorithms by Joy Buolamwini (TEDxBeaconStreet, Nov 2016)

·      Junk News by Newshour Science Series (PBS, May 2018)

·      How we can protect truth in the age of misinformation by Sinan Aral (TEDxCERN, Nov 2018)

·      The Great Hack (Netflix, 2019)

·      Facebook's role in Brexit — and the threat to democracy by Carole Cadwalladr (TED, Apr 2019)

·      Jaron Lanier Fixes the Internet by Jaron Lanier, Produced by Adam Westbrook (New York Times, Sep 2019)

·      In the Age of AI (PBS/Frontline, Nov 2019)

·      The Age of A.I. (YouTube Originals, Dec 2019)

·      The Coded Gaze: Bias in AI by Joy Buolamwini (Digital Life Design Conference, Munich, Germany, Jan 2020)

·      The Social Dilemma (Netflix, 2020)

 

Schedule (Evolving and Subject to Change)

Lec #

Date

Topic

Readings & Notes

1

Wed Sep 9

Overview of the course

 

2

Mon Sep 14

Overview of algorithms, artificial intelligence, and machine learning

What Are Algorithms, and Why Do They Make People Uncomfortable? by Heinzman and Hoffman (How to Geek, April 2019).

Chapter 1 of Artificial Intelligence: A Modern Approach by Russell and Norvig (Prentice Hall, 3rd Edition, 2010)

Machine Learning: Trends, Perspectives, and Prospects by Jordan and Mitchell (Science, 2015)

Science and Data Science by Blei and Smyth (Proceedings of the National Academy of Sciences, 2017)

Top 9 Ethical Issues in Artificial Intelligence by Bossmann (World Economic Forum, Oct 2016)

3

Wed Sep 16

Overview of networks and graphs

Chapter 1 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

Chapter 2 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

4

Mon Sep 21

Pros and cons of the algorithm age

Code-Dependent: Pros and Cons of the Algorithm Age by Rainie and Anderson (Pew Research Center, Feb 2017) (Read to the end of Page 18)

5

Wed Sep 23

Web search

Chapter 13 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

Chapter 14 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010) (skip Section 14.6)

Optional: Chapter 5 of Mining Massive Datasets by Rajaraman, Leskovec, and Ullman (Cambridge University Press, 2014)

6

Mon Sep 28

Online auctions

Chapter 15 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010) (skip Section 15.9)

Optional: Chapter 8 of Mining Massive Datasets by Rajaraman, Leskovec, and Ullman (Cambridge University Press, 2014)

7

Wed Sep 30

Societal impact of Web search and online auctions

Discrimination in Online Ad Delivery by Latanya Sweeney (Communications of the ACM, May 2013) – Additional informational website

Bias on the Web by Ricardo Baeza-Yates (Communications of the ACM, Jun 2018)

Google Autocomplete Still Makes Vile Suggestions by Issie Lapowsky (Wired, Feb 2018)

The Secretive Company That Might End Privacy as We Know It by Kashmir Hill (NY Times, Jan 2020)

Optional: Auditing Autocomplete: Suggestion Networks and Recursive Algorithm Interrogation by Robertson et al (ACM Web Science 2019)

8

Mon Oct 5

Recommender systems

Chapter 1 of Recommender Systems: The Textbook by Aggarwal (Springer, 2016)

The Long Tail by Chris Anderson (Wired, Oct 2004)

Optional: Chapter 9 of Mining Massive Datasets by Rajaraman, Leskovec, and Ullman (Cambridge University Press, 2014)

9

Wed Oct 7

Recommender systems (continued)

The Million Dollar Programming Prize by Bell et al. (IEEE Spectrum, May 2009)

Rise of the Netflix Hackers by Demerjian (Wired, Mar 2007)

Up Next: A Better Recommendation System by DiResta (Wired, Apr 2018)

10

Mon Oct 12

No class

Indigenous People’s Day

11

Wed Oct 14

Content ranking &
moderation in a
partisan world

Guest lecturer:
Mr. Ronald Robertson

Auditing Partisan Audience Bias within Google Search by Ronald Robertson et al. (Proceedings of the ACM Human-Computer Interaction, Jun 2018)

Bias Misperceived: The Role of Partisanship and Misinformation in YouTube Comment Moderation by Jiang, Robertson, and Wilson (Proceedings of the International AAAI Conference on Weblogs and Social Media, Jun 2019).

