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SOCM033/PHLM011-无代写
时间：2023-03-29
SYLLABUS: DATA GOVERNANCE AND ETHICS
SOCM033/PHLM011 (15 credits)
SOCM038/PHLM012 (30 credits)
University of Exeter
TERM 2, Academic year 2022-23
Credit value: 15 [assessed via a 3000-word essay] or 30 [assessed via a 6000-word essay]
Module Convenor (Term 2):
Stephan Guttinger
Email: s.m.guttinger@exeter.ac.uk
Office: F11, Byrne House, Streatham Campus
Office hours:
• Tuesdays 3pm-4pm, F11, Byrne House (please use this link to pre-book a slot:
https://calendly.com/guttinger/office-hour?month=2023-01
• Thursdays 10am-11am, online Zoom drop-in (no pre-booking required).
https://Universityofexeter.zoom.us/j/99757755039?pwd=ZGdLdnQwVTRpdF
pjeGozcFpmdEpmQT09
Teaching assistant:
Oliver Roberts-Garratt
email: or245@exeter.ac.uk
General Description of the Course
This module addresses the growing attention to the social embedding of data across
different settings, from business to policy and government, from sports to health and
climate change, and the challenges that such embedding brings both for the governance
and regulation of data flows and for the technical management and responsible use of data.
Data science, machine learning, artificial intelligence and “big data” have become central to
every aspect of social life. How can these complex and powerful technologies best be
managed and governed for the benefit of society now and in the future? In this module you
will: (1) identify some of the main risks and ethical/legal challenges involved in the
widespread automation and digitalisation of services characterising 21st century life (for
example, the clash between individual desire for privacy, frameworks for data ownership
and the institutional commodification of personal data); (2) examine whether and how such
concerns can be handled; and (3) discuss the responsibilities of data scientists and other
producers of technologies for data analysis towards their responsible use.
Aims of the module
This module aims to equip you with the knowledge and skills to reason around the complex
issues of data governance and ethics. The module introduces the key ethical questions
around the use of big data and associated technologies such as machine learning and
artificial intelligence, and places them in the broader framework of contemporary digital
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society, including its reliance on automation, social media and related platforms for
communication and service provision. We will review concerns around data protection,
surveillance, open data, citizen science and use (and misuse) of social data. We will explore
the legal and social contexts for decision-making through relevant scholarship and a number
of real-world case studies, which you will be invited to identify and discuss with your peers.
We will examine case studies from end to end, beginning with real-world examples of data
collection, storage, and analysis, following the possible (intended and unintended) ways in
which data is subsequently used to support decision-making, and considering the ethical
and legal issues that arise at each stage.
Intended learning outcomes
Module Specific Skills and Knowledge:
1. Evaluate the choices made at each stage of a data handling and the associated legal,
ethical and governance issues.
2. Identify key social concerns in relation to digital tools within contemporary society.
3. Understand the core regulatory and legislative frameworks that govern collection,
storage, processing and communication of data.
4. Appreciate the differing costs and benefits associated with use of data when
considered from perspectives of data user, data provider, decision-maker and
regulator.
Discipline Specific Skills and Knowledge:
5. Appreciate the wider social context of data science and related technologies,
including current issues such as open data, data protection, automated data analysis,
and misuse of data and related analytics.
6. Critically reflect on the ethical considerations associated with use of data within
organisations and governments.
7. Display understanding of key contributions to scholarship on data studies and the
digital society.
Personal and Key Transferable/ Employment Skills and Knowledge:
8. Effectively communicate complex ideas using written and verbal methods
appropriate to the intended audience.
9. Demonstrate cognitive skills of critical and reflective thinking.
10. Demonstrate effective independent study and research skills.
Structure of the module
This module will be taught via weekly one-hour lectures, recordings of which will posted on
the ELE page of the module. There will also be weekly one-hour seminars, in which the
lecturers and PTAs will meet you to discuss the readings and lecture content and do group-
work together. There will be several seminar groups, so that we can keep their size small
enough for discussion, and you will be assigned to one group by the timetabling services.
Seminars and lectures will be conducted in person.
3
Key deadlines
• Before each seminar: read required readings
• Week 6: Choose topic for presentation
• Weeks 8 and 9: Group presentations during seminars
• Week 10: Choose essay topic
• 2pm Tuesday 2 May 2023: Submit essay (100% of final mark)
[for details on these assignments, see page 4]
Preparation for Seminars
Attendance to the seminars is essential to pass the course, also because your lecturers will
discuss at length requirements for the essay as we go along. Before you join each seminar,
you need to prepare as follows:
1. Attend lecture
2. Read the core readings for the week, as indicated on the syllabus and available
digitally on the ELE page
3. Read and reflect on ‘Data Story’ that will be discussed in the seminars
Reading Materials
In addition to the core readings, there are recommended readings indicated for each lecture
in the syllabus below, which you are encouraged to study before we meet in class and will
help you to decide whether to focus your presentation and essay on that topic. To prepare
for your essay, you are also encouraged to research the topics that most interest you and
identify relevant scholarly materials on those. This is a fast-changing and expanding field,
and new relevant publications are sure to appear during the duration of the module. By the
end of the module, you will hopefully know more about your chosen topic than the
lecturers!
During some of the seminars, we will also be considering real-world “data stories” that
highlight some of the issues discussed in the lectures. You will be asked to read those stories
and discuss them in groups. This will give you the opportunity to discuss how the concepts
you are learning play out in real life, as well as giving you a model for the kind of analysis
required for the written essay.
4
Assessment:
Formative assessment (no marks):
Presentation given during seminars in groups of up to 5 students
• Presentation assessment: Your presentation will be discussed during seminars, and
you will receive oral feedback from the lecturer/PTA and your peers. There are no
marks for this assessment.
• Presentation topic: Identify a case of data work and analyse its social, political, and
ethical context and implications. You will need to refer to at least three of the
compulsory readings for this module in your analysis of the case you chose.
• Presentation format: Presentations must be 10 minutes long (but not longer). You
are welcome to use PowerPoint slides. Each member of the group needs to actively
participate in the making of the presentation and take a turn in presenting.
• Presentation schedule: How to prepare and the presentation schedule will be
discussed during week 1 of the module. You will have to choose a presentation topic
by week 6. Presentations will take place during seminars in weeks 8 and 9.
Summative assessment (determines final mark):
Essay to be submitted through the e-BART system by 2pm on Tuesday 2 May 2023.
• Topic: Consider one or more institutional, ethical and/or social concern involved in
using data within a specific project or case study. This can be a case that you are
familiar with from your own experience; or it can be a case you have read about
(e.g., in the news or in scholarly work) and whose details you are interested in
exploring in more detail. The essay needs to explore the implications of the issue(s)
at hand, the challenges they pose and the way to overcome those challenges. You
need to be as specific as possible about the details of the case you chose: vague
discussions of data ethics “in general” won’t be acceptable. You are welcome to
write your essay on the same topic on which you did a presentation, but this is not
compulsory.
• Sources and references: You are asked to reference at least THREE of the readings
listed in this syllabus; and complement those sources with additional ones of
specific relevance to your chosen topic.
• Schedule: We will discuss topics in seminar groups during week 10, so you will need
to choose a topic for your essay by the end of week 9 at the absolute latest.
• Assessment: The essay constitutes 100% of the module assessment.
• Length:
o 3000 words (excluding bibliography) for students taking the 15 credits
version of the module SOCM033/PHLM011
o 6000 words (excluding bibliography) for students taking the 30 credits
version of the module SOCM038/PHLM012
• Marking criteria: In week 2 of the term, you will find a video on ELE (under
“Assessment”) containing a review of the criteria against which your essay will be
marked, and a discussion of what it takes to write a first-rate essay. A summary of
marking criteria is also provided below and on the ELE page for the module. The
lecturers will discuss these criteria with you during seminars.
