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Ethics in dealing with data.


http://contextbridge.com/2014/05/15/data-science-ethics/

kate crawford microsoft

Data Science + Ethics

Ethics in dealing with data. 

A conference at Stanford, September 15-16, 2014: Ethics of Data in Civil Society.

A nice talk in Data.Stories with Kate Crawford of Microsoft Research. Data.Stories is a great podcast series on data visualization by Enrico Bertini and  Moritz Stefaner.

Information Week (October 2013): contains an interview with the founder of a young organization calling itself, the Data Science Association.  A quote from the article, the founder says:

 Here’s a link to the Data Science Association’s Code of Conduct.

Here is an extract of the code of conduct …. point F … just to show you how thoughtful these folks are.

(f) A data scientist shall not knowingly: (1) fail to use scientific methods in performing data science; (2) fail to rank the quality of evidence in a reasonable and understandable manner for the client; (3) claim weak or uncertain evidence is strong evidence; (4) misuse weak or uncertain evidence to communicate a false reality or promote an illusion of understanding; (5) fail to rank the quality of data in a reasonable and understandable manner for the client; (6) claim bad or uncertain data quality is good data quality; (7) misuse bad or uncertain data quality to communicate a false reality or promote an illusion of understanding; (8) fail to disclose any and all data science results or engage in cherry-picking; (9) fail to attempt to replicate data science results; (10) fail to disclose that data science results could not be replicated; (11) misuse data science results to communicate a false reality or promote an illusion of understanding; (12) fail to disclose failed experiments or disconfirming evidence known to the data scientist to be directly adverse to the position of the client; (13) offer evidence that the data scientist knows to be false. If a data scientist questions the quality of data or evidence the data scientist must disclose this to the client. If a data scientist has offered material evidence and the data scientist comes to know of its falsity, the data scientist shall take reasonable remedial measures, including disclosure to the client. A data scientist may disclose and label evidence the data scientist reasonably believes is false; (14) cherry-pick data and data science evidence.

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