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Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. post
They begin by outlining five equally important guiding principles which I've shortened here.
__Responsibility__ Make available externally visible avenues of redress.
__Explainability__ Ensure that decisions driving data can be explained.
__Accuracy__ Identify, log, and articulate sources of error.
__Auditability__ Enable third parties to review algorithm behavior.
__Fairness__ Ensure decisions do not create discriminatory impacts.
This from the 2016 meeting: Fairness, Accountability, and Transparency in Machine Learning. site
Two of the 13 authors write further in MIT Technology Review. post
The work was brought to my attention by John Naughton in his Memex 1.1. post