12

Mon Oct 19

Social bots

Guest lecturer:
Prof. Onur Varol

The Rise of Social Bots by Ferrara et al (Communications of the ACM, Jul 2016)

Deception Strategies and Threats for Online Discussions by Varol and Uluturk (arXiv, Jun 2019)

13

Wed Oct 21

Crowdsourcing

&

Wikipedia

The Wisdom of Crowds: Introduction Chapter (Parts I through V) by James Surowiecki (Anchor, 2005)

Human Computation: A Survey and Taxonomy of a Growing Field by Alexander Quinn and Benjamin Bederson (Proceedings of ACM CHI Conference, May 2011)

Wikipedia on Wikipedia

Governance in Social Media: A Case Study of the Wikipedia Promotion Process by Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg (Proceedings of the International AAAI Conference on Weblogs and Social Media, May 2010)

Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes by Srijan Kumar, Robert West, Jure Leskovec (Proceedings of the 25th International Conference on World Wide Web, Apr 2016)

Wikipedia’s “Constitutional Crisis” Pits Community Against Foundation by Stephen Harrison (Slate, Jul 2019)

14

Mon Oct 26

The gig economy

Gig Work, Online Selling and Home Sharing by Pew Research Center (Nov 2016)

The Supreme Court of California, Decision on Workers Classified as Employees or as Independent Contractors (Apr 2018)

Digital Labor Platforms and the Future of Work: Towards Decent Work in the Online World (Chapters 1, 2, and 6) by Janine Bergs et al. (International Labor Organization, 2018)

15

Wed Oct 28

Facial recognition

Case Study: Facial Recognition by Hilary Cohen, Rob Reich, Mehran Sahami, and Jeremy Weinstein (Ethics, Technology, and Public Policy at Stanford University, 2018)

The Secretive Company That Might End Privacy as We Know It by Kashmir Hill (New York Times, Jan 2020)

Clearview’s Facial Recognition App Has Been Used By The Justice Department, ICE, Macy’s, Walmart, And The NBA by Ryan Mac, Caroline Haskins, and Logan McDonald (BuzzFeed News, Feb 2020)

Here’s the File Clearview AI Has Been Keeping on Me, and Probably on You Too by Anna Merlan (Vice, Feb 2020)

16

Mon Nov 2

Use of algorithms in law enforcement and judicial system

Case Study: Algorithmic Decision Making and Accountability by Hilary Cohen, Rob Reich, Mehran Sahami, and Jeremy Weinstein (Ethics, Technology, and Public Policy at Stanford University, 2018)

Machine Bias by Angwin, Larson, Mattu, and Kirchner (ProPublica, May 2016)

Algorithms, Correcting Biases by Cass R. Sunstein (Social Research: An International Quarterly, 2019)

Combatting Police Discrimination in The Age of Big Data by Sharad Goel et al. (New Criminal Law Review, 2017)

17

Wed Nov 4

Use of algorithms in law enforcement and judicial system

The Accuracy, Fairness, and Limits of Predicting Recidivism by Dressel and Farid (Science Advances, Jan 2018)

The Limits of Human Predictions of Recidivism by Zhiyuan “Jerry” Lin et al (Science Advances, Feb 2020)

Impact of Risk Assessment on Judges’ Fairness in Sentencing Relatively Poor Defendants by Jennifer Skeem, Nicholas Scurich, and John Monahan (Law and Human Behavior, 2020)

