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Dr Paul Prinsloo, Education Consultant and Researcher, Directorate for Curriculum and Learning Development, University of South Africa
ETHICS AND LEARNING ANALYTICS AS A FAUSTIAN PACT: BETWEEN ORWELL, HUXLEY, KAFKA AND THE DEEP BLUE SEA
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OVERVIEW OF THE PRESENTATION• Some questions to ponder on…• Learning analytics and ethics –introductory remarks• A short history of profiling• Ethics in learning analytics – different approaches• Orwell, Huxley, Kafka and Faust • A short overview of different frameworks• Learning analytics as moral practice• Some questions to ponder on… (revisited)• (In)conclusions
SOME QUESTIONS TO PONDER ON…1. What are some of the dangers in learning analytics?2. Is “raw data” an oxymoron?3. Should students be allowed to opt-out of having their
personal digital footprints harvested and analysed?4. To what extent should students have access to the
content of their digital dossiers, who have access to these dossiers, and what it is used for?
5. How complete and permanent a picture do our data provide about students?
SOME QUESTIONS TO PONDER ON… (cont.)6. To what extent do we provide students the option to
update their digital dossiers and provide extra (possibly qualitative) data?
7. Do students have the right to request that their digital dossiers be deleted on graduation?
8. If we outsource the collection (and analysis) of student digital data to companies, do students need to give consent? [Who owns a student’s data?]
9. Is bigger data sets always better or provide more complete pictures?
10.What responsibility comes with ‘knowing’?
SOME BRIEF INTRODUCTORY REMARKS
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Amidst the hype and potential…Learning analytics, like knowledge and progress, is not an unqualified good – “it can be used as much as a curse as a blessing”
(Gray 2004, p. 70 referring to knowledge and progress)
The dilemma…
Finding the signal in the noise, patterns in the chaos
(Silver, 2012)
The information network…An elaborate lattice of information
networks creates digital collages and biographies from shards of data to form
digital dossiers used by governments, employers, higher education institutions
and the corporate sector
(Solove, 2004)
The gaps…
“We are more than the bits of data we give off as we go about our lives. Our digital biography is
revealing of ourselves but in a rather standardized way” (p. 46). Our digital
biographies are therefore not only (to some extent) unauthorized, but also to a huge extent
reductive, partial and often inaccurate. (Solove, 2004)
Six provocations of Big Data…1. “Automating research changes the definition of knowledge” (p.3)2. “Claims to objectivity and accuracy are misleading” (p. 4)3. “…bigger data are not always better data” (p. 6)4. “Not all data are equivalent” (p. 8)5. “Just because it is accessible doesn’t make it ethical” (p. 10)6. “Limited access to big data creates new digital
divides” (p. 12)(boyd & Crawford, 2013)
Contested objectivity…
“Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations” (Crawford, 2013, para.2).
“…raw data is an oxymoron” (Crawford, 2013, para. 7).
A number of disclaimers…I am not going to refer to • Aristotle, Emmanuel Kant, John Stuart Mill, and John Rawl• Various international and South African laws on privacy,
protection of personal information, personal and national security
• Institutional review boards’ criteria and processesI will• Share my personal sense-making of an extremely complex and
volatile field• Provoke and question some of my own and possibly our
assumptions about data, surveillance and our current techno determinism and reductionist approaches to interpreting student data
A short overview of the history of
profiling
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From Aristotle (4 BCE), to those in the 18th century and beyond… Lavatar, Lombroso, Linnaeus, Blumenbach, Bernier…
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But analysis of data and the compilation of profiles have also allowed us to stop…
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How can learning analytics assist us to stop the revolving door in higher education?
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A brief overview of different approaches
to thinking about ethics in learning
analytics
Different approaches to discussing ethical implications of surveillance, and profiling:• Philosophical approaches – e.g. Willis, Campbell and
Pistilli (2013) • Legal approaches – Marx (1998, 2001); Solove (2004)• A rights-based approach versus a discursive-disclosive
approach (Stoddard, 2012)• Tensions between “public” and “private” (Marx, 2001)• Borders of personal information (Marx & Muschert,
2007)• Socio-critical (Bauman & Lyon, 2013; Prinsloo & Slade,
2013; Slade & Prinsloo, 2013)
“Metaphors are tools of shared cultural understanding” (Balkin, 1998, in Solove, 2004, p. 28)
Metaphors don’t function “to render a precise descriptive presentation of the problem; rather, they capture our concerns over privacy in a way that is palpable, potent, and compelling” (Solove, 2004, p. 28).
TOWARDS A FRAMEWORK FOR ETHICS IN LEARNING ANALYTICS
• 1973 – “Code of fair information practices”• 20 questions by Marx (1998)• 9 Principles by Pounder (2008)
1. There must be no personal-date record-keeping systems whose very existence is secret.
2. There must be a way for an individual to find out what information about him (sic) is in the record and how it is used.
3. There must be a way for an individual to prevent information obtained about him for one purpose for being used or made available for other purposes without his consent.
4. There must be a way for an individual to correct or amend a record of identifiable information about him.
5. Any organisaton creating, maintaining, sing, or disseminating records of indentifiable personal data must assure the reliability of the data for their intended use and must take reasonable precautions to prevent misue of the data.
