Algorithms & Bias
Resources for learning about how tech and AI algorithms can have bias
Algorithm: “A mathematical process to solve a problem using a finite number of steps.” via
Bias: “A tendency, inclination, or prejudice toward or against something or someone.” via PsychologyToday
Here are some resources to gain a better understanding of how technology can be biased too, mainly within algorithm bias in Artificial Intelligence.
Includes: articles, books, films, videos, real stories taken from social media, and more!
Will be continuously updated for my own reference also for others interested in learning more about it.
Credit: Wall Street Journal
1. Why algorithms can be racist and sexist by Rebecca Heilweil
“One of the reasons algorithmic bias can seem so opaque is because, on our own, we usually can’t tell when it’s happening (or if an algorithm is even in the mix).”
2. Algorithms are Racist. Now What? by Jennifer Ulloa via
“One problem with predictive software is that it often places a camera on poorer communities of color.”
3. ExamSoft’s remote bar exam sparks privacy and facial recognition concerns by Khari Johnson
“On top of common concerns that come with taking a state bar exam — like passing the test — Caton has to deal with challenges presented by facial recognition technology.”
4. How Racial Bias in Tech Has Developed the “New Jim Code” by Sarah E. Bond and Nyasha Junior via hyperallergic
“When machine learning and the use of computers are emphasized in artistic research, in reconstructions, or in beauty contests, viewers often take the results to be scientific, objective, and unbiased. But they are not.”
1. The Social Dilemma via Netflix
“From the creators of Chasing Ice and Chasing Coral, The Social Dilemma blends documentary investigation and narrative drama to disrupt the disrupters, unveiling the hidden machinations behind everyone’s favorite social media and search platforms.”
1. How I’m fighting bias in algorithms by Joy Buolamwini
Via TED Talks
1. Weapons of Math Destruction by Cathy O’Neil
2. Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin
3. Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble
4. TechniColor: Race, Technology, and Everyday Life by Alondra Nelson, Thuy Linh Nguyen Tu, and Alicia Headlam Hines
5. The Alignment Problem: Machine Learning and Human Values by Brian Christian
1. This image speaks volumes about the dangers of bias in AI by Brad Wyble
2. An image of Barack Obama getting upsampled into a white guy is floating around because it illustrates racial bias in #MachineLearning.