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Big data ethics, also known simply as data ethics, refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data, in particular personal data. [1] Since the dawn of the Internet the sheer quantity and quality of data has dramatically increased and is continuing to do so exponentially.
Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. [1]
Data Feminism is a book written by Catherine D’Ignazio and Lauren F. Klein as part literature review, part call to action, Data Feminism provides a framework for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science.
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The Ten Commandments of Computer Ethics were created in 1992 by the Washington, D.C.–based Computer Ethics Institute. [1] The commandments were introduced in the paper "In Pursuit of a 'Ten Commandments' for Computer Ethics" by Ramon C. Barquin as a means to create "a set of standards to guide and instruct people in the ethical use of computers."
The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service.
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The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes. [1] This includes algorithmic biases, fairness, automated decision-making, accountability, privacy, and regulation.