{"id":5836,"date":"2021-03-22T15:54:30","date_gmt":"2021-03-22T22:54:30","guid":{"rendered":"https:\/\/blogs.ubc.ca\/etec523\/?p=5836"},"modified":"2021-03-22T15:54:31","modified_gmt":"2021-03-22T22:54:31","slug":"machine-bias","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/etec523\/2021\/03\/22\/machine-bias\/","title":{"rendered":"Machine Bias"},"content":{"rendered":"\n

Big Data, AI, and machine learning are hot topics in education. Before their popularity in education, however, they first boomed in commercial industries. For example, it was an algorithm that chose who to boot off that United Airlines flight when it was overbooked in 2017 (United Airlines faces backlash after dragging man from plane – CBS News<\/a>). Even though news articles say that passengers were chosen at random, it was really an algorithm that calculated who in the plane was the least valuable, who was the least likely to ride with United again, and who was not a part of their rewards program. In other words, United Airlines made a decision on who was the most dispensable. Big data, AI, and machine learning pervade in many aspects of our lives, hidden, in the sense that we do not know when they are employed to make decisions, and when we do know, the code that generates these decisions are often proprietary and blocked from public scrutiny. <\/p>\n\n\n\n

Computer generated decisions appear to be 100% objective because a machine cannot be swayed by emotion or bias, and this can be great when they are designed to solve climate change, traffic congestion, or to model a pandemic. This seemingly unbiased nature of computer programs is appealing because we want to eliminate human error while simultaneously be able to crunch huge volumes of data. However, we cannot forget that it is humans that create these algorithms in the first place, and in doing so, prejudice and bias can be unintentionally coded in, which can result in severe consequences if the programs are making decisions on people. Prime examples of algorithms and machine learning gone wrong can be found in the justice system where decisions on sentence severity, bail, and even arrests are made by machines. If arrests are made more frequently in poorer neighbourhoods and with people of colour because cops are prejudiced and racist when they do their patrols, then the data fed into a machine that is learning how to predict crime is prejudiced and racist. It is not so hard to imagine how a computer might grant bail more easily to a non person of colour, even if that person’s crime is more severe than that from a person of colour. This podcast from ETEC 523 discusses the ethical and moral issues with current algorithms and AI.<\/p>\n\n\n\n

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