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Criteria for Essay Marking (summative)
Focus
How well-formulated is your main claim/question?
Argument
How clear and convincing are the arguments you offer? Is there evidence of original
research, independent thinking?
Background
How well did you prepare (is there evidence of relevant background reading, including both
scholarship on data ethics ad governance from the syllabus and sources documenting your
specific topic)?
How well do you understand the issues?
Structure and Style
Is your essay structured in a logical, efficient manner? Is your writing clear and well-
structured?
Note:
• We are not looking for evidence of wide-ranging background reading. Rather, we
want to see evidence for your claims and a good understanding of the sources you
cite. Please make sure that you use at least THREE different scholarly references
from the syllabus in a meaningful way. You have to work with and discuss these
references, not just drop the names in your essay.
• Originality does not mean breaking completely new ground. Rather, it involves
providing a personal, well-reasoned take on an issue that matters to you. You should
come up with your own views on the issue you are discussing. Merely summarizing
what others have said won't be enough.
• While we do not expect you to use a wide range of citations, we do ask for rigorous
citation of the sources that you do use. In text, ALWAYS refer to author, year of
publication and page number if referring to a specific argument. E.g. (Kelly 2009,
27) Otherwise your writing may count as plagiarism. This goes for direct quotes as
well as for content of arguments or empirical evidence.
• ALWAYS add ALL and ONLY the sources you have used. Make sure to have Author,
Year, Title, Journal/Publishing house, Volume, Page Numbers or URL for online
sources.
• The HARVARD REFERENCE STYLE is the compulsory at the university. See the style
instruction sheet on the ELE page for this module.
Note about submission: Please be sure to submit your work at least three hours before the
deadline to avoid any difficulties. If you wish you may use the Turnitin link on the module
ELE page to check your work for potential referencing or plagiarism problems before
submitting it to E-BART. Note that all assignments will be checked through Turnitin after
submission. If you have doubts about plagiarism, make sure that you take the SPA Academic
Honesty ELE quiz available here: https://vle.exeter.ac.uk/course/view.php?id=1977
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Course overview
Week Topic Readings
1
(16.
Jan)
Lecture:
Introduction: Why
Care About Data?
1.A Introduction to
the module themes,
structure and aims
1.B The
phenomenon of
Datafication
Seminars:
Discussion of
module
aims/assessment
and concept of
‘datafication’
Core reading:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 1
Recommended readings:
• Floridi L., 2014. The fourth revolution: how the infosphere is
reshaping human reality. OUP, Oxford, UK. Introduction.
• Hey, T., Tansley, S. and Tolle, K., ed., 2009. The fourth paradigm:
Data- intensive scientific discovery. Redmond, WA: Microsoft
Research. Available Open Access here:
http://research.microsoft.com/en-
us/collaboration/fourthparadigm/4th_paradigm_book_complete_lr
.pdf
• Van Dijck, J., Poell, T. and De Waal, M., 2018. The Platform Society:
Public Values in a Connective World. Oxford University Press.
• Ebeling, M.F., 2016. Healthcare and Big Data: Digital Specters and
Phantom Objects. New York: Palgrave Macmillan.
2
(23.
Jan)
Lecture: Big Data
in context
A discussion of the
mythology of Big
Data
Seminars:
Data Story 1 – Big
Data on Consumer
Habits
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 2
• Anderson, C., 2008. The end of theory: The data deluge makes the
scientific method obsolete. Wired Magazine.
http://archive.wired.com/science/discoveries/magazine/16-
07/pb_theory
Recommended readings:
• Kitchin, R. and McArdle, G., 2016. What makes Big Data, Big Data?
Exploring the ontological characteristics of 26 datasets. Big Data &
Society, 3(1), pp.1-10.
https://doi.org/10.1177%2F2053951716631130
• Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution
that will transform how we live, work, and think. New York: Eamon
Dolan/Houghton Mifflin Harcourt.
• boyd, d., and Crawford, K., 2012. Critical questions for big data:
Provocations for a cultural, technological, and scholarly
phenomenon. Information, communication & society, 15(5),
pp.662-679. http://dx.doi.org/10.1080/1369118X.2012.678878
• Leonelli, S., 2014. What difference does quantity make? On the
epistemology of Big Data in biology. Big data & society, 1(1), pp.1-
11. https://doi.org/10.1177%2F2053951714534395
7
• Leonelli, S., 2020. Scientific Research and Big Data. Stanford
Encyclopaedia for Philosophy.
https://plato.stanford.edu/entries/science-big-data/
• Daston, L., 2018. Sciences of the Archive. Chicago University Press.
Introduction and Epilogue.
• Porter, T., 1995. Trust in Numbers. Princeton University Press.
Introduction, Chapters 1-4.
• Edwards, P.N., 2010. A vast machine: Computer models, climate
data, and the politics of global warming. Cambridge, MA: MIT
Press.
• Kelty, C.M., 2008. Two bits: The cultural significance of free
software. Durham, NC: Duke University Press.
• Ensmenger, N.L., 2010. The computer boys take over: Computers,
programmers, and the politics of technical expertise. Cambridge,
MA: MIT Press.
• Hicks, M., 2018. Programmed Inequality: How Britain Discarded
Women Technologists and Lost Its Edge in Computing. MIT Press.
3
(30.
Jan)
Lecture: What are
data?
A critical look at the
key characteristics of
data
Seminars:
Data Story 2 –
Remote Sensing
for Biodiversity
Research
Core reading:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 3
• Borgman, C.L., 2019. The lives and after lives of data. Harvard Data
Science Review, 1(1). https://doi.org/10.1162/99608f92.9a36bdb6
Recommended readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 4
• Leonelli, S., 2015. What counts as scientific data? A relational
framework. Philosophy of Science, 82(5), pp.810-821.
• Kitchin, R., 2014. The data revolution: Big data, open data, data
infrastructures and their consequences. Thousand Oaks: Sage.
• Schutt, R. and O'Neil, C., 2013. Doing data science: Straight talk
from the frontline. Sebastopol, CA: O'Reilly. Introduction and
Chapter 1.
• Gabrys, J., Pritchard, H. and Barratt, B., 2016. Just good enough
data: Figuring data citizenships through air pollution sensing and
data stories. Big Data & Society, 3(2), pp.1-14.
https://doi.org/10.1177/2053951716679677
4
(6.
Feb)
Lecture: Putting
Data to Work:
New Data Skills
4.A New Data Skills:
Who are data
scientists?
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapters 5 and 6.
• Meng, X.L., 2019. Data science: An artificial ecosystem. Harvard
Data Science Review, 1(1).
https://doi.org/10.1162/99608f92.ba20f892
8
4.B Key data
challenges: quality,
security and
processing
Seminars:
Data Story 4 –
Tracking
Tuberculosis Using
Phone Data
Recommended readings:
• Edwards, P.N., Mayernik, M.S., Batcheller, A.L., Bowker, G.C. and
Borgman, C.L., 2011. Science friction: Data, metadata, and
collaboration. Social studies of science, 41(5), pp.667-690.
http://dx.doi.org/10.1177/0306312711413314
• Starosielski, N., 2015. The Undersea Network. Duke University
Press. Introduction.
• Leonelli, S., 2020. Learning from Data Journeys. In Data Journeys in
the Sciences. Cham: Springer. (Open Access).
https://library.oapen.org/handle/20.500.12657/39987
• Mackenzie, A., 2017. Machine Learners: Archaeology of a Data
Practice. Cambridge, MA: MIT Press.
