• 
• 10 min
• New York Times
• 2019

Racial bias in facial recognition software used for Government Civil Surveillance in Detroit. Racially biased technology. Diminishes agency of minority groups and enhances latent human bias.

• Civil Surveillance
• Algorithmic Bias
• Computer Vision
• Data Streaming

• 
• 7 min
• TED
• 2017

Predictive policing software such as PredPol may claim to be objective through mathematical, “colorblind” analyses of geographical crime areas, yet this supposed objectivity is not free of human bias and is in fact used as a justification for the further targeting of oppressed groups, such as poor communities or racial and ethnic minorities. Further, the balance between fairness and efficacy in the justice system must be considered, since algorithms tend more toward the latter than the former.

• Predictive Policing
• Algorithmic Bias
• 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

• 
• 7 min
• Vice
• 2019

An academic perspective on an algorithm created by PredPol to “predict crime.” Unless every single crime is reported, and unless and police pursue all types of crimes committed by all people equally, it’s impossible to have a reinforcement learning system that predicts crime itself.Rather, police find crimes in the same places they’ve been told to look for them, feeding the algorithm ineffective data and allowing unjust targeting of communities of color by the police to continue based on trust in the algorithm.

• Predictive Policing
• Machine Learning

• 
• 2 min
• Wired
• 2019

Synthetic human faces are able to be generated by a machine learning algorithm.

• Deep Fake
• Computer Vision
• Machine Learning

• 
• 7 min
• MIT Technology Review
• 2019

Autonomous vehicles could be subject to hacks by adversarial machine-learning, possibly perpetrated by out-of-work truck/Uber drivers and “adversarial machine learning”. The fact that vehicle algorithms can already be fairly easily tricked also raises concerns.

• Cyber Crime
• Transportation Technologies
• Machine Learning
• Autonomous Vehicles