considerations for the development of a draft south african policy and framework on the ethical...

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Considerations for the development of a draft South African policy and framework on the ethical collection, analysis and use of student data

Paul PrinslooUniversity of South Africa (Unisa)

@14prinsp

Workshop at the 3rd Siyaphumelela Conference, 27-29 June 2017, Johannesburg, South Africa

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I do not own the copyright of any of the images in this presentation and acknowledge the original copyright and

licensing regime of every image used.

This presentation (excluding the images) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International

License

http://creativecommons.org/licenses/by-nc/4.0/

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An exploration in two parts

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Part 1: Exploring1. Factors that shape our collection,

analysis and use of student data2. The rationale for considering the

ethical implications of the collection, analysis and use of student data

3. Four distinct but overlapping influences:• Context is everything• The role of data scientists• Our approaches to data analysis• Our expectations and beliefs

regarding data• Approaches to ethics – whose

ethics/whose values?4. The potential and limitations of

policies

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Part 2: Responding

• Points of departure• Decolonising methodologies

and ethics• Emancipatory research • Realities• In scope/Out of scope• Guiding principles• Towards implementation• (In)conclusions

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What/who shapes/influences our collection, analysis and use of student data?

And… what are the ethical implications?

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What could possibly go wrong when we collect, analyse and use student data?

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Yes, a lot can ‘go right’ as well, but for now, let us consider what can go wrong despite our

best intensions…

Why bother?

Learning analytics is a structuring device, not neutral, informed by current beliefs about what counts as knowledge and learning, colored by

assumptions about gender/sexual orientation/race/class/capital/literacy/engagement and in service of and perpetuating existing

or new power relations

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Why is there a need for accountability? How are we held accountable? Who will

hold us accountable?

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Four pointers for consideration when contemplating a contextualised approach to

the ethical use of student data…

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What does a contextualised, South African perspective on the ethical

collection, analysis and use of student data entail?

1. Context is everything

Prinsloo, P. (2016, October 26). Mapping the ethical implications of using student data – A South African contextualised view. Retrieved from https://www.slideshare.net/prinsp/mapping-the-ethical-implications-of-using-student-data-a-south-african-contextualised-view

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What does a contextualised, South African perspective on the ethical

collection, analysis and use of student data entail?

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Many (most?) of the discourses pertaining to the use of collection, analysis and use of student data

have been and are shaped by the Global North

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Can we ignore the way colonialism

• Stole the dignity and lives of millions based on arbitrary criteria and beliefs about meritocracy supported by asymmetries of power

• Extracted value in exchange for bare survival• Objectified humans as mere data points and

information in the global, colonial imaginary• Controlled the movement of millions based on

arbitrary criteria such as race, cultural grouping and risk of subversion?

Image credit: https://en.wikipedia.org/wiki/Xhosa_Wars

Prinsloo, P. (2016, October 26). Mapping the ethical implications of using student data – A South African contextualised view. Retrieved from https://www.slideshare.net/prinsp/mapping-the-ethical-implications-of-

using-student-data-a-south-african-contextualised-view

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Can we ignore how data were used during Apartheid to classify humans according to those worthy of humanity and dignity and those who

were , somehow, less human, less worthy, and of lesser merit?

Prinsloo, P. (2016, October 26). Mapping the ethical implications of using student data – A South African contextualised view. Retrieved from https://www.slideshare.net/prinsp/mapping-the-ethical-implications-of-using-student-data-a-

south-african-contextualised-view

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Can we ignore the fact that data collection, analysis and use are political acts and serve declared and hidden assumptions about the purpose of higher education and the masters it serves (Apple, 2004, 2007; Grimmelman, 2013; Watters, 2015)?



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How do we collect, analyse and use student data recognising that their data are not indicators of their potential, merit or even necessarily engagement but the results of the inter-generational impact of the skewed allocation of value and resources based on race, gender and culture?



