Spring 2020

Algorithms that Affect Lives

(HONR 1310, Section 10, CRN 37840)


General Information

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

·      Lectures: Mondays & Wednesdays, 2:50 PM – 4:30 PM, Snell Library, Room 043

·      Office hours: Mondays, 4:45 PM – 6:00 PM, 177 Huntington Ave, Room 1023

o   Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [sp20].



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.



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



·      Homework assignments (45% = 3 * 15%)

·      Semester-long project (35%)

·      In-class participation (20%)



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.


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)


Schedule (Evolving and Subject to Change)

Lec #



Readings & Notes


Mon Jan 6

Overview of the course



Wed Jan 8

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)


Mon Jan 13

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)


Wed Jan 15

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)


Mon Jan 20

No class

Martin Luther King Jr. Day


Wed Jan 22

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)


Mon Jan 27

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)


Wed Jan 29

Societal impact of Web search and online auctions

Discrimination in Online Ad Delivery by Latanya Sweeney (SSRN, Jan 2013)

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)

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


Mon Feb 3

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)


Wed Feb 5

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)


Mon Feb 10

Social bots
(Guest lecturer:
Dr. 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)


Wed Feb 12

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).


Mon Feb 17

No class

President’s Day


Wed Feb 19




Mon Feb 24




Wed Feb 26

Online social networking



Mon Mar 2

No class

Spring Break


Wed Mar 4

No class

Spring Break


Mon Mar 9

Online social networking



Wed Mar 11

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)


Mon Mar 16

Facial recognition



Wed Mar 18

Speech recognition



Mon Mar 23

Use of algorithms in judicial system



Wed Mar 25

Use of algorithms in law enforcement



Mon Mar 30

Use of algorithms in healthcare & medicine



Wed Apr 1

Use of algorithms in hiring



Mon Apr 6

Use of algorithms in credit scoring & loan approvals



Wed Apr 8

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)


Mon Apr 13

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



Notes, Policies, and Guidelines

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

·      We will use Northeastern’s Blackboard 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 [sp20].

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