Resources that discuss frameworks on how to encourage participation from everyone, notably for those who have been underrepresented or discriminated against.
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…
Author: Tim Harford; Publisher: Tim Harford (Personal Website); Publication Year: 2021. The following article discusses how Florence Nightingale, the founder of modern nursing, famously used data visualization to communicate the impact of the Sanitary Commission’s work on the medical outcomes of soldiers. Although her thinking about sanitation was largely correct, her choice to use a rose diagram was somewhat unbecoming…
Author: Tiffany Oliver; Publisher: TEDx Talks; Publication Year: 2022. In the following talk, Dr. Oliver talks about her work to address the underrepresentation of women and BIPOC in data science. In 2021, Dr. Oliver created a summer research program at Spelman College, a historically Black women’s college, to introduce Spelman students to data science. There are 2 notable aspects of Dr. Oliver’s…
Author: Thomas Walle; Publisher: Fobers; Publication Year: 2020. The following article is particularly insightful as it shows the difference between data privacy and data ethics, even though folks have a tendency to use the terms somewhat interchangeably. The article starts by introducing the idea of data privacy and the data privacy legislation that is being passed. The author then states that data privacy is fairly…
Author: Thomas J., McNabb S.; Publisher: Global Health Data Methods; Publication Year: N/A. The following article discusses how misuse in health range from a physician not informing their patient about the risks of a procedure they will perform to the manager of a public health data system not protecting the confidentiality of patient records. Public health systems must protect the confidentiality of sensitive information while ensuring practitioners…
Author: Thomas Brewster; Publisher: Forbes; Publication Year: 2015. The following article discusses how for over a year, SilverPush, an Indian firm, was secretly using inaudible sounds that would play through people’s televisions and get picked up by their cell phones. The imperceivable sound would tune during commercials and get picked up by phones that had SilverPush’s technology embedded in various applications…
Author: N/A; Publisher: The United Kingdom’s Department for Digital, Culture, Media & Sport; Publication Year: 2020. The following framework is based on 3 principles: transparency, accountability, and fairness. These principles, supported by 5 specific actions, guide organizations through different stages of the project and provide practical considerations. The 5 actions are: 1). Define the goal or benefit, 2). Use diverse teams to minimize bias (evaluators may be part-…
Author: N/A; Publisher: The Greenlining Institute; Publication Year: 2021. The following article discusses algorithmic biases in credit and finance, healthcare, employment, government programs, education and housing. Focusing in on algorithmic bias in credit and finance, banks and the fintech industry have eagerly replaced loan officers with algorithms that are more complex and use more sources of data than ever before to make…
Author: N/A; Publisher: The Global Indigenous Data Alliance; Publication Year: 2022. The following guidelines set out the minimum requirements for Indigenous-designed data approaches and standards, which can be generalized to all approaches and standards to data ethics surrounding marginalized communities. They show the current inadequacy of consent and data privacy protections, and highlight community-controlled…
Author: N/A; Publisher: Australian Government; Publication Year: 2022. The following guidelines provided by the Australian Government has a list of 8 items that make up their data ethics framework. These include: 1). Human, societal and environmental wellbeing, 2). Human-centered values, 3). Fairness , 4). Privacy protection and security , 5). Reliability and safety , 6). Transparency and explainability , 7). Contestability, and…