• 
• 3 min
• Kinolab
• 2018

Wade Watts lives in an imagined future in which the OASIS, a limitless virtual reality world, acts as a constant distraction from the real world for the majority of citizens. In this scene, his virtual avatar Parzival visits the Halliday Journals, a complete archive of the memories of James Halliday, the creator of the OASIS. These memories are digitized in their complete abstract form, and seem freely accessible to anyone.

• Data Privacy
• Scientific Innovation
• Brain-Computer Interfaces
• Virtual Reality

• 
• 30 min
• Wired
• 2019

In China, “supercompanies” such as WeChat or Alipay aggregate massive amounts of varied data on users. The Zhima Credit score system directly influences the agency of users by limiting their options in acting in their environment, or determining with whom they interact. The Chinese government interests itself with allying with large tech companies to incorporate a social ranking system which can be used to control and suppress citizens. Although the United States does not have “supercompanies” like those mentioned from China, the large companies that collect user data in the US certainly have the same potential to limit human agency.

• Data Privacy
• Governing Technologies
• Data Mining
• Digital Cash

• 
• 5 min
• GIS Lounge
• 2019

GIS, a relatively new form of computational analysis, can often contain algorithms with biases based on biases present in the training data from open data sources, with this case study focusing on the tendency of power-line identification data being centered around the Western world. This problem can be improved by approaching data collection with more intentionality, either broadening the pool of collected geographic data or inputting artificial images to help the tool recognize a greater number of circumstances and thus become more accurate.

• Global Impact of Technology
• Algorithmic Bias
• Machine Learning
• Object Recognition

• 
• 5 min
• MIT Technology Review
• 2019

Introduction to how bias is introduced in algorithms during the data preparation stage, which involves selecting which attributes you want the algorithm to consider. Underlines the difficult nature of ameliorating bias in machine learning, given that algorithms are not always perfectly attuned to human social contexts.

• Fundamental Ethics
• Algorithmic Bias
• Machine Learning

• 
• 5 min
• Wired
• 2019

Monster Match, a game funded by Mozilla, shows how dating app algorithms are reinforcing bias through combining personal and mass aggregated data to systematically hide a vast number of profiles from user sight, effectively caging users into narrow preferences.

• Technology Based Entertainment and Leisure
• Algorithmic Bias
• Machine Learning
• Social Networks

• 
• 5 min
• Wall Street Journal
• 2019

Incorporation of ethical practices and outside perspectives in AI companies for bias prevention is beneficial, and becoming more popular. Spawns from a need for consistent human oversight in algorithms.

• Algorithmic Bias
• Technology and Race
• Machine Learning