• Cai, L. and Zhu, Y., 2015. The Challenges of Data Quality and Data
Quality Assessment in the Big Data Era. Data Science Journal, 14,
p.2. DOI: http://doi.org/10.5334/dsj-2015-002
• Borgman, C.L., 2015. Big Data, Little Data, No Data. Cambridge,
MA: MIT Press.
• Floridi, L. and Illari, P. eds., 2014. The philosophy of information
quality (Vol. 358). Cham, CH: Springer.
https://doi.org/10.1007/978-3-319-07121-3
• Stevens, H., 2013. Life out of sequence: A data-driven history of
bioinformatics. Chicago: University of Chicago Press. Introduction
and Chapter 1.
• Helmreich, S., 2000. Silicon second nature: Culturing artificial life in
a digital world. Berkeley: University of California Press.
• Leonelli, S., 2016. Data-centric Biology: A Philosophical Study.
Chicago University Press. Especially introduction and Chapter 1.
• Leonelli, S., 2019. Data Governance is Key to Interpretation:
Reconceptualizing Data in Data Science. Harvard Data Science
Review, 1(1). https://doi.org/10.1162/99608f92.17405bb6
• Strasser, B.J., 2019. Collecting Experiments: The Making of Big Data
Biology. Chicago University Press. Introduction, Chapter 6 and
Conclusion.
5
(13.
Feb)
Lecture: Data
Governance
5.A The role of data
infrastructures in the
data ecosystem
5.B Open Science
and Open Data: why
circulation matters
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 7
• Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G.,
Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B.,
Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for
scientific data management and stewardship. Scientific data, 3(1),
pp.1-9. https://doi.org/10.1038/sdata.2016.18
Recommended readings:
9
Seminars: Data
Story 3 – it’s a GIS
World
• Science International, 2015. Open Data in a Big Data World.
http://www.science-
international.org/sites/default/files/reports/open-data-in-big-data-
world_long_en.pdf
• Blum, A., 2012. Tubes: behind the scenes at the internet. Penguin
UK.
• Boulton, G., Campbell, P., Collins, B., Elias, P., Hall, W., Laurie, G.,
O’Neill, O., Rawlins, M., Thornton, J., Vallance, P. and Walport, M.,
2012. Science as an open enterprise. The Royal Society Science
Policy Centre report 02/12.
https://royalsociety.org/~/media/royal_society_content/policy/pro
jects/sape/2012-06-20-saoe.pdf
• Mauthner, N.S. and Parry, O., 2013. Open access digital data
sharing: Principles, policies and practices. Social
Epistemology, 27(1), pp.47-67.
https://doi.org/10.1080/02691728.2012.760663
• Bezuidenhout, L.M., Leonelli, S., Kelly, A.H. and Rappert, B., 2017.
Beyond the digital divide: Towards a situated approach to open
data. Science and Public Policy, 44(4), pp.464-475.
https://doi.org/10.1093/scipol/scw036
• Cath, Corinne, 2021. The technology we choose to create: Human
rights advocacy in the Internet Engineering Task Force.
Telecommunications Policy, 45(6).
https://doi.org/10.1016/j.telpol.2021.102144
6
(20.
Feb)
Lecture: Data
Matters: Politics
of Data
6.A Evidence- based
decision- making
6.B The “surveillance
society”: Identifying
and handling
political and social
concerns in data
science
Seminars:
Recap of first few
weeks
Discussion of
presentation
topics
Data Story 5 –
Uber Drivers
Core readings:
• Zuboff, S., 2015. Big other: surveillance capitalism and the
prospects of an information civilization. Journal of Information
Technology, 30(1), pp.75-89. https://doi.org/10.1057/jit.2015.5
• Lane, J. 2020. Democratising our Data: A Manifesto. MIT Press.
Chapter 1.
Recommended readings:
• Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for
the Future at the New Frontier of Power. Profile Books.
• Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V. and Floridi,
L., 2021. The Chinese approach to artificial intelligence: an analysis
of policy, ethics, and regulation. AI & SOCIETY, 36(1), pp.59-77.
https://doi.org/10.1007/s00146-020-00992-2
• O’Neill, C., 2016. Weapons of Math Destruction: How Big Data
Increases Inequality and Threatens Democracy. Allen Lane.
• Van Dijck, J., 2014. Datafication, dataism and dataveillance: Big
Data between scientific paradigm and ideology. Surveillance &
society, 12(2), pp.197-208. https://doi.org/10.24908/ss.v12i2.4776
• D'Ignazio, C. and Klein, L.F., 2020. Data feminism. MIT press.
10
• Beer, D., 2016. Metric power. London: Palgrave Macmillan.
• Rouvroy A. and Berns T., 2013. Gouvernementalité algorithmique et
perspectives d'émancipation. Le disparate comme condition
d'individuation par la relation? Réseaux, 31(177), pp.163-196. DOI :
10.3917/res.177.0163. https://www.cairn.info/revue-reseaux-
2013-1-page- 163.htm [in English translation. Foundational text for
algorithmic governmentality]
7
(27.
Feb)
Lecture: Privacy
and responsibility
8.A The concept of
privacy
8.B Responsible
work with data
Seminars:
Data Story 6 –
Self- Tracking via
Dating Apps
Core readings:
• Véliz, Carissa, 2020. Data, Privacy & The Individual. Madrid: Center
for the Governance of Change, IE University.
https://philpapers.org/archive/VLIPM.pdf
• Zook, M., Barocas, S., Crawford, K., Keller, E., Gangadharan, S.P.,
Goodman, A., Hollander, R., Koenig, B.A., Metcalf, J., Narayanan, A.
and Nelson, A., 2017. Ten simple rules for responsible big data
research. PLoS Computational Biology, 13(3), pp.e1005399-
e1005399. https://doi.org/10.1371/journal.pcbi.1005399
Recommended readings:
• Lane, J., Stodden, V., Bender, S. and Nissenbaum, H. eds., 2014.
Privacy, big data, and the public good: Frameworks for
engagement. Cambridge: Cambridge University Press
• Enserink, M. and Chin, G., 2015. The end of privacy.
Introduction. Science (New York, NY), 347(6221), pp.490-491.
• Marmor, A., 2015. What is the right to privacy? Philosophy & Public
Affairs, 43(1), pp.3-26.
• Véliz, Carissa, 2020. Privacy is Power: Why and how you should take
back control of your data. Bantam Press.
https://www.penguin.co.uk/books/442343/privacy-is-power-by-
carissa-veliz/9780552177719
8
(6.
Mar)
Lecture: Acting
Responsibly:
Ethical
frameworks
Overview of four
ethical theories and
how they apply to
data work.
Seminar: Student
presentations
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 9
• Taddeo, M. and Floridi, L., 2018. How AI can be a force for
good. Science, 361(6404), pp.751-752.
https://doi.org/10.1126/science.aat5991
Recommended readings:
• Leslie, D., 2019. Understanding artificial intelligence ethics and
safety: A guide for the responsible design and implementation of AI
systems in the public sector. The Alan Turing Institute.
https://dx.doi.org/10.2139/ssrn.3403301
• Noble, S.U., 2018. Algorithms of Oppression: How Search Engines
Reinforce Racism, New York: NYU Press.
11
• Pasquale, F., 2015. The Black Box Society. Harvard University Press.
Chapter 2: “Digital Reputation in an Era of Runaway Data” (pp.19-
58)
• Lupton, D. and Michael, M., 2017. “For Me, the Biggest Benefit Is
Being Ahead of the Game”: The Use of Social Media in Health
Work. Social Media+ Society, 3(2), pp.1-10..
https://doi.org/10.1177%2F2056305117702541
• Leonelli, S., 2016. Locating ethics in data science: responsibility and
accountability in global and distributed knowledge production
systems. Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 374(2083),
p.20160122. http://dx.doi.org/10.1098/rsta.2016.0122
• Mittlestadt, B.D. and Floridi, L. eds., 2016. The Ethics of Biomedical
Big Data. Springer.
