Information and messages expressed in a way involving visual elements, such as images, animations, diagrams or other mediums.
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: 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: Peter Brownell; Publisher: Systemic Data Ethics; Publication Year: N/A. The following article and visualization establishes a framework for data ethics that can be mapped visually in a number of ways. At its core, the framework is divided into 12 distinct domains that stand up as pillars of data ethics – enough to fully encapsulate the fine issues of data ethics but still few enough to simplify the complex notion. The 12 domains are…
Author: Nicky Case; Publisher: ncase.me; Publication Year: N/A. The following resource considers how should we balance leveraging sensitive data to combat important social issues (e.g., COVID-19 contact tracing) while ensuring data privacy? One answer is the DP-3T protocol used to automatically trace contact between individuals using location-based data from mobile devices. However, even with a…
Author: Mando Rotman, Tom Jongen, Floor Komen; Publisher: IG&H; Publication Year: N/A. The following tool is an Ethical Risk Quick Scan for helping clients assess the ethical risks of their data. The following questions are asked during the Quick Scan: 1). Vulnerable people impacted; 2). Number of people impacted large; 3). Matters of life affected; 4). Influencing personal behavior; 5). No, or slow, feedback loop; 6). No human in…
Author: Jonny Robinson; Publisher: Hurree; Publication Year: 2021. The following article/infographic goes over 5 main topics when it comes to ethics around big data: informed consent, privacy, ownership, algorithms/objectivity, and the big data divide. They define big data ethics as “outlining, defending, and recommending concepts of right and wrong practice when it comes to the use of data.” The infographic…
Author: N/A; Publisher: Intrepid Tech Ventures; Publication Year: N/A. The following article discusses how when data ethical considerations comes to mind, many people go straight to ethics relating to artificial intelligence (AI) and machine learning. The visuals contained within the article serves as great summaries of what an ethical AI algorithm would encompass: Respect for Human Autonomy, Prevention of Harm, Fairness…
Author: N/A; Publisher: Eticas Foundation; Publication Year: 2018. The following infographic provides 9 main checklist items to go over when considering data ethics for a project. These include: 1). Team roles identified; 2). Project fully planned out; 3). Project approved; 4). Subjects gave consent; 5). Data provenance has been documented; 6). Privacy is prioritized; 7). Careful with third party sharing; 8). Scrutinized…
Author: N/A; Publisher: Deloitte Analysis; Publication Year: N/A. The following resource outlines a framework to providing a more holistic approach to ethics within the technology industry. It states the 5 necessary ways to accomplish and provides a nice visual. The main takeaway from this flow chart are the 5 strategies you can implement into the next company you work for which are establishing the…
Author: David Weinberger; Publisher: What-If Tool; Publication Year: N/A. The following article discusses how Google created a “What-If” visualization tool to test machine learning systems and demonstrate how they perform in regards to different definitions of “fair”. This article outlines how the what-if tool assesses 5 different types of fairness: Group unaware, demographic parity, equal opportunity, equal accuracy, and group…