Ethics and Data Science Checklist

	A	B	C	E
1	The 'Ethics and Data Science' Checklist
2	Based on the free ebook 'Ethics and Data Science' by H.Mason, DJ Patil, and Mike Loukides (O'Reilly, 2018)
3		Link to ebook:	http://bit.ly/ethicsdatasci
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5		Project Name:
6		Team Member:
7		Date:
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9		Item	Consideration	Comments
10			Have we listed how this technology can be attacked or abused?
11			Have we tested our training data to ensure it is fair and representative?
12			Have we studied and understood possible sources of bias in our data?
13			Does our team reflect diversity of opinions, backgrounds, and kinds of thought?
14			What kind of user consent do we need to collect to use the data?
15			Do we have a mechanism for gathering consent from users?
16			Have we explained clearly what users are consenting to?
17			Do we have a mechanism for redress if people are harmed by the results?
18			Can we shut down this software in production if it is behaving badly?
19			Have we tested for fairness with respect to different user groups?
20			Have we tested for disparate error rates among different user groups?
21			Do we test and monitor for model drift to ensure our software remains fair over time?
22			Do we have a plan to protect and secure user data?
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27	How well have we addressed the 5 C's?
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29		Item	The 5 C's of Data Ethics	Comments
30			Consent: do users have the ability to provide appropriate and necessary consent?
31			Clarity: do users have clarity about what data is being provided and what is being done with it?
32			Consistency and Trust: will we build trust and be seen as predictable by our users?
33			Control and Transparency: are we giving our users control over their data?
34			Consequences: have we considered how this might cause harm to an individual or group?
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