1973 Code of fair information practices
20 questions by Marx (1998)
Means: Harm, boundaries, trust, personal relationships, validityData collection contexts: Awareness, consent, golden rule, minimisation, public decision making, human review, right of inspection, right to challenge, redress and sanction, stewardship and protection, unintended precedents, etc.Uses: Beneficiary, proportionality, alternative means, consequences of inaction, protections, etc.
9 Principles by Pounder (2008)Principle 1:The justification principlePrinciple 2:The approval principlePrinciple 3:The separation principlePrinciple 4:The adherence principlePrinciple 5:The reporting principlePrinciple 6: The independent supervision principlePrinciple 7: The privacy principlePrinciple 8: The compensation principlePrinciple 9: The unacceptability principle
6 Principles for learning analytics as moral practice (Slade & Prinsloo, 2013)1.Learning analytics as moral practice2.Students as agents3.Student identity and performance as temporal
dynamic constructs4.Student success is a complex and
multidimensional phenomenon5.Transparency6.Higher education cannot afford not to use data
A number of considerations for an ethics architecture for learning analytics (Slade & Prinsloo, 2013)1.Who benefits and under what conditions?2.Conditions for consent, de-identification, and
opting out – including considerations regarding vulnerability and harm
3.Data collection, analyses, access and storage4.Governance and resource allocation
SOME QUESTIONS TO PONDER ON…Revisited
1. What are some of the dangers in learning analytics?2. Is “raw data” an oxymoron?3. Should students be allowed to opt-out of having their
personal digital footprints harvested and analysed?4. To what extent should students have access to the
content of their digital dossiers, who have access to these dossiers, and what it is used for?
5. How complete and permanent a picture do our data provide about students?
SOME QUESTIONS TO PONDER ON… (cont.)6. To what extent do we provide students the option to
update their digital dossiers and provide extra (possibly qualitative) data?
7. Do students have the right to request that their digital dossiers be deleted on graduation?
8. If we outsource the collection (and analysis) of student digital data to companies, do students need to give consent? [Who owns a student’s data?]
9. Is bigger data sets always better or provide more complete pictures?
10.What responsibility comes with ‘knowing’?
(In)conclusions
Referring to his set of 29 questions, Marx (1998) states that the questions will not satisfy philosophers and practitioners who “lust after a Rosetta stone of clear and consistent justifications” but the questions strive to serve as “an imperfect compass than a detailed map” (Marx, 1998, p. 182).
A detailed map “can lead to the erroneous conclusion that ethical directions can be easily reached or to a statement so far in the stratosphere that only angels can see and apply it” (Marx, 1998, p. 182).
Thank you for sharing my personal journey to make sense
of ethics in learning analytics as a Faustian pact, caught between Orwell, Huxley, Kafka and the
deep blue sea…
Thank you. Baie dankie. Ke a leboga Paul Prinsloo
TVW4-69P O Box 392
Unisa0003
[email protected]://opendistanceteachingandlearning.wordpress.com
Twitter: 14prinsp
+27124293683+27823954113
REFERENCESboyd, d., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Code of fair information practices. (1973). Retrieved from http://
simson.net/ref/2004/csg357/handouts/01_fips.pdfCrawford, K. 2013, April 1. The hidden biases in big data. [Web log post]. Harvard
Business Review. Retrieved from http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html
Gray, J. 2004. Heresies. Against progress and other illusions. London, UK: Granta Books.Marx, G.T. 1998. Ethics for the new surveillance. The Information Society: An
International Journal, 14(3), 171-185. DOI: org/10.1080/019722498128809Marx, G.T. 2001. Murky conceptual waters: the public and the private. Ethics and
Information Technology, 3, 157-169. Marx, G.T., & Muschert, G.W. 2007. Personal information, borders, and the new
surveillance. Annual Review of Law and Social Science, 3: 375-95. Pounder, C.N.M. 2008. Nine principles for assessing whether privacy is protected in a
surveillance society. Identity in the Information Society, 1, 1-22. DOI 10.1007/s12394-008-0002-2.
REFERENCES (CONT.)Prinsloo, P., & Slade, S. 2013. An evaluation of policy frameworks for addressing ethical
considerations in learning analytics. LAK '13 Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK13), pp. 240-244. Retrieved from http://dl.acm.org/citation.cfm?id=2460344
Silver, N. 2012. The signal and the noise. The art and science of prediction. London, UK: Allen Lane.
Slade, S., & Prinsloo, P. 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, XX(X), 1-20. DOI: 10.1177/0002764213479366
Solove, D.J. 2004. The digital person. Technology and privacy in the information age. New York, NY: New York University Press.
Stoddart, E. (2012). a. A surveillance of care. Evaluating surveillance ethically. In K. Ball, K.D. Haggerty, and D. Lyon, (eds.), Routledge handbook of surveillance studies. London, UK: Routledge, pp. 369 – 376.
Willis, J.E.III, Campbell, J.P., & Pistilli, M.D. 2013, May 6. Ethics, big data, and analytics: a model for application. EDUCAUSEreview online. Retrieved from http://www.educause.edu/ero/article/ethics-big-data-and-analytics-model-application
