Input Presentation at the „Computational Communication Science: Towards a Strategic Roadmap” conference in Hannover (http://ccsconf.com/), 15th Feb 2018

1. Big Data and Research Ethics
Jan-Hinrik Schmidt  @janschmidt Hannover  15.02.2018
Senior Researcher for digital interactive media
and political communication
Input Presentation at the
„Computational Communication
Science: Towards a Strategic
Roadmap” conference in
Hannover (http://ccsconf.com/)

2. 2/9
My Background
• Disclaimer: limited practical experience with big data / large scale
computational research
• My remarks do not apply to „big data“ only - I want to make a few
more general points on the ethics of doing communication research in
datafied times
• My ad-hoc definition: Explaining and understanding communicative action and
its consequences via the study of digital traces and/or texts

3. 3/9
Framing Research Ethics
• Guiding principle for research ethics:
Do no Harm!
• More specifically (well, somewhat):
• Respect individuals and their rights
• Be a responsible member of academic community
• Serve democracy
• Different ethical paradigms frame research ethics, e.g.
• Utilitarian ethics: Do (societal) benefits of my research outweigh
(individual) harms I might cause?
• Ethics of care: Does my research respect diversity, unveil hidden power
structures, empower progressive change?
• Communicative ethics: Do I as researcher adhere to standards such as
veracity and truthfulness of communication?
© Benjamin Schwartz; https://pixels.com/featured/first-do-no-harm-benjamin-schwartz.html

4. 4/9
Research Ethics as part of Professional Practice
• Thinking about ethical implications of your research
right from the beginning is integral part of
professional research practice!
• But: there is no „cookbook“, no „one size fits all“
checklist of steps and decisions
• Ethical considerations usually include striking a
balance between competing aims and values
• Professional academic organizations issue guidelines
and recommendations for ethical research (e.g.
https://aoir.org/ethics )
http://de.muppet.wikia.com/wiki/D%C3%A4nischer_Koch

5. 5/9
Who is involved?
• Key distinction: Is research interested in traces or texts?
• Depending on answer, you
• treat people as subjects or as authors;
• touch different rights (privacy vs. copyright);
• need different ways to ensure consensual participation
• But: peculiarities of CCS introduce other agents besides „primary research
subjects“, posing difficult questions (for which I have no final answers):
• To what extent will research on relational data (e.g. social graph; interaction
patterns; conversational content) touch rights of other individuals?
• Since access to field is often only possible via platforms: How do their Terms
of Service deal with research? Can/should researchers be visible?
• How to treat computational agents, e.g. bots?
• Is it acceptable to use leaked / hacked datasets for research?
© M.C. Escher; https://www.wikiart.org/en/m-c-escher/drawing-hands

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Ensuring participants rights
• Main strategies to ensure participants‘ rights
• get informed consent before data gathering (e.g. at beginning of survey /
interview; before installing a research app)
• Provide debriefing in case of studies where research interest cannot be
stated beforehand (e.g. certain experimental studies)
• Practical problems:
• When is informed consent necessary?
• Who has to consent in cases of relational
data, conflict with platform ToS,
computational agents, leaked data?
• How to ensure pre-study consent really is
informed, especially given possible
unintended consequences resulting from
publication of findings / datasets?
McKee/Porter 2009: 21

7. 7/9
Publishing research
• Two good, but possibly contradictory practices:
• Aggregate findings in order to anonymize individual subjects
• Make datasets available for re-analysis
• But: the richer the dataset, the harder it is to truly anonymize
• In addition, even aggregated data might have unintended consequences
https://thedefensepost.com/2018/01/30/fitness-tracker-strava-opsec-risk/

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The big picture
• Big Data is crucial part of „surveillance capitalism“ (Zuboff 2015) –
companies as well as states rely on computational methods and
comprehensive datasets to monitor, control and monetarize citizens
• Is this – in the long run – compatible with
democracy? I don‘t know.
• Should CCS researchers aim to foster
democracy? Of course.
• So please always consider: Will your findings, your data, and/or your
methods support or suppress democratic practice?
http://www.independent.co.uk/arts-entertainment/tv/news/the-simpsons-sideshow-bob-finally-set-
to-kill-bart-simpson-at-halloween-as-spider-pig-returns-10302986.html

9. 9/9
Thank you!
Dr. Jan-Hinrik Schmidt
Hans-Bredow-Institut
Rothenbaumchaussee 36, 20148 Hamburg
j.schmidt@hans-bredow-institut.de
www.hans-bredow-institut.de
www.schmidtmitdete.de

10. 10/9
References and suggested readings
• Heise, Nele; Schmidt, Jan-Hinrik (2014): Ethik der Online-Forschung. In: Martin
Welker, Monika Taddicken, Jan-Hinrik Schmidt und Nikolaus Jackob (Hg.):
Handbuch Online-Forschung. Sozialwissenschaftliche Datengewinnung und -
auswertung in digitalen Netzen. Köln: Von Halem. 519–539.
• Heise, Nele (2017): Warum das Rad neu erfinden? Gedanken zur Diskussion
um Ethik in der Kommunikationswissenschaft. In: Medien &
Kommunikationswissenschaft, 4, 2017.
• Schlütz, Daniela; Möhring, Wiebke (2016): Kommunikationswissenschaftliche
Forschungsethik – Sonntagsworte, Selbstzweck, Notwendigkeit? In: Medien &
Kommunikationswissenschaft, 4, 2016. 483 – 496.
• Schlütz, Daniela; Möhring, Wiebke (2017): Das Rad rollt! In: Medien &
Kommunikationswissenschaft, 4, 2017.
• Zimmer, Michael; Kinder-Kurlanda, Katharina (Eds.): Internet Research Ethics
for the Social Age. New York: Peter Lang
• Zuboff, Shoshana (2015): Big other. Surveillance capitalism and the prospects
of an information civilization. In: Journal of Information Technology & Politics
30 (1), 75–89. DOI: 10.1057/jit.2015.5.