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PHIL 231: Media Ethics: Big Data

Spring 2015/ Dr. Achim Koeddermann

Big Data

VIDIA: Chances, Risks and Responsibilities. Education in the digital age.
Since the beginning of Social Science research, the notion of objectivity has been at the forefront of concerns for philosophers of science (Durkheim 1895). However, Big Data analysis is not following the old model that is driven by hypothesis – it allows you to find patterns in large samples – which also means that the corrective from the old method, falsification, is not available.  We are not fact driven, as we claim, but driven by interpretation (Gitelman 2011).
Big Data  analytics are too new for us to assume that a common foundation of established societal norms can be uniformly applied. As evidence that common groundwork is lacking, please consider the NSA/Snowden scandal, WIKI LEAKS, HSBC and marketing. Basic rights depend on the willingness of the actors to agree on a common foundation, such as respect, justice, and fairness, and its translation into concepts such as intellectual property or privacy. Furthermore, flaws in data collection, data set selection and interpretation can lead to inaccurate assumptions. Hermeneutical safeguards are needed to avoid profiling, discrimination, or distortion.
As big data analytics often remain invisible to individuals and subjects of inquiry, the fiduciary duty to protect fundamental rights of those individuals rests with those who use the data interpretation tools. It is a reasonable expectation that the use of Big Data in the classroom by users who are “natives” in the digital world will ideally lead to a consensus what those rules encompass. The desired result, a shared ethical framework for the use of big data analytic tools, will be a common denominator of papers handed in by students in Environmental Ethics and Media Ethics classes at SUNY Oneonta. This will serve in developing standards of transparency that will guide future generations of researchers. A pluralistic ethical standard should emerge. With the help of VIDA, a general understanding of the dangers of data mining is to ensure the scientific and normative validity of data mining applications. Goal is to avoid commercial applications to lead to manipulation that is neither scientifically nor ethically acceptable (Schmidt, Eric/ Cohen, Jared (2013).

Elizabeth A. Buchanan and Annette N. Markham, guidelines for Ethical Decision-Making and Internet Research (2012).

Schmidt, Eric/ Cohen, Jared (2013): Die Vernetzung der Welt: Ein Blick in unsere Zukunft. (The New Digital Age: Reshaping the Future of People, Nations and Business)

Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think Paperback, 2014

Danah Boyd and Kate Crawford, Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon, retrieved 2.10 2015http://research.microsoft.com/pubs/228268/BigData-ICS-Draft.pdf

Davis, Kord (2012): Ethics of Big Data: Balancing Risk and Information

Durkheim, Emile (1895/1982) Rules of Sociological Method, The Free Press, New York,
NY.

Lisa Gitelman and Virginia Jackson. (2014) Notes for the upcoming collection,  ‘Raw Data’ is an Oxymoron, MIT Press, Cambridge MA 2015, introduction, pre-print retrieved Feb. 59, 2015,http://mitpress.mit.edu/books/raw-data-oxymoron (2011)