18

Mon Nov 9

Misinformation online

Misinformation and Its Correction: Cognitive Mechanisms and Recommendations for Mass Communication by Briony Swire and Ullrich Ecker (In. B. Southwell, E. A. Thorson, and L. Sheble. (Eds), Misinformation and Mass Audiences. Austin, TX: University of Texas Press, 2018)

They Might Be a Liar But They’re My Liar: Source Evaluation and the Prevalence of Misinformation by Briony Swire-Thompson et al. (In Political Psychology, 2019)

19

Wed Nov 11

No class

Veterans’ Day

20

Mon Nov 16

Online social networking

Chapter 3 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

Chapter 4 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

Chapter 5 of Networks, Crowds, and Markets by Easley and Kleinberg (Cambridge University Press, 2010)

Facebook Ads Can Still Discriminate Against Women and Older Workers, Despite a Civil Rights Settlement by Ava Kofman and Ariana Tobin (ProPublica, Dec 2019)

21

Wed Nov 18

Facebook ads

Guest lecturer:
Prof. Alan Mislove

Investigating Sources of PII Used in Facebook’s Targeted Advertising by Venkatadri et al. (Proceedings on Privacy Enhancing Technologies, Jul 2019)

Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes by Ali et al. (Proceedings of the ACM on Human-Computer Interaction, Nov 2019)

22

Mon Nov 23

Use of algorithms in hiring

&

Use of algorithms in healthcare & medicine

The Legal and Ethical Implications of Using AI in Hiring by Ben Dattner et al. (Harvard Business Review, Apr 2019)

Can Social Media Lead to Labor Market Discrimination? Evidence from a Field Experiment by Matthieu Manant, Serge Pajak, and Nicolas Soulié (Journal of Economics & Management Strategy, Aug 2018)

Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (US Food and Drug Administration, Apr 2019)

Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations by Ziad Obermeyer et al. (Science, Oct 2019)

--

Wed Nov 25

No class

Thanksgiving Break

23

Mon Nov 30

Web privacy and tracking

Guest lecturer:
Prof. Christo Wilson

Online Tracking: A 1-million-site Measurement and Analysis by Englehardt and Narayanan (Extended version of a paper that appeared in Proceedings of the 23rd ACM Conference on Computer and Communications Security, Oct 2016) –
Associated informational website

(Un)informed Consent: Studying GDPR Consent Notices in the Field by Utz et al. (Proceedings of the 26th ACM Conference on Computer and Communications Security, Nov 2019)

Panoptispy: Characterizing Audio and Video Exfiltration from Android Applications by Pan et al. (Proceedings on Privacy Enhancing Technologies, Jul 2018)

24

Wed Dec 2

Use of algorithms in credit scoring & loan approvals

Credit Denial in the Age of AI by Aaron Klein (Brookings, Apr 2019)

Fairness and Machine Learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan

25

Mon Dec 7

Privacy

Differential Privacy: A Primer for a Non-technical Audience by Kobbi Nissim et al. (Mar 2017)

Case Study: Private Platforms by Hilary Cohen, Rob Reich, Mehran Sahami, and Jeremy Weinstein (Ethics, Technology, and Public Policy at Stanford University, 2018)

26

Wed Dec 9

What should we as citizens demand in the age of AI?

A Blueprint for a Better Digital Society by Lanier and Weyl (Harvard Business Review, Sep 2018)

 

Notes, Policies, and Guidelines

·      You are expected to have read the assigned material before each lecture.

·      We will use Northeastern’s Canvas for announcements, assignments, and your contributions.

·      Late assignments are not accepted for credit. If you have a verifiable medical condition or other special circumstances, email eliassi [at] ccs [dot] neu [dot] edu as soon as possible.

·      When emailing eliassi [at] ccs [dot] neu [dot] edu about the course, begin the subject line with [fa20].

·      Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!