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A contextualised approach to the ethical collection, analysis and use of student data …

• Acknowledges the lasting, inter-generational effects of colonialism and apartheid

• Collects, analyses and use student data with the aim of addressing these effects and historical and arising tensions between ensuring quality, sustainability and success

• Critically engages with the assumptions surrounding data, identity, proxies, consequences and accountability

• Responds to institutional character, context and vision• Considers the ethical implications of the purpose, the

processes, the tools, the staff involved, the governance and the results of the collection, analysis and use of student data

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So what are (some of) our beliefs and assumptions about…

1. Data scientists2. Data analysis3. Data and student data4. Whose ethics? Whose values?5. The potential of policies to make a difference?

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Prinsloo, P., & Slade, S. (2017, March 13). Building the learning analytics curriculum: Should we teach (a code of) ethics? Retrieved from https://www.slideshare.net/prinsp/building-the-learning-analytics-curriculum-should-we-teach-a-code-of-

ethics

Data scientists

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Web page credit: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

2012

Web page credit: https://www.techopedia.com/2/28526/it-business/it-careers/data-scientists-the-new-rock-stars-of-the-tech-world

2014

Web page credit: https://www.wired.com/2002/12/holytech/

“…computation seems almost a theological process. It takes as its fodder the primeval choice between

yes or no, the fundamental state of 1 or 0.”

2002

Web page credit: https://blogs.ischool.utexas.edu/digitalcuration/2012/10/03/data-scientists-or-data-gods/

Web page credit: https://www.wsj.com/articles/academic-researchers-find-lucrative-work-as-big-data-scientists-1407543088

Page credit: https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/amp/

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020

IBM Predicts Demand For Data Scientists Will Soar 28% By 2010

Credit: Retrieved from http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf

Harris, H., Murphy, S., & Vaisman, M. (2013). Analyzing the analyzers: An introspective survey of Data Scientists and their work. O'Reilly Media, Inc."

How do data scientists think about themselves

and their career?

“What skills do you bring to your work? What are your primary areas of expertise?”

Credit: Retrieved from http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf

If these are the people who collect and analyse students data…

• How do they understand the inter-generational histories entangled/embedded in student data?

• How do they understand variables such as street addresses as proxies for what?

• How do they understand learning?• How are they held accountable for their analyses and the

rationale for their findings when the methods and tools they use are increasingly complex and ‘black boxes’? Who will hold them to account?

Gods, rock stars, game changers or …fallible humans with biases?

They are ‘mere’ humans who “interpret meaning from data in different ways. Data scientists can be shown the same sets of data and reasonably come to different conclusions. Naked and hidden biases in selecting, collecting, structuring and analysing data present serious risks. How we decide to slice and dice data and what elements to emphasise or ignore influences the types and quality of measurements.”

Walker, M. A. (2015). The professionalisation of data science. International Journal of Data Science, 1(1), 7-16

Image credit: https://pixabay.com/en/computer-room-computer-screens-415141/

What about all the others who request and analyse student data?

• How do they understand the inter-generational histories entangled/embedded in student data?

• How do they understand variables such as street addresses as proxies for what?

• How do they understand learning?

Image credit: http://maxpixel.freegreatpicture.com/static/photo/1x/Metal-Fence-Iron-Old-77940.jpg

Data analysis is an “art” (Ibrahim, 2013) and a “black art” (Floridi, 2012). These descriptions

create the impression of data analysis providing access to ‘hidden’ knowledge, not normally

accessible to mere mortals - knowledge that can only be accessed through an interlocutor

Data analysis

How do we hold this power accountable?

Image credit: https://www.flickr.com/photos/bionicteaching/2920562020

Some of our beliefs about data…• Data are neutral• Represents ‘the Truth’ – you can’t argue with data• We talk about data as “raw”, “cooked”, “corrupted”,

“cleaned”, “scraped” “mined” and “processed” (Gitelman & Jackson, 2013)

• Data are self explanatory (Mayer-Schönberger & Cukier, 2013)

• We believe that n=all, and that knowing ‘what’ is happening erases the need to know ‘why’ something is happening

• Big(ger) data are better data• We can distinguish between the signal and the noise (Silver,

2012)

Data are not neutral, raw, objective and pre-analytic but framed “technically, economically, ethically, temporally, spatially and philosophically. Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them”

(Kitchen, 2014, p. 2)

Contrary to our beliefs...

