Author: Jessica Fjeld, Adam Nagy; Publisher: Berkman Klein Center; Publication Year: 2020. The following paper describes and explains 9 principles of ethical data practices: Informed consent of data subjects, security of data, anonymization, transparency, diversity, bias, prominence and communication. The principles listed in this framework cover 4 essential values of ethical data practices: Fairness, benefit, openness and reliability…
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: 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…
Author: Steven Tiell; Publisher: Accenture; Publication Year: 2021. The following article talks about how In 2021, data ethics will be the tool financial services firms choose to manage digital risks in automation and cybersecurity. Practicing data ethics helps organizations to identify these risks early in the development of new products and services, and to intervene with tools, assessments, and governance processes…
Author: Sonja Kelly, Mehrdad Mirpourian; Publisher: Women’s World Banking; Publication Year: 2021. The following report discussed where gender-based bias originates and how to mitigate such biases in the emerging digital credit space. The report discussed the data collection process on emerging credit platforms and what data would be collected. It also introduced 3 potential origins of bias in algorithms, including sampling bias, labeling bias…
Author: Sigal Samuel; Publisher: Vox; Publication Year: 2022. The following article introduced the idea of bias in a statistical sense and how bias is interpreted in society. This is an important distinction to recognize and is often overlooked. The author discussed that defining what “fair” is can be very tricky and provided different definitions of “fairness”. An example is procedural fairness: an algorithm is…
Author: Rumman Chowdhury; Publisher: Accenture Technology; Publication Year: 2019. In the following video, Dr. Ruman Chowdury explains how “[artificial intelligence (AI)] is information about people meant to understand trends about human behavior.” There are 2 kinds of bias: bias in data and models and bias in the imperfect world. Ethics is not just about improving technology but improving the society behind the technology. Technologists…
Author: Rik Chomko; Publisher: Forbes; Publication Year: 2022. The following article describes how, from an equity standpoint, artificial intelligence (AI) transparency is vital for preventing bias that has the potential to penalize protected classes or negatively impact a person’s well-being. From a business standpoint, it is becoming just as important. Across the nation and the globe, standards and legislation are…
Author: Phaedra Boinodiris; Publisher: IBM Technology; Publication Year: 2021. The following article discusses how artificial intelligence (AI) is making decisions that directly impact all of us. People assume that because AI is a machine, the AI is unbiased and creates a “correct” decision. There are 5 pillars to earning trust in an AI decision. The first pillar is fairness. Is AI fair to everyone? In particular, is AI being proper for historically…
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…