Fleeing from Frankenstein and meeting Kafka on the way: Algorithmic decision-making in higher education

Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367
By Paul Prinsloo (@14prinsp)
University of South Africa (Unisa)
Presentation at NUI Galway, 22 September 2016
Fleeing from Frankenstein
and meeting Kafka on
the way:
Algorithmic
decision-making in
higher education

• This presentation provides some of my thoughts in a
submission to the Special Issue of E-Learning and Digital
Media with as theme “Learning in the age of algorithmic
cultures.” Editors: Petar Jandrić, Jeremy Knox, Hamish
Macleod and Christine Sinclair.
• I don’t own the copyright of any of the images in this
presentation. I therefore 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
Acknowledgements

My own positionality/location
Image credit: https://upload.wikimedia.org/wikipedia/commons/b/bf/Maze_01.svg

Image credit: http://www.northsideservices.com/wp-
content/uploads/2015/11/Revolving_door-base.jpg
Persisting concerns that we have
not solved the student
departure/attrition question

Image credit: https://pixabay.com/en/stress-anxiety-depression-stressed-1084525/
Increased reporting to an ever-growing number
of stakeholders (the ‘audit’ society), the need
for evidence and the necessity to ensure the
effective allocation of resources

Image credit: https://s-media-cache-
ak0.pinimg.com/736x/5c/58/07/5c58072b5f003d7a69b129cb6f8055b6.jpg

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

Image credit: https://en.wikipedia.org/wiki/Triage
Web page: http://www.chronicle.com/article/Are-Struggling-College/235311

How can we use algorithmic decision-making
in higher education to ensure, on the one
hand, caring, appropriate, affordable and
effective learning experiences, and on the
other hand, ensure that we do so in a
transparent, accountable and ethical way?

Overview of this presentation
• The social imaginaries surrounding algorithms
• Mapping the potential of and issues surrounding
algorithmic decision-making in the context of higher
education, student success, data and the role of data
scientists
• Exploring two frameworks (Danaher, 2015; Knox, 2010)
for making sense of algorithmic decision-making in
higher education
• Considering pointers for a way forward
• (In)conclusions

Image credits:
Frankenstein - https://www.amazon.com/Frankenstein-Mary-
Shelley/dp/1512308056/ref=sr_1_4?s=books&ie=UTF8&qid=1474302559&sr=1-4&keywords=frankenstein
Mary Shelley - https://en.wikipedia.org/wiki/Frankenstein
The Trail - https://www.amazon.com/Trial-Franz-Kafka/dp/1612931030/ref=sr_1_1?ie=UTF8&qid=1474302647&sr=8-
1&keywords=the+trial+kafka

In the social imaginary algorithms
simulataneously repel and attract us
Image credit: https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/

While Artificial Intelligence (AI) “tools are
producing compelling advances in complex tasks,
with dramatic improvements in energy
consumption, audio processing, and leukemia
detection”, we are also faced with the reality that
“AI systems are already making problematic
judgements that are producing significant social,
cultural, and economic impacts in people’s
everyday lives” (Crawford and Whittaker, 2016,
par. 1).

“Just as we learn our biases from the world around us, AI
will learn its biases from us” (Collins, 2016)

“Technology has no ethics” (Brin, 2016)
“Adapting to a new technology is like a love
affair… The devices, apps and tools seduce us …
and any doubts or fears we had melt away”
(Ellen Ullman as quoted by Miller, 2013)

Algorithms
“… encode human prejudice, misunderstanding,
and bias into automatic systems that increasingly
manage our lives. Like gods, these mathematical
models are opaque, their workings invisible to all
but the highest priests in their domain:
mathematicians and computer scientists. Their
verdicts, even when wrong or harmful, are beyond
dispute or appeal. And they tend to punish the poor
and the oppressed in our society, while making the
rich richer” (O’Neill, 2016a, par. 14).

Algorithmic imaginaries emerges from “a very
specific economic and innovative culture”
associated with Silicon Valley technology
companies, and they privilege their
originators’ “techno-euphoric interpretations
of Internet technologies as driving forces for
economic and social progress” (Mager, 2015,
pp. 5-6)
Image credit:
https://upload.wikimedia.org/wikipedia/commons/2/23/Firmen_im_Silicon_Valley.jpg

Image credits: Amazon.com

Higher
Education in a
changing, fluid
landscape
Student
retention and
success
Data
Data Scientists
Algorithms
No ICTs Individual &
social
well-being
dependent
on ICTs
Individual &
social well-
being
related to ICTs

Data
Data Scientists
Algorithms
No ICTs Individual &
social
well-being
dependent
on ICTs
Humanity - Technology - Nature
Humanity - Technology - Technology
Technology - Technology - Technology
(Danaher, 2016; Floridi, 2014)
Individual &
social well-being
related to ICTs
The “in-betweenness” of technology

• Rationalisation, commercialisation and outsourcing
• Funding constraints – funding follows performance rather
than preceding it
• Evidence-based management and the rise of the
administrative university
• Our quantification fetish and obsession with data
• Increasingly online and digital, increasingly dependent on
ICTs
Higher
Education in a
changing, fluid
landscape
Student
retention and
success
Data
Data Scientists
Algorithms

Quality
CostAccess
The iron triangle in
education
• Impact on increased
access
• Cost of
teaching/support/care
• Quality

What are the potential, challenges and ethical
implications of using algorithms to address issues
of cost, quality, access and care?

Higher Education
in a changing,
fluid landscape
Student
retention and
success
Data
Data Scientists
Algorithms
• Selecting students who applied for access to higher education
– admission requirements. Criteria?
• Ensuring optimum ‘fit’ between individual student
characteristics, background, aspirations and chosen program
and/or courses
• Allocating resources appropriately, efficiently and ethically to
individual students. Criteria?
• Ensuring completion in the ‘quickest’ possible time and return
on investment

What are the potential, challenges and ethical
implications of using algorithms to admit
students, chose ‘fit’, allocate resources and enable
the most affordable, safest (and quickest) route to
success?
Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-
what-does-its-complicated-mean

Higher Education
in a changing,
fluid landscape
Data
Data Scientists
Algorithms
Student
retention and
success
• Our data fetish – the more, the better
• The belief that data are neutral and our samples are [n=all]
• The belief that it is sufficient to spot patterns and not to
understand/investigate the ‘why’
• The use of data points and variables as proxies and mistaking
correlation for causation
• Our assumption that our students’ digital lives and clicks
provide us with a holistic/complete picture. What about their
non-digital lives? What about what we cannot measure?

Higher Education
in a changing,
fluid landscape
Student
retention and
success
Data
Data Scientists
Algorithms
• Data analysis as a “black art” (Floridi, 2012)
• Data scientists as the “high-priests of algorithms”
(Dwoskin, 2014), the “engineer[s] of the future”
(Van der Aalst, 2014) and data scientists as “rock
stars and gods” (Harris, Murphy & Vaisman, 2013),
the “hottest” job title (Chatfield, & Shlemoon &
Redublado, 2014, p. 2)

Higher Education
in a changing,
fluid landscape
Student
retention and
success
Data
Data Scientists
Algorithms
Who are they … really? (Harris, Murphy and Vaisman, 2015)
[sample 250]
• Data developers (strong on machine learning, programming,
good overall)
• Data researchers (disproportionally strong in statistics)
• Data creatives (all-rounders – statistics, machine learning,
programming)
• Data businesspersons (disproportionally strong in business,
then stats)

Higher Education
in a changing,
fluid landscape
Student
retention and
success
Data
Data Scientists
Algorithms
Data analysis as séance with the
data scientist as interlocutor,
scientist, charlatan, oracle
and/or medium (Prinsloo, 2016)
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VontR5jZTEE/U7Ga6mUcRvI/AAAAAAAAA7s/qNq_toHlh34/s1600/mesas-girantes.jpg

Higher Education
in a changing,
fluid landscape
Student
retention and
success
Data
Data Scientists
Algorithms
Image credit: https://encrypted-
tbn1.gstatic.com/images?q=tbn:ANd9GcREGZ_tw83Gm-Ma-WC9-
SVq8H4a20jJ6gKyLw3dyySJciCF52YC

(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

Preliminary seven dimensions of surveillance
(Knox, 2010)
1. Automation
2. Visibility
3. Directionality
4. Assemblage
5. Temporality
6. Sorting
7. Structuring

1. Automation
Key questions Dimensional intensity
What is the timing of the
collection?
Intermittently/i
nfrequently
Continuous
Locus of control? Human Machine
Can it be turned on and
off (and by whom?)
All the
monitoring can
be turned
on/off
None of the
monitoring can be
turned off

2. Visibility
Is the surveillance
apparent and transparent?
All parts
(collection,
storage,
processing and
viewing) are
visible
None of the
monitoring is visible
Ratio of self-to-surveillant
knowledge?
Subject knows
everything the
surveillant
knows
Subject does not
know anything that
the surveillant knows