When a fact is proven false, it stops being accepted as a fact. When data are false, it

remains data…

The relation between data, information, knowledge, evidence and wisdom is more complex and contested than we may be comfortable with…

Data are political in nature – loaded, shaped and limited with the values, interests and assumptions of those who collect, frame and use the data (Selwyn, 2014)

What are the implications for learning analytics as ethical practice when...

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86Apophenia – “seeing patterns

where none actually exist, simply because enormous quantities of data can offer

connections that radiate in all directions”

(boyd & Crawford, 2012, p. 668)

What are the implications for learning analytics as ethical practice when...

Seeing Jesus in toast: Irreverent ideas on some of the claims pertaining to learning analytics (Prinsloo, 2016) –https://opendistanceteachingandlearning.wordpress.com/2015/12/07/seeing-jesus-in-toast-irreverent-ideas-on-some-of-the-claims-pertaining-to-learning-analytics/

@Jesus_H_Toast

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Student data as the ‘new black”, as oil, as a resource to be mined

Image credit: http://fpif.org/wp-content/uploads/2013/01/great-oil-swindle-peak-oil-world-energy-outlook.jpg

We believe student (digital) data …

• Represents the whole picture• Whose interests are really at stake?• The data belong to us• Students don’t need access, and they

don’t need to know what we collect, the reasons for the collection, how we analyse the data, how long we keep the data, who has access to the data, and who we share the data with….

Whose ethics? Whose values?

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Whose values? It depends…

Neoliberal

CriticalLiberal

Prinsloo, P. (2016, October 19). A social cartography of student data: Moving beyond #StudentsAsDataObjects –https://www.slideshare.net/prinsp/a-social-cartography-of-student-data-moving-beyond-studentsasdataobjects

Teleological• The potential for harm• The scope of consent

and recourse in cases of unintended harm are negotiated and agreed upon

Deontological• Basis for legal and

regulatory frameworks• Terms and Conditions• By consent and/or

contract• Works well in stable

environments

Two traditional categories of ethical approaches

(1) a utilitarian approach (deciding on an action that “provides the greatest balance of good over evil”);

(2) a rights approach (referring to basic, universal rights such as the right to privacy, not to be injured);

(3) a fairness or justice approach; (4) the common-good approach (where the welfare of the

individual is linked to the welfare of the community); and

(5) the virtue approach (based on the aspiration towards certain shared ideals)

An alternative framework

Velasquez, M., Andre, C., Shanks, T.S.J., & Meyer, M.J. (2015, August 1). Thinking ethically. Retrieved from

https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/thinking-ethically/

But…will it make a difference?


Ethics in learning analytics: Selected examples 2013-2017

So the question is not “should we have a framework/code of ethics as part of a

institutionilising learning analytics?” but …

under what conditions might this make a difference?

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Towards implementation• Policy and legal frameworks lag behind technological

developments and become obsolete after publication• There is a difference between policy rhetoric and

practiced reality• Policies are implemented if it falls within the comfort

zone of management or resembles the flavor of the day• Policies are used to distribute power, resources and

knowledge and result in categories of ‘winners’ and ‘losers’

“Ethics are the mirror in which we evaluate ourselves and hold ourselves

accountable” (emphasis added). Holding actors and humans accountable

still works “better than every single other system ever tried” (Brin, 2016)

The way forward: some considerations

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Part 2: Responding

• Points of departure• Decolonising methodologies

and ethics• Emancipatory research • Realities• In scope/Out of scope• Guiding principles• Towards implementation• (In)conclusions

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Page credit: https://www.amazon.com/Ethical-Futures-Qualitative-Research-International/dp/1598741411

Page credit: https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-sovereignty

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“If you have come to help us, you can go home. If you have come to accompany us, please

come. We can talk”

