Fall 2023 198:345
Algorithms in Society

Tuesdays-Thursdays, 3:50-5:10pm, BRR-4071, Livingston Campus

Instructor: Amélie Marian
Office hours: Thursdays 2-3pm CoRE 324 

TA: Abraham (Yehuda) Gale
Office hours: Mondays 5-6pm CoRE 342

Announcements

Class Announcements will be posted via Canvas. If you are registered for the course and do not see the course on Canvas (once the semester has started), please contact the instructor.

9/4: Recitations will start on 9/13


Course Description

This class will introduce students to various types of algorithms used for real-world decisions in a wide range of applications: public policy, health, e-commerce, justice. We will discuss the social impacts of algorithmic decisions, their legal implications, and the need for transparency and accountability in algorithmic decision-making. The class will cover topics such as bias in data and processes, fairness, game theory, and trust, as they relate to algorithms. 

The class will focus on hands-on projects and discussions that will give students an understanding of a participatory design, and include them as stakeholders of the algorithms they are implementing. 

The course will give students a critical understanding of the often competing goals of efficiency and optimization with those of transparency, accountability and fairness, which are necessary for trust and widespread adoption of automated decision systems. It will teach them to consider all stakeholders of a decision process, as well as the broader impacts of their algorithmic designs. 

Recitations

Recitations will consist of labs, using Python Jupyter notebooks, that alternate between implementations of the algorithms discussed in class on case studies, and building components to use in the class projects.

Section 1: Wednesday 4:05-5:00pm Livingston TIL-105

Section 2: Wednesday 5:55-6:50pm Busch SEC 207

Prerequisites

CS210 or CS112 and knowledge of Python, or by permission of instructor.


Grading

Grading will be based on 3 projects, (60%), recitation labs and participation activities (20%), and a midterm (20%) . 


Readings

Readings will be posted on Canvas.


Schedule (To be Confirmed)


Date

Topics

Class Activities and Assignments

Tue September 5
Thu September 7


Introduction.
Overview of Operation Research, Computational Economics and Computational Social Science


Tue September 12
Thu September 13
Tue September 19
Thu September 21 


Resource Allocation and Matching.
Mixed-Integer Linear Programming. Gale-Shapley Algorithm. Top-trading Cycles. Stable Roommates. 


Activity: Creating Project teams.


Tue September 26
Thu September 28 


School Matching. Real-life matchings.

Priorities, set-aside, Fairness

Lifecycle of the Algorithms, Errors




Tue October 3
Thu October 5


Design of objective functions
Incentives
Legal aspects of automated decisions (rules and audits) 



Tue October 10
Thu October 12

Voting Algorithms. Ranked Choice Voting.
Arrow's impossibility theorem.


Activity: Voting for Candy.



Tue October 17
Thu October 19 

 

Redistricting and Gerrymandering


Tue October 24

Privacy and Anonymization 


Activity: De-anonymizing data.

Thu October 26 

Midterm review 

Tue October 31 

Midterm Exam (TENTATIVE)

Thu November 1

Tue November 7
Thu November 9
Tue November 14

Auctions 


Activity: Class Auction

Thu November 16
Tue November 21 

Predictions
Predictive Bias 


Thanksgiving break 

Tue November 28
Thu November 30


Participatory Decision-Making
Participatory budgeting 


Activity: Participatory Grading.

Tue December 5
Thu December 7 


Lotteries
Lottocracy, Sortition
Fair Division 


Tue December 12 

Wrapping up. 



Course Policies

Attendance and Participation 

This course will heavily rely on course discussions and activities. Students are expected to be present and participate in the discussions, but should prioritize their health and safety.

If you cannot attend class, please let the instructor know. Students will not be penalized for notified absences. 


Disability Accommodations 

Students in need of disability accommodations to register for accommodations and consult the policies and procedures of the Office of Disability Services website: https://ods.rutgers.edu 


Civility 

Some topics covered in the class will relate to ethics and fairness in automated decision systems, as well as the need for diverse viewpoints when designing algorithms. Disagreements on some of these issues are expected, and part of the learning experience. Students are expected to behave in a respectful manner towards everyone in the course, to ensure that all participants in the class feel welcome and supported. 


Academic Integrity Policy 

Rutgers University takes academic dishonesty very seriously. By enrolling in this course, you assume responsibility for familiarizing yourself with the Academic Integrity Policy and the possible penalties (including suspension and expulsion) for violating the policy. As per the policy, all suspected violations will be reported to the Office of Student Conduct. Academic dishonesty includes (but is not limited to):

If you are ever in doubt, consult your instructor.

Please familiarize yourself with the University Academic Intgrity Policy http://nbacademicintegrity.rutgers.edu/


Student Support and Mental Wellness 

In the last few years, we have all been going through a lot, individually and together. It is important to acknowledge that events and circumstances outside of the classroom can impact our ability to be present and engaged at any given moment. At Rutgers, we are focused on the whole student. If, at any point, you experience anything impacting your performance or ability to participate in this class, please reach out to me. Please also see the academic, health, and mental wellness resources on the syllabus as well as others searchable at https://success.rutgers.edu/ for further support.


Additional support resources: