Author: Will Horvath; Publisher: Data Science Ethics; Publication Year: 2019. The following article discusses how with countries fighting against the growing threat of privacy invasion (and many losing), Europe implemented in 2018 the General Data Protection Regulation (GDPR) that provides the ‘most sweeping protection of user privacy rights yet passed in the world.’ France’s CNIL used the GDPR to fine Google for $57 million…
Author: Tom Jongen; Publisher: Medium; Publication Year: 2021. The following article discusses how if any company uses customer data to make customer-facing decisions, there are ethical issues that need to be considered. Also, it asks us to consider “When ‘Know Your Customer’ goes too far;” and the issues related to Facebook/Cambridge Analytica political influence scandal from 2016 provide a clear…
Author: Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.; Publisher: Arxiv; Publication Year: 2016. The following article discusses how machine learning applied blindly runs the risk of amplifying biases in data. Word embedding, a popular framework for representing text data as vectors that has been used in many machine learning and natural language processing tasks, poses such a risk. The authors demonstrate that even word embeddings…
Author: Timnit Gebru; Publisher: DAIR Research Institute; Publication Year: N/A. The following website features an interdisciplinary and globally distributed artificial intelligence (AI) research institute that was founded by Timnit Gebru who was fired by Google in December 2020 for raising issues of discrimination in the workplace as was was the co-lead for Google’s Ethical AI research team. This resource seemed unique because it’s…
Author: Safiya Noble; Publisher: TIME Magazine; Publication Year: 2018. The following article talks about how Google’s search algorithm is biased against the Black woman. It shows and talks about the author’s own identity as a black woman and she talks about how a simple search of black woman would result in pornography of Black girls on Google where as in fact a similar search of say white woman does not yield such…
Author: Susanne Schmidt, Sam Gilbert; Publisher: Open Innovations; Publication Year: 2021. In the following article, Susanne and Sam wrestle with different definitions of data ethics and the contexts behind these definitions. They decide that the different definitions are not as important as the actual questions that data ethics should try to conquer. Some of the main questions that they cipher through regard personal privacy and personally…
Author: Sigal Samuel; Publisher: Vox; Publication Year: 2019. The following article talks about the self-driving Uber cars. It points to a very important ethical issue in data science i.e. the issue that self-driving Uber cars so biased that they cannot detect a dark-skinned pedestrian for stopping. This shows that algorithms are inherently biased and that is probably because historically white males in positions of power…
Author: Shannon Vallor, William J Rewark; Publisher: Santa Clara University; Publication Year: N/A. The following article provides a deep dive into the risks and benefits that data can present. After that, the authors talk about what ethical issues or concerns may arise when working with data. The authors of the article take both perspectives in their article: user-based and people who are given access to the data. The authors also point out what dangers…
Author: Samantha Cole; Publisher: Vice; Publication Year: 2022. The following article discusses how earlier this year, Google created a writing assistant tool that works similarly to Grammarly but checks for inclusive language in addition to making suggestions regarding style and tone. In theory, this product has good intentions, but in practice, it has performed poorly at times. For example, the word “motherboard” was…
Author: Robert Wiblin, Keiran Harris, Chris Olah; Publisher: 80,000 Hours; Publication Year: 2021. The following podcast episode features Chris Olah, a researcher at Google focused on data interpretability, specifically as it relates to neural networks and big machine learning algorithms. The interview provides a deep dive into some of the issues around data interpretability and why the data “black box” issue is a problem worth solving…