Glesne, C. (2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an agenda (pp. 169-178). Canberra, Australia: Australian National University Press. Retrieved from https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-sovereignty

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“The question of whose interests are served is central. And of course, there is clear advantage for those who collect

and control the data and information over those who provide the data and seek to benefit from that

contribution”

First Nations Information Governance Centre (FNIGC). (2016). Pathways to First Nations’ data and information sovereignty. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an agenda (pp. 139-156). Canberra, Australia: Australian National University Press. Retrieved from https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-

research-caepr/indigenous-data-sovereignty

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“The governance of data – that is, who has the power and authority to make rules and decisions about the design,

interpretation, validation, ownership, access to and use of data – has emerged as a site of contestation between

indigenous peoples and the colonial settler states within which they reside.”

Smith, D.E. (2016). Governing data and data for governance: the everyday practice of Indigenous sovereignty. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an agenda (pp. 117-135). Canberra, Australia: Australian National

University Press. Retrieved from https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-sovereignty

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“When institutions use race, ethnicity, age, gender, or socioeconomic status to target students for

enrolment or intervention, they can intentionally, or not, reinforce… inequality”

Ekowo. M., & Palmer, I. (2016). The promise and peril of predictive analytics in higher education. New America. Retrieved from https://www.newamerica.org/education-

policy/policy-papers/promise-and-peril-predictive-analytics-higher-education/

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Emancipatory research aims to prevent quantified, coded, “shallow, monocled gazes” and embraces an ethics of reciprocation that “gives back ownership of

knowledge and material benefit to those participating in research.”

Swartz, S. (2011). ‘Going deep’and ‘giving back’: strategies for exceeding ethical expectations when researching amongst vulnerable youth. Qualitative Research, 11(1), 47-68.

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How do our current realities shape our understanding of the ethical implications of the collection, analysis and use of student data?

Realities pertaining to student data, sources of data, quality of data, collection and analysis processes, tools used in the collection and analysis, the use of findings,

and governance of the collection, analysis and use

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What student data can we collect and what data should not/cannot be collected?

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Towards an ethics of care

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Towards an ethics of care:Principle 1: The moral relational duty of learning analyticsPrinciple 2: Defining student success in the nexus of student, institution and macro-societal agencies and contextPrinciple 3: Understanding data as framed and framingPrinciple 4: Student data sovereigntyPrinciple 5: AccountabilityPrinciple 6: TransparencyPrinciple 7: Co-responsibility

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Principle 1: The moral relational duty of learning analytics

Principle 1: The moral, relational duty of learning analytics

“If you have come to help us, you can go home. If you have come to accompany us,

please come. We can talk”

Glesne, C. (2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an agenda (pp. 169-178). Canberra, Australia: Australian National University Press. Retrieved from

https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-sovereignty

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Principle 2: Defining student success in the nexus of student, institution and macro-societal agencies and context

ProcessesInter & intra-

personaldomains

Modalities:• Attribution• Locus of control• Self-efficacy

ProcessesModalities:

• Attribution• Locus of control• Self-efficacy

DomainsAcademic OperationalSocial

TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES

THE STUDENT AS AGENTIDENTITY, ATTRIBUTES, HABITUS

Success

THE INSTITUTION AS AGENTIDENTITY, ATTRIBUTES, HABITUS

SHAPING CONDITIONS: (predictable as well as uncertain)

SHAPING CONDITIONS: (predictable as well as uncertain)

Choice, Admission

Learning activities

Coursesuccess

Gradua-tion

THE STUDENT WALK Multiple, mutually constitutive interactions between student,

institution & networks

FIT

FIT

FIT

FIT

Employ-ment/

citizenship

TRANSFORMED STUDENT IDENTITY & ATTRIBUTES

FIT

FIT

FIT

FIT

FIT

FIT

FIT

FIT

Retention/Progression/Positive experience

(Subotzky & Prinsloo, 2011)

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Principle 3: Understanding data as framed and framing

Source link: https://twitter.com/SwedishCanary/status/878622084141690881

Principle 4: Student data sovereignty

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Principle 4: Student data sovereignty (cont.)

Student data is not something separate from students’ identities, their histories, their beings. This framework accepts that data is an integral, albeit informational part of students being. Data

is therefore not something students own but rather are. Students do not own their data but

are constituted by their data.

Floridi, L. (2005). The ontological interpretation of informational privacy. Ethics and Information Technology, 7(4), 185-200.

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• Students have a right to control what personalised data is collected from them, for what purposes, by whom, and how it will be stored, governed

• Students have the right to access the data we have of them, to know who accessed their data, and how their data was used

• Students have a right to know what the rationale/criteria are for how we categorise them, our ‘regimes of truth’ and to engage with us to make sense of their data

• We should think past the binary of opting in/out – there are different nuances and possibilities

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Students should have access to supported and transparent recourse when (1) they allege harm as a result of the collection, analysis and use of

their data; (2) they did not have an opportunity to provide context or more

information on the data collected and used for the alleged infringement;

(3) their choices are limited without a clear explanation on the rationale and appropriateness of the limitation as well as a how the limitation will affect their learning journey; and

(4) when they have not been informed of the collection, analysis and use of their data outside of the original consent provided and original purpose for the collection of their data.

Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64, 881-901. DOI: 10.1007/s11423-016-9463-4 http://link.springer.com/article/10.1007/s11423-016-9463-4

Principle 5: Accountability

An interpretative multiple-case study: Indiana University, Open University (UK) and the University of South Africa (Unisa)

Typology: Learning analytics as…

Approval/oversight/accountability

Research Formal, well-defined processes

An emerging form of research

Undefined, unclearOur current processes do not allow for any oversight

Scholarship of teaching and learning

Undefined, unclearConsent normally not required. Oversight? Student complaints, feedback

Dynamic, synchronous and asynchronous sense-making

Undefined, unclear

Automated Undefined, unclear

Participatory process and collaborative sense-making

All stakeholders are involved – may need broad, blanket consensus at the beginning of each course – oversight by the highest academic decision making body. Important here is the role of students as collaborators in sharing interpretation, governance, quality assurance, integrity of data

(1)Humans

perform the task

(2)Task is

shared with algorithms

(3)Algorithms

perform task: human

supervision

(4)Algorithms

perform task: no human

input

Seeing Yes or No? Yes or No? Yes or No? Yes or No?

Processing Yes or No? Yes or No? Yes or No? Yes or No?

Acting Yes or No? Yes or No? Yes or No? Yes or No?

Learning Yes or No? Yes or No? Yes or No? Yes or No?

Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html

Human-algorithm interaction in the collection, analysis and use of student data

http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html

Principle 6: Transparency

Students have a right to know what data are collected, by whom, when, for what purposes, how they can verify the data, how long the data will be kept and who will have access to the data for which purposes

Image credit: https://pixabay.com/en/water-drop-blue-liquid-rain-clean-880462/

If they don’t know that we collect their data, the scope and purpose of the collection, how we will use their data and how it will impact on their learning journeys, it is not learning analytics but spying…

Principle 7: Co-responsibility

Our students’ journeys are intimately weaved into our (institutional) stories. In

the light of the asymmetrical power relationship, we have a bigger

responsibility

Image credit: https://pixabay.com/en/basket-weave-concentric-zen-circle-379867/

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Towards implementation

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‘Responsible’ learning analytics is found in the nexus between their stories and ours. We cannot afford to ignore the fact that it is their data, their aspirations, their learning journeys and that our data collection,

analysis and use may not tell the whole story.

(In)conclusions


Thank you

Paul Prinsloo Research Professor in Open Distance Learning (ODL)College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392Unisa, 0003, Republic of South Africa

T: +27 (0) 12 433 4719 (office)T: +27 (0) 82 3954 113 (mobile)

[email protected] Skype: paul.prinsloo59

Personal blog: http://opendistanceteachingandlearning.wordpress.com

Twitter profile: @14prinsp