• Richards, M., Anderson, R., Hinde, S., Kaye, J., Lucassen, A.,
Matthews, P., Parker, M., Shotter, M., Watts, G., Wallace, S. and
Wise, J., 2015. The collection, linking and use of data in biomedical
research and health care: ethical issues. Nuffield Council on
Bioethics, London.
https://www.repository.cam.ac.uk/bitstream/handle/1810/284384
/Biological_and_health_data_web.pdf?sequence=1&isAllowed=y
9
(13.
Mar)
Lecture: Data
Justice and
Fairness
Inequalities and
exclusions,
Intersections with
human rights. Data
justice and relational
ethics
Seminar: Student
presentations
Core readings:
• Dencik, L., Hintz, A., Redden, J. and Trere, E., 2019. Exploring data
justice: conceptions, applications and directions. Information,
Communication and Society, 22(7), pp.873-881.
https://doi.org/10.1080/1369118X.2019.1606268
• Binns, R., 2018. Fairness in machine learning: Lessons from political
philosophy. Proceedings of Machine Learning Research, 81, pp.149-
159. http://proceedings.mlr.press/v81/binns18a/binns18a.pdf
• Birhane, Abeba, 2021. Algorithmic injustice: a relational ethics
approach. Patterns 2(2).
https://doi.org/10.1016/j.patter.2021.100205
Recommended readings:
• Kleinberg, J., Ludwig, J., Mullainathan, S. and Sunstein, C.R., 2018.
Discrimination in the Age of Algorithms. Journal of Legal
Analysis, 10, pp.113-174. https://doi.org/10.1093/jla/laz001
• Wachter, S., Mittelstadt, B. and Russell, C., 2021. Why fairness
cannot be automated: Bridging the gap between EU non-
discrimination law and AI. Computer Law & Security Review, 41,
p.105567. https://doi.org/10.1016/j.clsr.2021.105567
• Sunstein, C.R., 2019. Algorithms, correcting biases. Social Research:
An International Quarterly, 86(2), pp.499-511.
• GDPR website (and the regulation itself): https://eugdpr.org/
12
• Prainsack, B., 2019. Logged out: Ownership, exclusion and public
value in the digital data and information commons. Big Data &
Society, 6(1), pp.1-15. https://doi.org/10.1177/2053951719829773
• Eubanks, V., 2018. Automating inequality: How high-tech tools
profile, police, and punish the poor. St. Martin's Press.
• O’Neill, C., 2016. Weapons of Math Destruction: How Big Data
Increases Inequality and Threatens Democracy. Allen Lane.
• Aradau, C. and Blanke, T., 2017. Politics of prediction: Security and
the time/space of governmentality in the age of big data. European
Journal of Social Theory, 20(3), pp.373-391.
• Latonero, M., 2018. “Big Data Analytics and Human Rights,” in Land,
M. K. and Aronson, J. D. (eds) New Technologies for Human Rights
Law and Practice. Cambridge: Cambridge University Press, pp. 149–
161. doi: 10.1017/9781316838952.007
https://www.cambridge.org/core/books/new-technologies-for-
human-rights-law-and-practice/big-data-analytics-and-human-
rights/AEFCC6D0B090A725742698E968E39431#
• Van Dijck, J., Poell, T. and De Waal, M., 2018. The Platform Society:
Public Values in a Connective World. Oxford University Press.
• V Vayena, E. and Tasioulas, J., 2016. The dynamics of big data and
human rights: the case of scientific research. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences, 374(2083), p.20160129.
https://doi.org/10.1098/rsta.2016.0129
10
(20.
Mar)
Lecture: Ethical
principles and
moral character
Principled
approaches to digital
ethics. Digital
technologies and
virtue ethics
Seminar:
Discussion of
essay topics
Core readings:
• Mittelstadt, B. ‘Principles alone cannot guarantee ethical AI.’ Nat
Mach Intell 1, 501–507 (2019). https://doi.org/10.1038/s42256-
019-0114-4
• Vallor, Shannon, 2016. Technology and the Virtues: A Philosophical
Guide to a Future Worth Wanting. Oxford University Press. Chapter
8. https://doi.org/10.1093/acprof:oso/9780190498511.003.0009
Recommended readings:
• Tasioulas, John, 2022. Artificial Intelligence, Humanistic Ethics.
Daedalus 151(2), pp.232-243.
https://doi.org/10.1162/daed_a_01912
• Vallor, Shannon, 2016. Technology and the Virtues: A Philosophical
Guide to a Future Worth Wanting. Oxford University Press
• Narayanan, A. and Vallor, S., 2014. Computing ethics: Why software
engineering courses should include ethics coverage.
Communications of the ACM 57(3), pp.23-25.
https://dl.acm.org/doi/fullHtml/10.1145/2566966
• Bilal. A. et al., 2021. Trust Development in Artificial Intelligence-
based Emerging Technologies: Rise of Technomoral Virtues and
Data Ethics. ACIS 2021 Proceedings, 52.
13
https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1051&context=
acis2021
11
(27.
Mar)
Lecture: Data and
the Knowledge
We Need
Final reflections on
using data science
for the good of the
planet
Seminar:
Data Story 6 –
Coronavirus
Tracking;
Module feedback
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 10
• Pasquale, F., 2015. The Black Box Society. Harvard University Press.
Chapter 6: “Toward an Intelligible Society”, pp.189-218.
• Leonelli, S. (2021). Data Science in Times of Pan(dem)ic. Harvard
Data Science Review. https://doi.org/10.1162/99608f92.fbb1bdd6
Recommended readings:
• Ford, M., 2018. Architects of Intelligence: The truth about AI from
the people building it. Packt Publishing Ltd.
• Global Alliance for Genomics and Health (GA4GH, 2016).
Framework for responsible sharing of genomic and health-related
data. See https://www.ga4gh.org/wp-content/uploads/Framework-
Version-3September20191.pdf
• O’Brien D., Ullman J., Altman M., Gasser U., Bar-Sinai M., Nissim K.,
Vadhan S. and Wojcik M. J., 2017. OECD Recommendations on
Health Data Governance http://www.oecd.org/els/health-
systems/health-data-governance.htm
• Sachs, J.D., Schmidt-Traub, G., Mazzucato, M., Messner, D.,
Nakicenovic, N. and Rockström, J., 2019. Six transformations to
achieve the sustainable development goals. Nature
Sustainability, 2(9), pp.805-814. https://doi.org/10.1038/s41893-
019-0352-9
• Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch,
S., Felländer, A., Langhans, S.D., Tegmark, M. and Nerini, F.F., 2020.
The role of artificial intelligence in achieving the Sustainable
Development Goals. Nature communications, 11(1), pp.1-10.
https://doi.org/10.1038/s41467-019-14108-y
• Sterner, T., Barbier, E.B., Bateman, I., van den Bijgaart, I., Crépin,
A.S., Edenhofer, O., Fischer, C., Habla, W., Hassler, J., Johansson-
Stenman, O. […] and Lange, A., 2019. Policy design for the
Anthropocene. Nature Sustainability, 2(1), pp.14-21.
https://doi.org/10.1038/s41893-018-0194-x
• Fleming, L.E., Tempini, N., Gordon-Brown, H., Nichols, G., Sarran, C.,
Vineis, P., Leonardi, G., Golding, B., Haines, A., Kessel, A., Murray,
V., Depledge, M. and Leonelli, S., 2017. Big Data in Environment and
Human Health: Challenges and Opportunities. Oxford Encyclopaedia
for Environment and Human Health. Oxford University Press.
• Green, S. and Vogt, H., 2016. Personalizing Medicine: Disease
Prevention in silico and in socio. HUMANA. MENTE Journal of
Philosophical Studies, 9(30), pp.105-145.
