• 
• 4 min
• OneZero
• 2020

A group of “Face Queens” (Dr. Timnit Gebru, Joy Buolamwini, and Deborah Raji) have joined forces to do important racial justice and equity work in the field of computer vision, struggling against racism in the industry to whistleblow against biased machine learning and computer vision technologies still deployed by companies like Amazon.

• Algorithmic Bias
• Technology and Race
• Computer Vision
• Machine Learning

• 
• 15 min
• Hidden Switch
• 2018

A hands-on learning experience about the algorithms used in dating apps through the perspective of a created monster avatar.

• Limitations of Digital Technologies
• Algorithmic Bias
• Machine Learning
• Social Networks

• 
• 10 min
• Survival of the Best Fit
• 2018

Explores hiring bias of AI by playing a game in which you are the hiring manager.

• Algorithmic Bias
• Technology and Race
• Machine Learning

• 
• 27 min
• Cornell Tech
• 2019

Solon Barocas discusses his relatively new course on ethics in data science, following a larger trend of developing ethical sensibility in this field. He shares ideas of spreading out lessons across courses, promoting dialogue, and making sure we are really analyzing problems while learning to stand up for the right thing. Offers a case study of technological ethical sensibilities through questions raised by predictive policing algorithms.

• Computing Ethics Issues
• Algorithmic Bias
• Machine Learning

• 
• 5 min
• Time Magazine
• 2017

Chicago police enact an algorithm to calculate a “risk score” for individuals based on factors such as criminal history and age with the aim of assessing and pre-emptively striking against risk. However, these numbers are inherently linked to human bias both in input and outcome, and could lead to unfair targeted of citizens, even as it supposedly introduces objectivity to the system.

• Predictive Policing
• Algorithmic Bias
• Machine Learning

• 
• 28 min
• Cornell Tech
• 2019

Pre-trial risk assessment is part of an attempted answer to mass incarceration. Data sometimes answers a different question than the ones we’re trying to answer (data based on riskiness before incarceration, not how dangerous they are later). Essentially, technologies and algorithms which end up in contexts of social power differentials can often be abused to further cause injustice against people accused of a crime, for example. Numbers are not neutral and can even be a “moral anesthetic,” especially if the sampled data has confounding variables that collectors ignore. Engineers designing technology do not always envisage ethical questions when making decisions that ought to be political.

• Civil Surveillance
• Algorithmic Bias
• Machine Learning