Professor : Ted Pedersen

• Office Hours : Mon 2:00 - 2:50 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/nfz-nave-nvx

  • Join by phone : dial +1 240-776-5790 and enter this PIN: 381 530 284#

• 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 problem. 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 might need a subscription or there will be ads. 

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. You may be considered absent if you miss a considerable portion of a class period (due to arriving late, leaving early, etc).

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.

Personal Essays

You will write five or six 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 AI Writing Tools

Please do not use automated writing tools like ChatGPT, Grammarly, CoPilot, DeepSeek, etc. at any point in developing work for this class. This includes our Personal Essays, any take home Exams, and your Podcast. Do not use them for brainstorming ideas, do not use them for writing, and do not use them for polishing or correcting your work. 

All of your written work should be composed entirely in Google Docs. You may use the spelling and grammar checking tools provided in the standard version of Google Docs but do not use or add-on anything beyond that for any of our assignments.  

Why such a strict policy? I read all of your essays and exams. I listen to your podcasts. I do not offload the grading of your work on to a teaching assistant or an automated AI tool. I read what you submit carefully, and I would like to hear your own unique voice come through in the work you do for this class. I genuinely enjoy this experience. These tools obscure your voice and restrict your imagination. They make you sound more generic and less like the unique individual that you are. 

Any work that you submit in this class must be uniquely and exclusively written by you. This means no AI Writing Tools, it also means no cutting and pasting or overly close paraphrasing from other sources (which is essentially what these AI tools do, just in a very fancy and elaborate way). If you submit work that you did not uniquely and exclusively create, you may receive a 0 on that assignment.  

Late Work

Personal Essays, any take home exams, and your podcast may be submitted late. However, any late work submitted 1 - 24 hours after the deadline will receive an automatic 20% penalty. Any work submitted 1 - 8 school days after the deadline will receive an automatic 10% penalty per (full) school day. Any work submitted 9 or more school days after the deadline will receive a score of 0.

Example 1 : Suppose a Personal Essay (worth 5 points) is due on Sunday at 11:59 pm. If you turn it in on Monday at 11:00 am (11 hours after the deadline) you would receive a 20% penalty which would be 1 of 5 points, meaning your maximum possible grade would be 4 of 5 points. 

Example 2 : Suppose a Midterm Exam is take home and is due Tuesday at11:59 pm. If you turn it in on Wednesday at 6:00 am (6 hours after the deadline) you would received a 20% penalty which would be 4 of 20 points, meaning your maximum possible grade would be 16 of 20.

Example 3 : Suppose a Personal Essay is due on Wednesday at 11:59 pm. If you turn it in on Tuesday of the next week at 5:00 am this would be considered 3 days after the deadline (Thursday, Friday, and Monday but not Tuesday since that is not yet a full school day). You would lose 20% for Thursday, 10% for Friday, and 10% for Monday so you would receive a 40% penalty which would be 2 of 5 points, meaning your maximum possible grade would be 3 of 5 points.

Example 4 : Any work submitted 9 school days or more after the deadline will receive a score of 0.

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.

Front Covers of our Textbooks