SOCM033/PHLM011 (15 credits)
SOCM038/PHLM012 (30 credits)
University of Exeter
TERM 2, Academic year 2022-23
Credit value: 15 [assessed via a 3000-word essay] or 30 [assessed via a 6000-word essay]
Module Convenor (Term 2):
Stephan Guttinger
Email: s.m.guttinger@exeter.ac.uk
Office: F11, Byrne House, Streatham Campus
Office hours:
• Tuesdays 3pm-4pm, F11, Byrne House (please use this link to pre-book a slot:
https://calendly.com/guttinger/office-hour?month=2023-01
• Thursdays 10am-11am, online Zoom drop-in (no pre-booking required).
https://Universityofexeter.zoom.us/j/99757755039?pwd=ZGdLdnQwVTRpdF
pjeGozcFpmdEpmQT09
Teaching assistant:
Oliver Roberts-Garratt
email: or245@exeter.ac.uk
General Description of the Course
This module addresses the growing attention to the social embedding of data across
different settings, from business to policy and government, from sports to health and
climate change, and the challenges that such embedding brings both for the governance
and regulation of data flows and for the technical management and responsible use of data.
Data science, machine learning, artificial intelligence and “big data” have become central to
every aspect of social life. How can these complex and powerful technologies best be
managed and governed for the benefit of society now and in the future? In this module you
will: (1) identify some of the main risks and ethical/legal challenges involved in the
widespread automation and digitalisation of services characterising 21st century life (for
example, the clash between individual desire for privacy, frameworks for data ownership
and the institutional commodification of personal data); (2) examine whether and how such
concerns can be handled; and (3) discuss the responsibilities of data scientists and other
producers of technologies for data analysis towards their responsible use.
Aims of the module
This module aims to equip you with the knowledge and skills to reason around the complex
issues of data governance and ethics. The module introduces the key ethical questions
around the use of big data and associated technologies such as machine learning and
artificial intelligence, and places them in the broader framework of contemporary digital
2
society, including its reliance on automation, social media and related platforms for
communication and service provision. We will review concerns around data protection,
surveillance, open data, citizen science and use (and misuse) of social data. We will explore
the legal and social contexts for decision-making through relevant scholarship and a number
of real-world case studies, which you will be invited to identify and discuss with your peers.
We will examine case studies from end to end, beginning with real-world examples of data
collection, storage, and analysis, following the possible (intended and unintended) ways in
which data is subsequently used to support decision-making, and considering the ethical
and legal issues that arise at each stage.
Intended learning outcomes
Module Specific Skills and Knowledge:
1. Evaluate the choices made at each stage of a data handling and the associated legal,
ethical and governance issues.
2. Identify key social concerns in relation to digital tools within contemporary society.
3. Understand the core regulatory and legislative frameworks that govern collection,
storage, processing and communication of data.
4. Appreciate the differing costs and benefits associated with use of data when
considered from perspectives of data user, data provider, decision-maker and
regulator.
Discipline Specific Skills and Knowledge:
5. Appreciate the wider social context of data science and related technologies,
including current issues such as open data, data protection, automated data analysis,
and misuse of data and related analytics.
6. Critically reflect on the ethical considerations associated with use of data within
organisations and governments.
7. Display understanding of key contributions to scholarship on data studies and the
digital society.
Personal and Key Transferable/ Employment Skills and Knowledge:
8. Effectively communicate complex ideas using written and verbal methods
appropriate to the intended audience.
9. Demonstrate cognitive skills of critical and reflective thinking.
10. Demonstrate effective independent study and research skills.
Structure of the module
This module will be taught via weekly one-hour lectures, recordings of which will posted on
the ELE page of the module. There will also be weekly one-hour seminars, in which the
lecturers and PTAs will meet you to discuss the readings and lecture content and do group-
work together. There will be several seminar groups, so that we can keep their size small
enough for discussion, and you will be assigned to one group by the timetabling services.
Seminars and lectures will be conducted in person.
3
Key deadlines
• Before each seminar: read required readings
• Week 6: Choose topic for presentation
• Weeks 8 and 9: Group presentations during seminars
• Week 10: Choose essay topic
• 2pm Tuesday 2 May 2023: Submit essay (100% of final mark)
[for details on these assignments, see page 4]
Preparation for Seminars
Attendance to the seminars is essential to pass the course, also because your lecturers will
discuss at length requirements for the essay as we go along. Before you join each seminar,
you need to prepare as follows:
1. Attend lecture
2. Read the core readings for the week, as indicated on the syllabus and available
digitally on the ELE page
3. Read and reflect on ‘Data Story’ that will be discussed in the seminars
Reading Materials
In addition to the core readings, there are recommended readings indicated for each lecture
in the syllabus below, which you are encouraged to study before we meet in class and will
help you to decide whether to focus your presentation and essay on that topic. To prepare
for your essay, you are also encouraged to research the topics that most interest you and
identify relevant scholarly materials on those. This is a fast-changing and expanding field,
and new relevant publications are sure to appear during the duration of the module. By the
end of the module, you will hopefully know more about your chosen topic than the
lecturers!
During some of the seminars, we will also be considering real-world “data stories” that
highlight some of the issues discussed in the lectures. You will be asked to read those stories
and discuss them in groups. This will give you the opportunity to discuss how the concepts
you are learning play out in real life, as well as giving you a model for the kind of analysis
required for the written essay.
4
Assessment:
Formative assessment (no marks):
Presentation given during seminars in groups of up to 5 students
• Presentation assessment: Your presentation will be discussed during seminars, and
you will receive oral feedback from the lecturer/PTA and your peers. There are no
marks for this assessment.
• Presentation topic: Identify a case of data work and analyse its social, political, and
ethical context and implications. You will need to refer to at least three of the
compulsory readings for this module in your analysis of the case you chose.
• Presentation format: Presentations must be 10 minutes long (but not longer). You
are welcome to use PowerPoint slides. Each member of the group needs to actively
participate in the making of the presentation and take a turn in presenting.
• Presentation schedule: How to prepare and the presentation schedule will be
discussed during week 1 of the module. You will have to choose a presentation topic
by week 6. Presentations will take place during seminars in weeks 8 and 9.
Summative assessment (determines final mark):
Essay to be submitted through the e-BART system by 2pm on Tuesday 2 May 2023.
• Topic: Consider one or more institutional, ethical and/or social concern involved in
using data within a specific project or case study. This can be a case that you are
familiar with from your own experience; or it can be a case you have read about
(e.g., in the news or in scholarly work) and whose details you are interested in
exploring in more detail. The essay needs to explore the implications of the issue(s)
at hand, the challenges they pose and the way to overcome those challenges. You
need to be as specific as possible about the details of the case you chose: vague
discussions of data ethics “in general” won’t be acceptable. You are welcome to
write your essay on the same topic on which you did a presentation, but this is not
compulsory.
• Sources and references: You are asked to reference at least THREE of the readings
listed in this syllabus; and complement those sources with additional ones of
specific relevance to your chosen topic.
• Schedule: We will discuss topics in seminar groups during week 10, so you will need
to choose a topic for your essay by the end of week 9 at the absolute latest.
• Assessment: The essay constitutes 100% of the module assessment.
• Length:
o 3000 words (excluding bibliography) for students taking the 15 credits
version of the module SOCM033/PHLM011
o 6000 words (excluding bibliography) for students taking the 30 credits
version of the module SOCM038/PHLM012
• Marking criteria: In week 2 of the term, you will find a video on ELE (under
“Assessment”) containing a review of the criteria against which your essay will be
marked, and a discussion of what it takes to write a first-rate essay. A summary of
marking criteria is also provided below and on the ELE page for the module. The
lecturers will discuss these criteria with you during seminars.
