Algorithmic transparency: Difference between revisions

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Revision as of 22:49, 29 May 2017

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'''Algorithmic transparency''' is the principle that the factors that influence the decisions made by [[algorithms]] should be visible, or transparent, to the people who use, regulate, and are impacted by systems that employ those algorithms. Although the phrase was coined in 2016 by Nicholas Diakopoulos and Michael Koliska about the role of algorithms in deciding the content of digital journalism services<ref>Nicholas Diakopoulos & Michael Koliska (2016): Algorithmic Transparency in the News Media, Digital Journalism, DOI: 10.1080/21670811.2016.1208053</ref>, the underlying principle dates back to the 1970s and the rise of automated systems for scoring consumer credit.

'''Algorithmic transparency''' is the capacity for the user of an [[Algorithms|algorithm]] to understand its functioning and its resulting output. It is to be opposed to the functioning of an algorithm as a [[black box]], which lacks explainability in its automated decision making.<ref>{{cite journal|last1=Diakopoulos|first1=Nicholas|title=Algorithmic Accountability: Journalistic Investigation of Computational Power Structures.|journal=Digital Journalism|date=2015|volume=3|issue=3|page=398-415}}</ref>

The phrases '''algorithmic transparency''' and '''algorithmic accountability'''<ref>{{cite journal|last1=Diakopoulos|first1=Nicholas|title=Algorithmic Accountability: Journalistic Investigation of Computational Power Structures.|journal=Digital Journalism|date=2015|volume=3|issue=3|page=398-415}}</ref> are sometimes used interchangeably---especially since they were coined by the same people---but they have subtly different meanings. Specifically '''algorithmic transparency''' states that the inputs to the algorithm and the algorithm's use itself must be known, but they need not be fair. '''Algorithmic accountability''' implies that the organizations that use algorithms must be accountable for the decisions made by those algorithms, even though the decisions are being made by a machine, and not by a human being.

Current research around algorithmic transparency is mainly interested in the societal effects of accessing remote services running black box algorithms.<ref>{{cite web|title=Workshop on Data and Algorithmic Transparency|url=http://datworkshop.org/|accessdate=4 January 2017|date=2015}}</ref>

Current research around algorithmic transparency is mainly interested in theboth societal effects of accessing remote services running black box algorithms.<ref>{{cite web|title=Workshop on Data and Algorithmic Transparency|url=http://datworkshop.org/|accessdate=4 January 2017|date=2015}}</ref>, as well as mathematical and computer science approaches that can be used to achieve algorithmic transparency<ref>{{cite web|title=Fairness, Accountability, and Transparency in Machine Learning|url=http://www.fatml.org/|accessdate=29 May 2017|date=2015}}</ref>

==See also==