Author: Vindu Goel; Publisher: The New York Times; Publication Year: 2014. The following article focuses on issues prompted by controversial studies by Facebook. The first, in 2014, measured how users’ emotions were impacted by an adjustment to the kind of content appearing in their news feed. The second, from 2010, sent notifications to users reminding them to vote, and found that notifications that showed a list…
Author: Safiya Umoja Noble; Publisher: TEDx Talks; Publication Year: 2014. The following video initially discusses how the speaker is not the same skin tone as her white mother. She then goes into discussing the cultural revolution that was happening during her childhood in the 70s and 80s. In referencing a study that was done in the 40s based on giving Black children both a Black and a white doll and asking questions…
Author: Rebecca Herold; Publisher: SecureWorld; Publication Year: 2014. The following article discusses how 10 of the most significant privacy risks are: 1). Privacy breaches and embarrassments; 2). Anonymization become impossible; 3). Insufficient data masking; 4). Unethical misinterpretation; 5). Inaccurate data or flawed models; 6). Discrimination; 7). Few legal protection; 8). Forever-existing data; 9). Concerns for e-…
Author: Michelle N. Meyer; Publisher: Wired; Publication Year: 2014. The following article describes how for one week in 2012, Facebook data scientists tested the effect of making News Feeds both more positive and less positive. Although the statistically significant findings were of small order, they could matter given the number of daily active users on Facebook. The test found that when a user’s News Feed was more…
Author: Marie Wallace; Publisher: TED; Publication Year: 2014. The following video is a TED talk discussing the issue of privacy in data collection. The speaker gives an overview of the issue of privacy, how it affects people, the role that companies and governments play in it, and how it is a difficult problem to solve that can not have a one-size-fits-all solution. She then relates the issue of privacy to that of transparency…
Author: Kenneth Cukier; Publisher: TED; Publication Year: 2014. The following talk’s primary idea is that when you have more data, you can see patterns and trends than you would have if you had small amounts of data. It allows us to see new, better, and different perspectives. We live in a day in age where we have more data than we know what to do with. Information used to come in clay disks. We still use disks to…
Author: Charles Duhigg; Publisher: Random House Trade Paperbacks; Publication Year: 2014. The following book discusses how in 2002, statistician Andrew Pole began working for Target as a data expert. He was tasked with making an algorithm that could predict pregnancy status. A year after his algorithm was implemented, the father of a high school female angrily entered a Minnesota Target wondering why his…
Author: N/A; Publisher: The Chartered Financial Analyst (CFA) Institute; Publication Year: 2014. Summary: The following handbook discusses regulation in relation to ethics of financial analysts. It covers how to keep privacy of data, to transfer data, and to store the data. Through the regulations, it conveys an idea that data should be treated in the most decent way, and an analyst should try his or her best to protect data, both…
Author: N/A; Publisher: The Center for Open Science; Publication Year: 2014. The following paper addresses how the Center for Open Science’s (COS) mission is to increase the openness, integrity, and reproducibility of research. Toward this effort, they have created an online ecosystem where researchers can pre-register methods, publish data, share study materials, upload analysis code, and pre-write…
Author: Uri Simonsohn, Leif D. Nelson, and Joseph P. Simmons.; Publisher: Berkeley Initiative for Transparency in the Social Sciences; Publication Year: 2014. The following article overviews the concept of the “P-curve,” a tool that can help detect if published research is likely due to p-hacking. P-hacking is the practice of data scientists mining their data until they yield a low p-value, a sign of statistical significance, even if no relationship between variables really exists. P-…