3. Directionality
What is the relative power
of surveillant to subject?
Subjects hold all
the power
Surveillant holds all
the power
Who has access to
monitoring/recording/
broadcasting functions?
Subjects Surveillant

4. Assemblage
Medium of surveillance Single medium
(e.g. text)
Multimedia
Are the data stored? No Yes
Who stores the data? Subject or
collector
Third party

5. Temporality
When does the monitoring
occur?
Confined to the
present
Combines the present
with the past
How long is the monitoring
frame?
One, isolated,
relatively short
frame (e.g. test)
Long periods, or
indefinitely
Does the system attempt to
predict future
behavior/outcomes
No – only
assessment of the
present
Present + past used to
predict the future
When are the data available? All of the data
available only after
event is completed
Available in real-time
and experienced as
instantaneous

6. Sorting
Are subjects’ data
compared with other
data – other individuals/
groups/ abstract
configurations/ state
mandates?
None Other data are used
as basis for
comparison

7. Structuring
Are data used to alter the
environment (i.e.
treatment, experience,
etc.)?
Not used Used to alter the
environment of all
subjects
Are data used to target
the subject for different
treatment that they would
otherwise receive?
No data are used
as basis for
differing
treatment
Based on data,
treatment is
prescribed

Higher
Education in a
changing, fluid
landscape
Student
retention and
success
Data
Data Scientists
Algorithms

The way forward: some considerations
• Human oversight over algorithmic design and
application: ‘straddling’ “the boundary
between resistance and accommodation”
(Danaher, 2016, p. 19)
• Epistemic enhancement of humans
• Sousveillance
• Individual (integrative and non-integrative)
partnerships with algorithms
(Danaher, 2016)

“A simplistic over-reliance on algorithms is heavily
flawed.”
Web page:
http://www.nature.com/polopoly_fs/1.20653!/menu/main/topColumns/topLeftColumn/pdf/537449a.p
df

• Does the idea violate the human rights of
anyone involved?
• Does this idea substitute human
relationships with machine
relationships?
• Does this idea put efficiency over
humanity?
• Does this idea put economics and profits over the
most basic human ethics?
• Does this idea automate something that should not
be automated? (Leonard, 2016)

• If “technology has no ethics” (Brin, 2016) then we should not
rely on ethics considering human history and “taking
perspective from the long ages of brutal, feudal darkness
endured by our ancestors.”
• It is not that ethics are not important and that frameworks do
not serve any purpose but to what extent do ethics and
frameworks affect “the worst human predators, parasites and
abusers?”
• “Ethics are the mirror in which we evalaute oursleves and hold
outselves accountable” (emphasis added). Holding actors and
humans accountable still works “better than every single
other system ever tried.”

The way forward (some pointers)
• “Solid values and self-regulation reign in
only the scrupulous” (p. 206)
• We need laws but we must “reevaluate our
metrics of success…. What should we be
counting?” (p. 206). What are the hidden
costs and non-numerical values?
• Any data collected, must be based on opt-in
• We need to question the assumptions, limitations and implications
of the proxies we use (e.g. zip codes)
• The role and scope of transparency, openness, accountability and
audits. Also audit fairness…
• There are some things we cannot and should not attempt to
model (e.g. teacher effectiveness)

• While we have to be concerned about efficiency, it is not the
only question we should ask, and most probably not the most
important. We also need to consider whether a curriculum,
pedagogy and assessment are appropriate (Biesta, 2007, 2010)
• Education is an open and recursive system (Biesta 2007, 2010),
where student success is the result of a dynamic interaction
between context, students, institutional efficiencies,
epistemological access, resources and support at a particular
moment in time (Subotzky & Prinsloo, 2011)
• “Who benefits from algorithmic education technology? How?
Whose values and interests are reflected in its algorithms?”
(Watters, 2016; emphasis added)

• Rule 1: Do no harm.
• Rule 2: Read rule 1
• Students have a right to know who designs our algorithms, for
what purposes, using what data, how they are affected, and
make an informed decision to opt-in
• Provide students access to information and data held about
them, to verify and/or question the conclusions drawn, and
where necessary, provide context
• Provide access to a neutral ombudsperson
• Ethical oversight? Accountability?
(See Prinsloo & Slade, 2015; Slade & Prinsloo, 2013; Willis, Slade
& Prinsloo 2016)

(In)conclusions
Who benefits,
under what
conditions, what
are the
(un)intended
consequences
and how and who
will keep us
accountable and
transparent?

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 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
T: +27 (0) 82 3954 113 (mobile)
prinsp@unisa.ac.za
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp

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