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Fall 2021

Computer Science Research Seminar

(CS 4950 – CRN 14685 – 1 credits)

General Information

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

·      Lectures: Thursdays, 2:50 PM – 3:55 PM, Ryder Hall 293

·      Office hours: Mondays & Thursdays, 4:45 PM – 5:30 PM, Online via Zoom

o   Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [fa21 cs4950].

·      Course website on Canvas: https://northeastern.instructure.com/courses/86658

Overview

This one-credit undergraduate seminar provides an overview of research in machine learning and its impact on our society. Students will read and present recent scientific literature and write annotated bibliographies.

Format

Students will be divided into teams of three. Each week, one team will present the week's reading to the class. This involves preparing slides (in either PowerPoint, Keynote, or Google Slides) and uploading the PDF of the slides into Canvas. Each member of the other teams will prepare his/her own annotated bibliography (which must be turned in before class in PDF format into Canvas). The team giving the presentation is not required to prepare an annotated bibliography.

Prerequisites

CS 3950: Introduction to Computer Science Research.

Grading

·      Attendance and participation: 40%

·      Annotated bibliographies for weekly paper reviews: 30%

·      Weekly paper presentations: 30%

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.

Schedule (Evolving and Subject to Change)

#

Date

Topic

Readings & Notes

Presenter

1

Sep 9

Overview & Logistics

 

Tina Eliassi-Rad

2

Sep 16

The Big Picture

Michael I. Jordan, Tom M. Mitchell. Machine Learning: Trends, Perspectives, and Prospects, Science, 349(6245): 255-260, 2015.

David M. Blei, Padhraic Smyth. Science and Data Science, Proceedings of the National Academy of Sciences, 114(33): 8689-8692, 2017.

Optional: Tom M. Mitchell. The Discipline of Machine Learning. Technical Report CMU-ML-06-108, Carnegie Mellon University, Pittsburgh, PA, July 2006.

Team 1

3

Sep 23

The Two Cultures

Leo Breiman. Statistical Modeling: The Two Cultures, Statistical Science, 16(3): 199-215, 2001.

Jake Hofman, Duncan Watts, Susan Athey, et al. Integrating Explanation and Prediction in Computational Social Science. Nature 595, 181-188, 2021.

Team 2

4

Sep 30

Algorithmically Infused Societies

David Lazer, Eszter Hargittai, Deen Freelon, et al. Meaningful Measures of Human Society in the Twenty-first Century. Nature 595, 189-196, 2021.

Claudia Wagner, Markus Strohmaier, Alexandra Olteanu, et al. Measuring Algorithmically Infused Societies. Nature 595, 197-204, 2021.

Team 3

5

Oct 7

Gender Shades

Joy Buolamwini, Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency (FAT’18), 2018: 77-91.

Team 4

6

Oct 14

Stochastic Parrots

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT’21), 2021: 610–623.

Tina Eliassi-Rad

7

Oct 21

Generative Adversarial Networks

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al. Generative Adversarial Networks. Communications of the ACM, 63(11): 139-144, 2020.

Team 2

8

Oct 28

Problems with Explaining Black Box Models

Dylan Slack, Sophie Hilgard, Emily Jia, et al. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society (AIES’20), 2020: 180-186.

Optional: Cynthia Rudin. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions. Nature Machine Intelligence, 1: 206–215, 2019.

Team 3

9

Nov 4

Causality

Judea Pearl. The Seven Pillars of Causal Reasoning with Reflections on Machine Learning. Communications of the ACM, 62(3): 54-60, 2019.

Team 4

--

Nov 11

No class

Veterans Day

 

10

Nov 18

Impossibility Results

Alexandra Chouldechova. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data 5(2): 153-163, 2017.

Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan. Inherent Trade-Offs in the Fair Determination of Risk Scores. In Proceedings of the 8th Innovations in Theoretical Computer Science Conference (ITCS’17), 43: 1-23, 2017.

Optional:

Branden Fitelson. A Decision procedure for Probability Calculus with Applications. Review of Symbolic Logic 1: 111-125, 2008.

Tina Eliassi-Rad, Branden Fitelson. Exploring Impossibility Results for Algorithmic Fairness Using PrSAT. Technical Report, Northeastern University, Boston, MA, March 2021.

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al. A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6): 1-35, 2021.

Tina Eliassi-Rad

--

Nov 25

No class

Thanksgiving

 

11

Dec 2

Machine Learning as a Software Engineering Enterprise

Charles Isbell. You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise. NeurIPS, 2020. (The video starts at 29 minutes).

Optional: Julia Bossman. Top 9 Ethical Issues in Artificial Intelligence. World Economic Forum, Oct 2016.

N/A

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 [fa21 cs4950].

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