Professor : Dr. Ted Pedersen

• Office Hours : Mon 3:00 - 3:50 pm (on Google Meet), Tue 2:30 - 3:20 (HH 309), and Thur 2:30 - 3:20 (HH 309) 

• Office Hour Google Meet link : https://meet.google.com/atm-ghsh-jup

• email : tpederse@d.umn.edu (however, please use the Canvas discussion for general course questions)

There is no TA for this class.

Course Description (From Catalog)

The algorithms that computers run are often believed to be neutral and impartial. This leads to the assumption that their results are fair, just, or even benevolent. This class explores a different reality, where algorithms obscure, replicate and amplify racism, and can even generate new forms of racial injustice. This class will first develop a basic understanding of race and algorithms. Thereafter we will consider case studies from both the past and present where algorithms have furthered racism and racial oppression. We will consider examples from social media, search engines, health care, criminal justice, education, and the workplace. We will conclude by discussing alternative futures where resistance and co-creation offer the potential to lessen algorithmic harms. This class assumes no background in computing or algorithms and is appropriate for students in any major.

Course Outcomes (SLO = Student Learning Outcome) 

SLO 1: Students will explain present-day racial inequalities or other avenues of racial oppression by applying historical, socio-cultural, institutional, structural and/or all of the aforementioned (i.e., systemic) ways of thinking. You will demonstrate this by being able to :

• Identify examples of racism from the experiences of others or from your own lived experience. 

• Explain how examples of racism today are connected to racism found in earlier periods in the history of the United States.

SLO 2:  Students will apply historical, socio-cultural, institutional, structural and/or all of the aforementioned (i.e., systemic) ways of thinking to discipline-specific real cases about race, power, and justice. You will demonstrate this by being able to : 

• Describe specific real world cases where algorithms and computing perpetuated or amplified racism. 

• Analyze specific real world cases where algorithms and computing perpetuated or amplified racism.

Class Schedule

The Class Schedule is where you can find our schedule for assignments and class meetings. Please check this page frequently as it is updated throughout the semester 

Required Textbooks

There are two required textbooks. We will read and discuss both of these books, so it is important to have a copy of each. The good news is that both are modestly priced - the first is available for less than $15 and the second for less than $30. You may want to shop around to find the best price. Note that UMD Bookstore has both books available at reasonable prices. Or, if you are participating in Course Works you should receive a copy of each book through that program. You can see the front covers of our textbooks at the bottom of this page.

1) So You Want To Talk about Race 

By Ijeoma Oluo

ISBN : ‎ 978-1580058827

Publisher ‏ : ‎ Seal Press (September 24, 2019) 

2) More than a Glitch : Confronting Race, Gender, and Ability Bias in Tech

By Meredith Broussard

ISBN : 978-0262047654

Publisher: The MIT Press (March 14, 2023) 

Please make sure you bring your copy of whatever book we are reading to class as we are likely to refer to it during our discussions.

Required Podcast

We will listen to the The 1619 Podcast as a part of this course. This consists of 5 episodes, each approximately 30-40 minutes long with a total listening time of about 2 1/2 hours. The best way to listen is to download the mp3 files from our Canvas Files (from the directory "The 1619 Podcast"). If you listen via the site above or a podcast platform (eg Apple, Spotify) you will need a New York Times subscription and you do not need to get one of those for this class.

Prerequisites

None

Grading Basis

• Participation : 20%

• Personal Essays : 20%

• Podcast : 20%

• Midterm Exam 1 (based on Week 1 - 5, roughly) :10%

• Midterm Exam 2 (based on Week 6 - 10, roughly) : 10%

• Final Exam (based on material week 1 - 15) : 20%

Participation

Your participation in class is important for your learning and that of your classmates. Participation is measured via engaged attendance, where you are not just present but also willing and able to engage with the class in a constructive fashion. Please make sure you understand and follow our Participation Norms for Small Group and Class Discussions. 

You are allowed 3 unexcused absences. After that each additional unexcused absence results in a 1/2 point deduction in your participation grade for the semester (of a possible 10). For example, after 4 unexcused absences your participation grade for the semester would be 9.5, after 5 it would be 9, and so on. Excused absences do not count against this limit, and are defined by the UMD policy on excused absences. You will be marked late if you arrive noticably late or depart early. Two lates will be considered one absence. 

Personal Essays

You will write five Personal Essays connected to our readings or class discussions during the semester. 

Midterm and Final Exams

We will have two Midterm exams, one around Week 6 and the other around Week 11. We will also have a Final exam during finals week.

Podcast 

You will create a podcast this semester. See Podcast Guidelines for more details.

No Use of Automated Writing / AI Tools

You may not use automated writing / AI tools like Gemini, CoPilot, ChatGPT, Grammarly, Claude, DeepSeek, etc. at any point in developing any work you will submit for a grade in this class. The only exception is that you may use the default spelling and grammar checkers found in Google Docs. 

The purpose of this policy is to encourage authentic engagement with our course materials in a way that improves our critical thinking skills and our ability to  organize and present information we have learned in an engaging and personal way. 

Late Work

Personal Essays, any take home exams, and your podcast may be submitted late. However, there is an automatic lateness penalty of 10% for every full day that passes after the deadline. Since a full day must pass before the lateness penalty is triggered that means there is a 24 hour grace period after each deadline where you can still submit without penalty. No further submissions are possible for any work 10 days after the deadline. 

Grading Scale

• 93 - 100 = A, 90 - 92 = A-

• 87 - 89 = B+, 83 - 86 = B, 80 - 82 = B -

• 77 - 79 = C+, 73 - 76 = C, 70 - 72 = C -

• 67 - 69 = D+, 60 - 66 = D

• 0 - 59 = F

UMD-wide syllabus policies

For anything not mentioned above, this class will follow the policies described in the UMD-wide syllabus.