5
Criteria for Essay Marking (summative)
Focus
How well-formulated is your main claim/question?
Argument
How clear and convincing are the arguments you offer? Is there evidence of original
research, independent thinking?
Background
How well did you prepare (is there evidence of relevant background reading, including both
scholarship on data ethics ad governance from the syllabus and sources documenting your
specific topic)?
How well do you understand the issues?
Structure and Style
Is your essay structured in a logical, efficient manner? Is your writing clear and well-
structured?
Note:
• We are not looking for evidence of wide-ranging background reading. Rather, we
want to see evidence for your claims and a good understanding of the sources you
cite. Please make sure that you use at least THREE different scholarly references
from the syllabus in a meaningful way. You have to work with and discuss these
references, not just drop the names in your essay.
• Originality does not mean breaking completely new ground. Rather, it involves
providing a personal, well-reasoned take on an issue that matters to you. You should
come up with your own views on the issue you are discussing. Merely summarizing
what others have said won't be enough.
• While we do not expect you to use a wide range of citations, we do ask for rigorous
citation of the sources that you do use. In text, ALWAYS refer to author, year of
publication and page number if referring to a specific argument. E.g. (Kelly 2009,
27) Otherwise your writing may count as plagiarism. This goes for direct quotes as
well as for content of arguments or empirical evidence.
• ALWAYS add ALL and ONLY the sources you have used. Make sure to have Author,
Year, Title, Journal/Publishing house, Volume, Page Numbers or URL for online
sources.
• The HARVARD REFERENCE STYLE is the compulsory at the university. See the style
instruction sheet on the ELE page for this module.
Note about submission: Please be sure to submit your work at least three hours before the
deadline to avoid any difficulties. If you wish you may use the Turnitin link on the module
ELE page to check your work for potential referencing or plagiarism problems before
submitting it to E-BART. Note that all assignments will be checked through Turnitin after
submission. If you have doubts about plagiarism, make sure that you take the SPA Academic
Honesty ELE quiz available here: https://vle.exeter.ac.uk/course/view.php?id=1977
6
Course overview
Week Topic Readings
1
(16.
Jan)
Lecture:
Introduction: Why
Care About Data?
1.A Introduction to
the module themes,
structure and aims
1.B The
phenomenon of
Datafication
Seminars:
Discussion of
module
aims/assessment
and concept of
‘datafication’
Core reading:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 1
Recommended readings:
• Floridi L., 2014. The fourth revolution: how the infosphere is
reshaping human reality. OUP, Oxford, UK. Introduction.
• Hey, T., Tansley, S. and Tolle, K., ed., 2009. The fourth paradigm:
Data- intensive scientific discovery. Redmond, WA: Microsoft
Research. Available Open Access here:
http://research.microsoft.com/en-
us/collaboration/fourthparadigm/4th_paradigm_book_complete_lr
• Van Dijck, J., Poell, T. and De Waal, M., 2018. The Platform Society:
Public Values in a Connective World. Oxford University Press.
• Ebeling, M.F., 2016. Healthcare and Big Data: Digital Specters and
Phantom Objects. New York: Palgrave Macmillan.
2
(23.
Jan)
Lecture: Big Data
in context
A discussion of the
mythology of Big
Data
Seminars:
Data Story 1 – Big
Data on Consumer
Habits
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 2
• Anderson, C., 2008. The end of theory: The data deluge makes the
scientific method obsolete. Wired Magazine.
http://archive.wired.com/science/discoveries/magazine/16-
07/pb_theory
Recommended readings:
• Kitchin, R. and McArdle, G., 2016. What makes Big Data, Big Data?
Exploring the ontological characteristics of 26 datasets. Big Data &
Society, 3(1), pp.1-10.
https://doi.org/10.1177%2F2053951716631130
• Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution
that will transform how we live, work, and think. New York: Eamon
Dolan/Houghton Mifflin Harcourt.
• boyd, d., and Crawford, K., 2012. Critical questions for big data:
Provocations for a cultural, technological, and scholarly
phenomenon. Information, communication & society, 15(5),
pp.662-679. http://dx.doi.org/10.1080/1369118X.2012.678878
• Leonelli, S., 2014. What difference does quantity make? On the
epistemology of Big Data in biology. Big data & society, 1(1), pp.1-
11. https://doi.org/10.1177%2F2053951714534395
7
• Leonelli, S., 2020. Scientific Research and Big Data. Stanford
Encyclopaedia for Philosophy.
https://plato.stanford.edu/entries/science-big-data/
• Daston, L., 2018. Sciences of the Archive. Chicago University Press.
Introduction and Epilogue.
• Porter, T., 1995. Trust in Numbers. Princeton University Press.
Introduction, Chapters 1-4.
• Edwards, P.N., 2010. A vast machine: Computer models, climate
data, and the politics of global warming. Cambridge, MA: MIT
Press.
• Kelty, C.M., 2008. Two bits: The cultural significance of free
software. Durham, NC: Duke University Press.
• Ensmenger, N.L., 2010. The computer boys take over: Computers,
programmers, and the politics of technical expertise. Cambridge,
MA: MIT Press.
• Hicks, M., 2018. Programmed Inequality: How Britain Discarded
Women Technologists and Lost Its Edge in Computing. MIT Press.
3
(30.
Jan)
Lecture: What are
data?
A critical look at the
key characteristics of
data
Seminars:
Data Story 2 –
Remote Sensing
for Biodiversity
Research
Core reading:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 3
• Borgman, C.L., 2019. The lives and after lives of data. Harvard Data
Science Review, 1(1). https://doi.org/10.1162/99608f92.9a36bdb6
Recommended readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 4
• Leonelli, S., 2015. What counts as scientific data? A relational
framework. Philosophy of Science, 82(5), pp.810-821.
• Kitchin, R., 2014. The data revolution: Big data, open data, data
infrastructures and their consequences. Thousand Oaks: Sage.
• Schutt, R. and O'Neil, C., 2013. Doing data science: Straight talk
from the frontline. Sebastopol, CA: O'Reilly. Introduction and
Chapter 1.
• Gabrys, J., Pritchard, H. and Barratt, B., 2016. Just good enough
data: Figuring data citizenships through air pollution sensing and
data stories. Big Data & Society, 3(2), pp.1-14.
https://doi.org/10.1177/2053951716679677
4
(6.
Feb)
Lecture: Putting
Data to Work:
New Data Skills
4.A New Data Skills:
Who are data
scientists?
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapters 5 and 6.
• Meng, X.L., 2019. Data science: An artificial ecosystem. Harvard
Data Science Review, 1(1).
https://doi.org/10.1162/99608f92.ba20f892
8
4.B Key data
challenges: quality,
security and
processing
Seminars:
Data Story 4 –
Tracking
Tuberculosis Using
Phone Data
Recommended readings:
• Edwards, P.N., Mayernik, M.S., Batcheller, A.L., Bowker, G.C. and
Borgman, C.L., 2011. Science friction: Data, metadata, and
collaboration. Social studies of science, 41(5), pp.667-690.
http://dx.doi.org/10.1177/0306312711413314
• Starosielski, N., 2015. The Undersea Network. Duke University
Press. Introduction.
• Leonelli, S., 2020. Learning from Data Journeys. In Data Journeys in
the Sciences. Cham: Springer. (Open Access).
https://library.oapen.org/handle/20.500.12657/39987
• Mackenzie, A., 2017. Machine Learners: Archaeology of a Data
Practice. Cambridge, MA: MIT Press.
• Cai, L. and Zhu, Y., 2015. The Challenges of Data Quality and Data
Quality Assessment in the Big Data Era. Data Science Journal, 14,
p.2. DOI: http://doi.org/10.5334/dsj-2015-002
• Borgman, C.L., 2015. Big Data, Little Data, No Data. Cambridge,
MA: MIT Press.
