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Summary Data Driven Dreams: Articles, lectures and workgroups
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Summary of all articles, lectures and workgroups for the course Data Driven Dreams. Grade: 8.5.
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InhoudsopgaveWeek 1: Data-Driven Technologies: What are they? ....................................................................................... 3
Lecture 1: The potential and challenges of data-driven technologies in healthcare ........................................... 3
Literature 1.......................................................................................................................................................... 7
Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: a revolution that will transform how we live, work
and think. Chapter 1: now. ............................................................................................................................. 7
Bensaude Vincent, B. (2014). The politics of buzzwords at the interface of technoscience, market and
society: The case of ‘public engagement in science’. Public understanding of science, 23(3), 238-253. ..... 11
Dalton, C., & Thatcher, J. (2014). What does a critical data studies look like, and why do we care? Society
and space. ..................................................................................................................................................... 15
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological,
and scholarly phenomenon. Information, communication & society, 15(5), 662-679. ................................ 19
Week 2: Data-Driven Technologies: How do they work? ............................................................................... 23
Lecture 2: Artificial intelligence ......................................................................................................................... 23
Q&A 2: ............................................................................................................................................................... 29
Literature 2........................................................................................................................................................ 30
Raghupathi, W. & V. Raghupathi (2014) "Big data analytics in healthcare: promise and potential" Health
Information Science and Systems2(3) .......................................................................................................... 30
Silver, N. (2012). Chapter 7: Role Models. In: the Signal and the Noise: why most predictions fail - but
some don't. p. 196-220................................................................................................................................. 35
Ziewitz, M. (2017). A not quite random walk: Experimenting with the ethnomethods of the algorithm. Big
Data & Society, 4(2) ...................................................................................................................................... 38
Working group 1+2 ........................................................................................................................................... 42
Week 3: Data-Driven Technologies: How can we use them in healthcare? .................................................... 44
Lecture 3: Machine learning for personal and precise care .............................................................................. 44
Literature 3........................................................................................................................................................ 48
Schutt, R. & C. O’Neil (2014). Chapter 1. Introduction: what is data science? In: Doing Data Science. Page 1
– 16. O’Reilly Media: Sebastopol, CA. ........................................................................................................... 48
Menger, V., M. Spruit, K. Hagoort & F. Scheepers. (2016). Transitioning to a Data Driven Mental Health
Practice: Collaborative Expert Sessions for Knowledge and Hypothesis Finding. Computational and
Mathematical Methods in Medicine. 9089321 ............................................................................................ 52
Baru, C. (2019). Data Science Needs a Clinical Program. .............................................................................. 56
Working group 3 ............................................................................................................................................... 58
Week 4: Data-Driven Technologies: What sort of knowledge do they produce? ............................................ 59
Lecture 4: The epistemology of big data ........................................................................................................... 59
Literature 4........................................................................................................................................................ 66
Anderson (2008). The end of theory. ........................................................................................................... 66
Stevens, M., Wehrens, R., & de Bont, A. (2018). Conceptualizations of Big Data and their epistemological
claims in healthcare: A discourse analysis. Big Data & Society, 5(2) ............................................................ 68
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1) ................. 75
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, Stevens, M., Wehrens, R. & de Bont, A. (2020). Epistemic virtues and data-driven dreams: On sameness
and difference in the epistemic cultures of data science and psychiatry. Social Science & Medicine 258:
113116. ......................................................................................................................................................... 83
Working group 4 ............................................................................................................................................... 88
Week 5 ......................................................................................................................................................... 91
Lecture 5............................................................................................................................................................ 91
Literature 5...................................................................................................................................................... 100
Rieder, G. (2018). Tracing Big Data imaginaries through public policy: the case of the European
Commission. In: Saetnan, A.R., I. Schneider & N. Green (2018). The politics and policies of big data: big
data, big brother? Routledge (pp. 89-110). ................................................................................................ 100
Custers, B., Dechesne, F., Sears, A. M., Tani, T., & van der Hof, S. (2018). A comparison of data protection
legislation and policies across the EU. Computer Law & Security Review, 34(2), 234-243......................... 105
Starkbaum, J. & Felt, U. (2019). Negotiating the reuse of health-data: Research, Big Data, and the
European General Data Protection Regulation. Big Data & Society, 6(2). .................................................. 108
Week 6 ....................................................................................................................................................... 113
Lecture 6: Ethics of data-driven technologies ................................................................................................. 113
Q&A week 6 .................................................................................................................................................... 123
Literature 6...................................................................................................................................................... 124
Mittelstadt, B. D., & Floridi, L. (2016). The ethics of big data: current and foreseeable issues in biomedical
contexts. Science and engineering ethics, 22(2), 303-341. ........................................................................ 124
Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 2053951714559253 ..................................... 133
Zook, M., Barocas, S., Crawford, K., Keller, E., Gangadharan, S. P., Goodman, A., ... & Nelson, A. (2017).
