Resources that discuss equitable distribution and use of resources.
Author: Terence Shin; Publisher: Medium; Publication Year: 2020. The following article begins by giving a brief overview of how dangerous artificial intelligence (AI) models can be if they are not properly checked for biases, then it dives into 3 real-world examples of actual ethical violations and biases that have happened in decision-making processes in real companies. The first involved a healthcare service favoring patients…
Author: N/A; Publisher: Belfer Center for Science and International Affairs, Harvard University; Publication Year: N/A. The following article discusses how biases and systems that routinely exclude and oppress have spread from the physical world into the technological world. TAPP Project, from the Harvard Kennedy School, works to ensure that emerging technologies are developed and managed in ways that serve the overall public good. It has 3 main principles:…
Author: Steven Tiell; Publisher: Ethics of Data; Publication Year: 2020. The following visualization focuses on how a lot of terms surrounding the ethical considerations of data seem interchangeable, yet educating ourselves on what really constitutes ethical data is not something we always consider. Each term has nuance that makes it unique, but they are also all interconnected in a data ethics framework. At the core…
Author: Stephen Spielberg; Publisher: N/A; Publication Year: 2002. The following film starring Tom Cruise concerns a future where people are arrested by a “Pre-Crime” unit before a crime is committed. There are several biases involved in this practice since certain groups of people may be targeted more than others based on past history. About halfway through the movie, ulterior motives for the founding of the Pre-…
Author: Sian Cain; Publisher: The Guardian; Publication Year: 2022. In the following article, FN Meka highlights the importance of considering marginalized communities when making decisions involving data. FN Meka was immediately met with backlash by the Black community when it was signed to a major record deal, but there were several moments leading up to the record deal where there was an opportunity…
Author: Shubnam Sharma; Publisher: UKTN; Publication Year: 2021. In the following article, data ethics deals with the moral obligations of gathering, protecting, and using personally identifiable information, and how it affects individuals. Upon surveying 1,000 British employees working in the field of data analytics, 42% of them said that it is not a priority for them in their careers. This is problematic as experts contend that…
Author: Sarah Thompson; Publisher: Cornell Chronicle; Publication Year: 2022. The following article discusses how a professor in the Department of Statistics and Data Science at Cornell recently began offering a Data Ethics online certificate program which offers 4 2-week courses, in order to focus on larger ethical issues related to algorithms as well as individual choices. This is a educational certificate that could help data science…
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: Sabina Leonelli; Publisher: The Royal Society; Publication Year: 2016. The following article talks about the challenges produced in data science around the responsibilities and accountabilities of individuals in the industry. The author advocates a flexible, participative management of data practices. Regulatory agencies should be responsible for encouraging data scientists to examine ethical implications of their work…
Author: Rowena Rodrigues; Publisher: Journal of Responsible Technology; Publication Year: 2020. The following article looks at how ethical issues such as algorithmic transparency, cybersecurity vulnerabilities, unfairness, bias and discrimination, lack of contestability, legal personhood issues, intellectual property issues, adverse effects on workers, privacy and data protection issues may result in serious liability for damage and lack of accountability as…