• Floridi, L. and Illari, P. eds., 2014. The philosophy of information
quality (Vol. 358). Cham, CH: Springer.
https://doi.org/10.1007/978-3-319-07121-3
• Stevens, H., 2013. Life out of sequence: A data-driven history of
bioinformatics. Chicago: University of Chicago Press. Introduction
and Chapter 1.
• Helmreich, S., 2000. Silicon second nature: Culturing artificial life in
a digital world. Berkeley: University of California Press.
• Leonelli, S., 2016. Data-centric Biology: A Philosophical Study.
Chicago University Press. Especially introduction and Chapter 1.
• Leonelli, S., 2019. Data Governance is Key to Interpretation:
Reconceptualizing Data in Data Science. Harvard Data Science
Review, 1(1). https://doi.org/10.1162/99608f92.17405bb6
• Strasser, B.J., 2019. Collecting Experiments: The Making of Big Data
Biology. Chicago University Press. Introduction, Chapter 6 and
Conclusion.
5
(13.
Feb)
Lecture: Data
Governance
5.A The role of data
infrastructures in the
data ecosystem
5.B Open Science
and Open Data: why
circulation matters
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 7
• Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G.,
Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B.,
Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for
scientific data management and stewardship. Scientific data, 3(1),
pp.1-9. https://doi.org/10.1038/sdata.2016.18
Recommended readings:
9
Seminars: Data
Story 3 – it’s a GIS
World
• Science International, 2015. Open Data in a Big Data World.
http://www.science-
international.org/sites/default/files/reports/open-data-in-big-data-
world_long_en.pdf
• Blum, A., 2012. Tubes: behind the scenes at the internet. Penguin
UK.
• Boulton, G., Campbell, P., Collins, B., Elias, P., Hall, W., Laurie, G.,
O’Neill, O., Rawlins, M., Thornton, J., Vallance, P. and Walport, M.,
2012. Science as an open enterprise. The Royal Society Science
Policy Centre report 02/12.
https://royalsociety.org/~/media/royal_society_content/policy/pro
jects/sape/2012-06-20-saoe.pdf
• Mauthner, N.S. and Parry, O., 2013. Open access digital data
sharing: Principles, policies and practices. Social
Epistemology, 27(1), pp.47-67.
https://doi.org/10.1080/02691728.2012.760663
• Bezuidenhout, L.M., Leonelli, S., Kelly, A.H. and Rappert, B., 2017.
Beyond the digital divide: Towards a situated approach to open
data. Science and Public Policy, 44(4), pp.464-475.
https://doi.org/10.1093/scipol/scw036
• Cath, Corinne, 2021. The technology we choose to create: Human
rights advocacy in the Internet Engineering Task Force.
Telecommunications Policy, 45(6).
https://doi.org/10.1016/j.telpol.2021.102144
6
(20.
Feb)
Lecture: Data
Matters: Politics
of Data
6.A Evidence- based
decision- making
6.B The “surveillance
society”: Identifying
and handling
political and social
concerns in data
science
Seminars:
Recap of first few
weeks
Discussion of
presentation
topics
Data Story 5 –
Uber Drivers
Core readings:
• Zuboff, S., 2015. Big other: surveillance capitalism and the
prospects of an information civilization. Journal of Information
Technology, 30(1), pp.75-89. https://doi.org/10.1057/jit.2015.5
• Lane, J. 2020. Democratising our Data: A Manifesto. MIT Press.
Chapter 1.
Recommended readings:
• Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for
the Future at the New Frontier of Power. Profile Books.
• Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V. and Floridi,
L., 2021. The Chinese approach to artificial intelligence: an analysis
of policy, ethics, and regulation. AI & SOCIETY, 36(1), pp.59-77.
https://doi.org/10.1007/s00146-020-00992-2
• O’Neill, C., 2016. Weapons of Math Destruction: How Big Data
Increases Inequality and Threatens Democracy. Allen Lane.
• Van Dijck, J., 2014. Datafication, dataism and dataveillance: Big
Data between scientific paradigm and ideology. Surveillance &
society, 12(2), pp.197-208. https://doi.org/10.24908/ss.v12i2.4776
• D'Ignazio, C. and Klein, L.F., 2020. Data feminism. MIT press.
10
• Beer, D., 2016. Metric power. London: Palgrave Macmillan.
• Rouvroy A. and Berns T., 2013. Gouvernementalité algorithmique et
perspectives d'émancipation. Le disparate comme condition
d'individuation par la relation? Réseaux, 31(177), pp.163-196. DOI :
10.3917/res.177.0163. https://www.cairn.info/revue-reseaux-
2013-1-page- 163.htm [in English translation. Foundational text for
algorithmic governmentality]
7
(27.
Feb)
Lecture: Privacy
and responsibility
8.A The concept of
privacy
8.B Responsible
work with data
Seminars:
Data Story 6 –
Self- Tracking via
Dating Apps
Core readings:
• Véliz, Carissa, 2020. Data, Privacy & The Individual. Madrid: Center
for the Governance of Change, IE University.
https://philpapers.org/archive/VLIPM.pdf
• Zook, M., Barocas, S., Crawford, K., Keller, E., Gangadharan, S.P.,
Goodman, A., Hollander, R., Koenig, B.A., Metcalf, J., Narayanan, A.
and Nelson, A., 2017. Ten simple rules for responsible big data
research. PLoS Computational Biology, 13(3), pp.e1005399-
e1005399. https://doi.org/10.1371/journal.pcbi.1005399
Recommended readings:
• Lane, J., Stodden, V., Bender, S. and Nissenbaum, H. eds., 2014.
Privacy, big data, and the public good: Frameworks for
engagement. Cambridge: Cambridge University Press
• Enserink, M. and Chin, G., 2015. The end of privacy.
Introduction. Science (New York, NY), 347(6221), pp.490-491.
• Marmor, A., 2015. What is the right to privacy? Philosophy & Public
Affairs, 43(1), pp.3-26.
• Véliz, Carissa, 2020. Privacy is Power: Why and how you should take
back control of your data. Bantam Press.
https://www.penguin.co.uk/books/442343/privacy-is-power-by-
carissa-veliz/9780552177719
8
(6.
Mar)
Lecture: Acting
Responsibly:
Ethical
frameworks
Overview of four
ethical theories and
how they apply to
data work.
Seminar: Student
presentations
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 9
• Taddeo, M. and Floridi, L., 2018. How AI can be a force for
good. Science, 361(6404), pp.751-752.
https://doi.org/10.1126/science.aat5991
Recommended readings:
• Leslie, D., 2019. Understanding artificial intelligence ethics and
safety: A guide for the responsible design and implementation of AI
systems in the public sector. The Alan Turing Institute.
https://dx.doi.org/10.2139/ssrn.3403301
• Noble, S.U., 2018. Algorithms of Oppression: How Search Engines
Reinforce Racism, New York: NYU Press.
11
• Pasquale, F., 2015. The Black Box Society. Harvard University Press.
Chapter 2: “Digital Reputation in an Era of Runaway Data” (pp.19-
58)
• Lupton, D. and Michael, M., 2017. “For Me, the Biggest Benefit Is
Being Ahead of the Game”: The Use of Social Media in Health
Work. Social Media+ Society, 3(2), pp.1-10..
https://doi.org/10.1177%2F2056305117702541
• Leonelli, S., 2016. Locating ethics in data science: responsibility and
accountability in global and distributed knowledge production
systems. Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 374(2083),
p.20160122. http://dx.doi.org/10.1098/rsta.2016.0122
• Mittlestadt, B.D. and Floridi, L. eds., 2016. The Ethics of Biomedical
Big Data. Springer.