Ten simple rules for responsible big data research. PLoS computational biology, 13(3) ........................... 137
Grote, T., & P. Berens. (2020). On the ethics of algorithmic decision-making in healthcare. Journal of
Medical Ethics 46(3): 205-211. ................................................................................................................... 140
Morley, J., Machado, C.C., Burr, C., Cowls, J., Joshi, I., Taddeo, M. and Floridi, L., (2020). The ethics of AI in
health care: A mapping review. Social Science & Medicine, p.113172. ..................................................... 146
Workgroup 6 ................................................................................................................................................... 151
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,Week 1: Data-Driven Technologies: What are they?
Lecture 1: The potential and challenges of data-driven technologies in healthcare
Part I:
Expectations about data-driven technologies
Drivers behind data-driven technologies
- Exponential increase of healthcare data (e.g., quality measures, electronical patients’
dossiers)
- Increase computational abilities > developments of ICT
Expectations on data driven technology are very high. Example: google flu trends, claim was that they
could identify flu outbreaks earlier and therefore could reduce the impact. Yet predictions were
inaccurate à overestimation of what big data can achieve.
Increase rise of concerns and negative media attention. Mostly concerning privacy issues and
implications for individuals (what if insurance or commercial companies have access to it?).
Data-driven technologies matter
- All over the news
- High expectations but also many concerns
Data-driven technologies: definitions and demarcations
Big Data: the classic definition > technical
Three V (2001): 3 main characteristics
- Volume: enormous quantities of data that become available
- Velocity: very rapid way, almost in real time
- Variety: big data is about the ability to combine various sources, from un- and semi-
structured and structured
Big data is not only about size, but also as much about speed of analysis and variety of sources. Size is
relative.
Big Data & Artificial Intelligence?
- Artificial intelligence: “The theory and development of computer systems able to perform
tasks normally requiring human intelligence, such as visual perception, speech recognition,
decision-making, and translation between languages.” (English Oxford Living Dictionary)
o AI is a subfield of computer science; its goal is to enable the development of
computers that are able to do thing normally done by people. In particular things
associated with people acting intelligently. This can be about visual perception, e.g.,
categorizing images, speed recognition, decision-making, translation between
languages. AI is usually seen as a very broad umbrella term.
- Machine learning: subset of AI. Consists of the techniques that enables computer to figure
things out from the data. Machine learning is not about mimicking human behaviour, but
about mimicking how humans learn. So, it’s different from early learn-based AI systems.
o Differences between early rules-based AI systems and machine/deep learning:
compare with reading, you don’t sit down and learn all grammar. You begin with
simple book. You learn spelling and grammar from your reading. Another way:
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, processed a lot of data and you learn from it. This process is what machine learning
tries to mimic.
- Deep learning: a subset of machine learning > enables computers to solve more complex
problems. This is done by trying to mimic the human brain through neural networks. Deep
learning AI learn through an artificial human network and allows the machine to analyse data
in a structure as humans do. DL machines don’t acquire a human programmer to tell them
what to do. If a ML-algorithm return an inaccurate prediction than an engineer needs to step
in and make adjustments. But in DL an algorithm can determine on their own if a prediction
is accurate or not.
AI: about development of computer systems that are able to do thing that are normally done by
people > engineering of making intelligent machines and programs
ML: subset of AI, which is about mimicking how humans learn. So, learning from data, getting
progressively better > ability to learning without being explicitly programmed
DL: approach in AI that enables computers to solve more complex problems by mimicking neural
networks in human brains > learning based on deep neural network
Example:
- ML: does not rely on rules-based programming, easy example is on demand streaming
service. For the service to make a decision on which series to recommend, ML algorithms
associate preferences with other viewers who have similar taste.
- DL: autonomous vehicles. Some DL models specialise in street signs while others specialise in
recognizing pedestrians. As a car navigates it down the road it can be informed by millions of
AI models, that allow the car to act.
Because DL systems get so complex it becomes impossible to show how a system reaches a decision
à questions on regulation, accountability, ethics.
“I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. If you have a
large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton
of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel”
à A good DL model needs a lot of data to learn from and need a critical model to make sense
Critical Data Studies: Big Data as a socio-technical phenomenon
“Big Data is a cultural, technological, and scholarly phenomenon that rests on the interplay of
technology (maximizing computation power and algorithmic accuracy to gather, analyse, link, and
compare large data sets), analysis (drawing on large data sets to identify patterns in order to make
economic, social, technical, and legal claims) and mythology (the widespread belief that large data
sets offer a higher form of intelligence and knowledge that can generate insights that were previously
impossible, with the aura of truth, objectivity, and accuracy) (boyd & Crawford, 2012: p. 663).
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