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Systemic Data Ethics Framework: A Stable Foundation for Responsible Innovation
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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…
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Good Practice Principles for Data Ethics in the Public Sector
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Author: N/A; Publisher: OECD; Publication Year: N/A. The following document introduces 10 Good Practice Principles for Data Ethics in the Public Sector, including a set of specific actions which can support their implementation: 1). Manage data with integrity; 2). Be aware of and observe relevant government-wide arrangements for trustworthy data access, sharing and use; 3).…
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Data Ethics Framework
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Author: N/A; Publisher: gov.uk; Publication Year: 2020. The following framework created by the UK government in 2020 is meant to be used throughout a full life cycle of a project. The framework is broken down into 3 overarching principles including transparency, accountability, and fairness. There is a scoring system from 1-5 associated with each of…
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Data Ethics in Tech; Here’s Why It’s So Hard
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Author: Nathan Kinch; Publisher: Medium; Publication Year: 2019. The following article discusses the importance of making an actionable data ethics framework, why it is so difficult for companies to do so, and ways to get started. This article states that specificity is one of the most important parts of of a framework in order to…
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3 Ways to Spot a Bad Statistic
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Author: Mona Chalabi; Publisher: TED; Publication Year: N/A. The following article warns us of the dangers of misleading statistics and the lack of accountability and incentives that private companies have to produce reliable stats. It encourages us to always check the data collection methods to validate any statistical claims. Misinformed or misguided insights start with…
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Data Ethics in combating COVID-19 after Lockdown
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Author: Michelle Seng Ah Lee; Publisher: Trust & Technology Initiative; Publication Year: N/A. The following article talks about using data to overcome challenges due to the COVID-19 pandemic, and lessons learned in using data ethically. South Korea successfully curbed the growth in COVID-19 infections not by enforcing lockdowns, but by “rapidly scaling up its testing…
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Ethical Issues Surrounding Research of AI in Health Care
2022, Code of Ethics, Diversity, Equity & Inclusion, Frameworks, Healthcare, News Article, Notable Peoplelnbressa
Author: Lisa Murtha, Pralika Jain, Kiyong Song; Publisher: Reuters; Publication Year: 2022. The following article discusses how artificial intelligence (AI) models can be flawed considering humans are the ones behind making these models. The authors highlight how important the roles of the developers are when training an algorithm. Specifically this article is exploring AI ethics…
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Our Data Ethics: Measure What Matters and Don’t Be Creepy
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Author: Lauren Peate; Publisher: Multitudes; Publication Year: 2021. The following article and video discuss how when starting a new company that uses lots of user data, it is important to have a good set of principles to follow in regards to data ethics. At Multitudes, there are 5 main principles to be considered: 1). Autonomy:…
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Ethics in Mapping
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Author: Kenneth Field; Publisher: ArcGIS Blog; Publication Year: 2022. The following article discusses the ethical practices that mapmakers should consider in the world of geographic information systems (GIS) and cartography. The author lists important frameworks that he believes map-making professionals should always hold themselves to: realize opportunities, understand impacts, do no harm, protect the vulnerable…
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Ethics and Empathy in Using Imputation to Disaggregate Data for Racial Equity
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Author: K. Steven Brown, LesLeigh Ford, Shena Ashley; Publisher: Racial Equity Analytics Lab; Publication Year: 2021. The following guide by the Urban Institute is meant to guide the practice of data disaggregation when using imputation, particularly for race and ethnicity. It discusses the practices of imputation and how it is important in data fields especially…
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