• Richards, M., Anderson, R., Hinde, S., Kaye, J., Lucassen, A.,
Matthews, P., Parker, M., Shotter, M., Watts, G., Wallace, S. and
Wise, J., 2015. The collection, linking and use of data in biomedical
research and health care: ethical issues. Nuffield Council on
Bioethics, London.
https://www.repository.cam.ac.uk/bitstream/handle/1810/284384
/Biological_and_health_data_web.pdf?sequence=1&isAllowed=y
9
(13.
Mar)
Lecture: Data
Justice and
Fairness
Inequalities and
exclusions,
Intersections with
human rights. Data
justice and relational
ethics
Seminar: Student
presentations
Core readings:
• Dencik, L., Hintz, A., Redden, J. and Trere, E., 2019. Exploring data
justice: conceptions, applications and directions. Information,
Communication and Society, 22(7), pp.873-881.
https://doi.org/10.1080/1369118X.2019.1606268
• Binns, R., 2018. Fairness in machine learning: Lessons from political
philosophy. Proceedings of Machine Learning Research, 81, pp.149-
159. http://proceedings.mlr.press/v81/binns18a/binns18a.pdf
• Birhane, Abeba, 2021. Algorithmic injustice: a relational ethics
approach. Patterns 2(2).
https://doi.org/10.1016/j.patter.2021.100205
Recommended readings:
• Kleinberg, J., Ludwig, J., Mullainathan, S. and Sunstein, C.R., 2018.
Discrimination in the Age of Algorithms. Journal of Legal
Analysis, 10, pp.113-174. https://doi.org/10.1093/jla/laz001
• Wachter, S., Mittelstadt, B. and Russell, C., 2021. Why fairness
cannot be automated: Bridging the gap between EU non-
discrimination law and AI. Computer Law & Security Review, 41,
p.105567. https://doi.org/10.1016/j.clsr.2021.105567
• Sunstein, C.R., 2019. Algorithms, correcting biases. Social Research:
An International Quarterly, 86(2), pp.499-511.
• GDPR website (and the regulation itself): https://eugdpr.org/
12
• Prainsack, B., 2019. Logged out: Ownership, exclusion and public
value in the digital data and information commons. Big Data &
Society, 6(1), pp.1-15. https://doi.org/10.1177/2053951719829773
• Eubanks, V., 2018. Automating inequality: How high-tech tools
profile, police, and punish the poor. St. Martin's Press.
• O’Neill, C., 2016. Weapons of Math Destruction: How Big Data
Increases Inequality and Threatens Democracy. Allen Lane.
• Aradau, C. and Blanke, T., 2017. Politics of prediction: Security and
the time/space of governmentality in the age of big data. European
Journal of Social Theory, 20(3), pp.373-391.
• Latonero, M., 2018. “Big Data Analytics and Human Rights,” in Land,
M. K. and Aronson, J. D. (eds) New Technologies for Human Rights
Law and Practice. Cambridge: Cambridge University Press, pp. 149–
161. doi: 10.1017/9781316838952.007
https://www.cambridge.org/core/books/new-technologies-for-
human-rights-law-and-practice/big-data-analytics-and-human-
rights/AEFCC6D0B090A725742698E968E39431#
• Van Dijck, J., Poell, T. and De Waal, M., 2018. The Platform Society:
Public Values in a Connective World. Oxford University Press.
• V Vayena, E. and Tasioulas, J., 2016. The dynamics of big data and
human rights: the case of scientific research. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences, 374(2083), p.20160129.
https://doi.org/10.1098/rsta.2016.0129
10
(20.
Mar)
Lecture: Ethical
principles and
moral character
Principled
approaches to digital
ethics. Digital
technologies and
virtue ethics
Seminar:
Discussion of
essay topics
Core readings:
• Mittelstadt, B. ‘Principles alone cannot guarantee ethical AI.’ Nat
Mach Intell 1, 501–507 (2019). https://doi.org/10.1038/s42256-
019-0114-4
• Vallor, Shannon, 2016. Technology and the Virtues: A Philosophical
Guide to a Future Worth Wanting. Oxford University Press. Chapter
8. https://doi.org/10.1093/acprof:oso/9780190498511.003.0009
Recommended readings:
• Tasioulas, John, 2022. Artificial Intelligence, Humanistic Ethics.
Daedalus 151(2), pp.232-243.
https://doi.org/10.1162/daed_a_01912
• Vallor, Shannon, 2016. Technology and the Virtues: A Philosophical
Guide to a Future Worth Wanting. Oxford University Press
• Narayanan, A. and Vallor, S., 2014. Computing ethics: Why software
engineering courses should include ethics coverage.
Communications of the ACM 57(3), pp.23-25.
https://dl.acm.org/doi/fullHtml/10.1145/2566966
• Bilal. A. et al., 2021. Trust Development in Artificial Intelligence-
based Emerging Technologies: Rise of Technomoral Virtues and
Data Ethics. ACIS 2021 Proceedings, 52.
13
https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1051&context=
acis2021
11
(27.
Mar)
Lecture: Data and
the Knowledge
We Need
Final reflections on
using data science
for the good of the
planet
Seminar:
Data Story 6 –
Coronavirus
Tracking;
Module feedback
Core readings:
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical
Introduction. SAGE Publications. Chapter 10
• Pasquale, F., 2015. The Black Box Society. Harvard University Press.
Chapter 6: “Toward an Intelligible Society”, pp.189-218.
• Leonelli, S. (2021). Data Science in Times of Pan(dem)ic. Harvard
Data Science Review. https://doi.org/10.1162/99608f92.fbb1bdd6
Recommended readings:
• Ford, M., 2018. Architects of Intelligence: The truth about AI from
the people building it. Packt Publishing Ltd.
• Global Alliance for Genomics and Health (GA4GH, 2016).
Framework for responsible sharing of genomic and health-related
data. See https://www.ga4gh.org/wp-content/uploads/Framework-
Version-3September20191.pdf
• O’Brien D., Ullman J., Altman M., Gasser U., Bar-Sinai M., Nissim K.,
Vadhan S. and Wojcik M. J., 2017. OECD Recommendations on
Health Data Governance http://www.oecd.org/els/health-
systems/health-data-governance.htm
• Sachs, J.D., Schmidt-Traub, G., Mazzucato, M., Messner, D.,
Nakicenovic, N. and Rockström, J., 2019. Six transformations to
achieve the sustainable development goals. Nature
Sustainability, 2(9), pp.805-814. https://doi.org/10.1038/s41893-
019-0352-9
• Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch,
S., Felländer, A., Langhans, S.D., Tegmark, M. and Nerini, F.F., 2020.
The role of artificial intelligence in achieving the Sustainable
Development Goals. Nature communications, 11(1), pp.1-10.
https://doi.org/10.1038/s41467-019-14108-y
• Sterner, T., Barbier, E.B., Bateman, I., van den Bijgaart, I., Crépin,
A.S., Edenhofer, O., Fischer, C., Habla, W., Hassler, J., Johansson-
Stenman, O. […] and Lange, A., 2019. Policy design for the
Anthropocene. Nature Sustainability, 2(1), pp.14-21.
https://doi.org/10.1038/s41893-018-0194-x
• Fleming, L.E., Tempini, N., Gordon-Brown, H., Nichols, G., Sarran, C.,
Vineis, P., Leonardi, G., Golding, B., Haines, A., Kessel, A., Murray,
V., Depledge, M. and Leonelli, S., 2017. Big Data in Environment and
Human Health: Challenges and Opportunities. Oxford Encyclopaedia
for Environment and Human Health. Oxford University Press.
• Green, S. and Vogt, H., 2016. Personalizing Medicine: Disease
Prevention in silico and in socio. HUMANA. MENTE Journal of
Philosophical Studies, 9(30), pp.105-145.
