Author: N/A; Publisher: YimmyTalks; Publication Year: 2020. The following video starts off by discussing the importance of data and data scientists in today’s society outlining their power and influence. It then shifts to talking about the responsibility that comes with this power. Since data scientists and their models have…
Author: Jessica Fjeld, Adam Nagy; Publisher: Berkman Klein Center; Publication Year: 2020. The following paper describes and explains 9 principles of ethical data practices: Informed consent of data subjects, security of data, anonymization, transparency, diversity, bias, prominence and communication. The principles listed in this framework cover 4 essential values of ethical data practices: Fairness, benefit, openness and reliability…
Author: Violeta Mezeklieva; Publisher: Open Data Institute; Publication Year: 2020. The following article is from the Open Data Institute and it highlights the experience that the author, Violeta Mezeklieva, has had over the past year on training people on data ethics and the insights that have been gleaned from that experience. She highlights that although it is her training all kinds of folks on the importance and the concepts of data ethics, it was her who really had an educational experience observing how much she has learned from the people who go through this sort of training. She starts out by saying she…
Author: N/A; Publisher: Stanford Encyclopedia of Philosophy; Publication Year: 2020. In the following article, Dr. Müller at Stanford discusses why artificial intelligence (AI) system scandals are covered differently in the media. For a problem to qualify as a problem for AI ethics would require that we do not readily know what the right thing to do is. In this sense, job loss, theft, or killing with AI is not a problem in ethics, but whether these…
Author: Valerie Schneider; Publisher: Columbia Human Rights Law Review; Publication Year: 2020. The following article is a very granular deep dive into how artificial intelligence (AI) can lead to discrimination in housing and how this relates to Fair Housing Act “disparate impact” jurisprudence. The motivation for the article was a proposed rule from HUD that would have immunized landlords using algorithms from disparate impact fair housing claims…
Author: N/A; Publisher: United Nations News; Publication Year: 2020. The following article discusses how artificial intelligence (AI) development is especially problematic for countries where governments do not have the ability to effectively regulate consumer privacy or misinformation. International AI regulation will probably be necessary to curb the problem. Social media companies are incentivized to supply…
Author: N/A; Publisher: U.K. Statistics Authority; Publication Year: 2020. The following visual provides a resource that outlines data ethics into 6 different categories. These categories are public good, data security, methods, legal compliance, public views/engagement, and transparency. A key takeaway from this resource is public good being at the top of the visual which emphasizes the fact that public good is crucial to…
Author: Tristan Harris, Audrey Tang; Publisher: Your Undivided Attention; Publication Year: 2020. The following podcast episode features Audrey Tang, minister of digital affairs in Taiwan. She is a transgender woman that identifies as “post-gender” and the first non-binary person to hold an executive-level position in Taiwan. In this interview, she discusses how she has helped implement greater data transparency in the government and outlines methods…
Author: Trisha Sanghal, Coco Sun, and Sina Ghandian; Publisher: Medium; Publication Year: 2020. The following article discusses findings regarding policies related to deaths caused by police departments. Is crime a predictor for violent force policies? Well, researchers found that violent crime does not explain restrictive policy. While policing is a complicated topic, this does point to inherent biases in the system which must be accounted…
Author: Travis Greene; Publisher: Medium; Publication Year: 2020. In the following article, Travis Greene discusses the idea of data scientists becoming activist data scientists. Loosely basing the idea on a concept called “activist engineering” which was developed in 2015, Greene describes an activist data scientist as someone who realizes the social and political community they live in, and who realizes that…