Ethics and Data Science Checklist
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The 'Ethics and Data Science' Checklist
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Based on the free ebook 'Ethics and Data Science' by H.Mason, DJ Patil, and Mike Loukides (O'Reilly, 2018)
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Link to ebook:
http://bit.ly/ethicsdatasci
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Project Name:
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Team Member:
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Date:
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ItemConsiderationComments
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Have we listed how this technology can be attacked or abused?
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Have we tested our training data to ensure it is fair and representative?
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Have we studied and understood possible sources of bias in our data?
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Does our team reflect diversity of opinions, backgrounds, and kinds of thought?
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What kind of user consent do we need to collect to use the data?
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Do we have a mechanism for gathering consent from users?
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Have we explained clearly what users are consenting to?
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Do we have a mechanism for redress if people are harmed by the results?
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Can we shut down this software in production if it is behaving badly?
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Have we tested for fairness with respect to different user groups?
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Have we tested for disparate error rates among different user groups?
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Do we test and monitor for model drift to ensure our software remains fair over time?
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Do we have a plan to protect and secure user data?
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How well have we addressed the 5 C's?
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ItemThe 5 C's of Data EthicsComments
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Consent: do users have the ability to provide appropriate and necessary consent?
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Clarity: do users have clarity about what data is being provided and what is being done with it?
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Consistency and Trust: will we build trust and be seen as predictable by our users?
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Control and Transparency: are we giving our users control over their data?
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Consequences: have we considered how this might cause harm to an individual or group?
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Form created by Ben Jones of Data Literacy, LLC
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https://dataliteracy.com
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