Author: Vince Lynch; Publisher: TechCrunch+; Publication Year: 2018. The following article discusses how bias in artificial intelligence (AI) can cause problems both because AI outputs are often blindly trusted, so any missed human bias could be spread, and because AI used in automated functions could spread bias without knowledge. The article names 3 ways to avoid bias in machine learning. 1). Choose the right…
Author: Sunil Yadav; Publisher: Baseline; Publication Year: 2022. The following article discusses how technology is created by humans and often reflects human biases. It is important to prioritize having unbiased artificial intelligence (AI) algorithms and models, because biased AI systems can produce erroneous and discriminatory predictions, and impact a business’s reputation, future opportunities, and…
Author: Steve Durbin; Publisher: World Economic Forum; Publication Year: 2022. The following article discusses how although artificial intelligence (AI) promises to improve and streamline business operations and everyday life, there are proportional increasing concerns about the implementation of the technology. In order to counteract possible negative effects of AI, data scientists need to account for “inbuilt prejudices” that…
Author: Selin Akgun, Christine Greenhow; Publisher: AI and Ethics; Publication Year: 2022. The following article discusses the current and proposed uses of artificial intelligence (AI) in promoting educational outcomes for K-12 students. It asserts that, if unchecked, AI algorithms can perpetuate systematic and human biases, especially against groups who are disadvantaged based on factors such as nationality, race, language, or…
Author: Sean Michael Kerner; Publisher: VentureBeat; Publication Year: 2022. The following article discusses how when labeling data for modeling and machine learning, the person labeling the data can accidently introduce bias to the models. This article argues that an open-source way to model (i.e. let a group of people label the data) can help to reduce these biases. This is an interesting idea: while labeling things yourself is a…
Author: Sarah E. Bon, Nyasha Junior; Publisher: Hyperallergic; Publication Year: 2020. The following article explores the use of artificial intelligence (AI) and machine learning in artistic research and reconstructions. Specifically, the authors explore the hidden human bias behind these technologies that are seemingly objective and scientific. Throughout the article, the authors name numerous examples when unethical and biased…
Author: Pedro Saleiro; Publisher: Feedzai; Publication Year: 2020. The following document discusses how recent events pushed organizations and individuals to push back against biases in financial institutes. In the digital age, however, it is not sufficient to address biases caused by humans but should also look into artificial intelligence. The author suggests 4 key takeaways for financial institutes in order to prevent…
Author: Ovetta Sampson; Publisher: Medium; Publication Year: 2018. The following article discusses how bad algorithms compounded with bad actors have caused much technological harm over the past few years. The Facebook Russian Election scandal went through due to Facebook’s “quantitative-quest” focus within its PageRank algorithm, leaving data engineers with no idea about all the prejudices their ads…
Author: Nicol Turner Lee, Paul Resnick, Genie Barton; Publisher: Brookings; Publication Year: 2019. The following piece describes how artificial intelligence (AI) is being used to manage greater aspects of our economy. There is risk that algorithms can magnify human biases. Bias in this piece is defined when outcomes to one group disproportionally bears a negative impact of an algorithm. In response to this, there should be processes in place that…
Author: N/A; Publisher: Future Learn; Publication Year: N/A. The following article discusses how data science has increased rapidly; however, the concerns around the ethical use of it by organizations have risen alongside it. We have seen examples of concerns in different realms, algorithms accepting and denying bank loans, scanning resumes, cookies to monitor behaviors, and airlines to decide on differential pricing…