Author: lnbressa
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The Hidden Ethics of Our Personal Data
Author: Danel Goddemeyer, Dominikus Baur; Publisher: TEDx; Publication Year: N/A. The following TED Talk features a collaborative audience demonstration on collecting data. It makes connections to how easy is it to leave a data trail…
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The Great Hack on Netflix: Ethics of Data Use
Cambridge Analytica Scandal, Facebook, Google, Mass Data Exploitation, Netflix, The Great Hack, Whistle-BlowingAuthor: Dan Wardrope; Publisher: Flexxable; Publication Year: N/A. The following article reviews people’s reactions to The Great Hack on Netflix. The Great Hack exposes the wrong-doing of mass data exploitation during the Cambridge Analytica scandal.…
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5 Questions to Ask to Ensure AI Ethics
Author: Dan Power; Publisher: Evanta; Publication Year: 2022. The following article lists questions that need to be asked to check if any ethics problems exist or if anything has been missed. Large companies such as…
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What Star Wars Can Teach Us About Cyber Ethics
Author: Dan Lohrmann; Publisher: Security InfoWatch; Publication Year: 2016. The following article attempts at relating Star Wars to cyber ethics. It relates the “Dark Side” to data breaches. It first states that sometimes, we are…
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How Does Queerness Fit Into the US Census
Author: Dan Bouk; Publisher: Wired; Publication Year: 2022. The following article provides what could be considered a history lesson about the U.S. Census. It touches on topics related to accurate collection of data (including measures…
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Algorithmic Bias: Why Bother?
Author: Damini Gupta, T. S. Krishnan; Publisher: California Review Management; Publication Year: 2020. The following article highlights the importance and the need to reduce algorithmic bias. The article starts by recognizing that human bias will…
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Conscientious Classification: A Data Scientist’s Guide to Discrimination-Aware Classification
Author: d’Alessandro B, O’Neil C, LaGatta T.; Publisher: National Library of Medicine; Publication Year: 2017. The following article describes discrimination in the context of machine learning. The authors discuss how models can be biased even…