Data in Society
3
Research ethics and politics
Basic principles and challenges
Ethics: definitions
• Ethics in OED
• Moral principles that govern a person's behaviour or the conducting of an
activity
• Moral correctness of specified conduct
• Ethics vs morals
Unethical, immoral research
• History of abusing and exploiting human
subjects, especially vulnerable people
• Experiments on concentration camp
prisoners in WWII Germany
• Tuskagee Syphilis Study 1932-1972,
leads to establishment of ethics
committees to oversee human subjects
research
• Harming research subjects
• Harming research fields, institutions, and
future research; reputational damage and
unwillingness to participate
Formalisation of research ethics and
governance in the wake of research abuse
• Guidelines developed from scandals
• Nuremberg principles (from N. trials)
• Belmont report (from Tuskagee)
• … and attempts to self-regulate
• Declaration of Helsinki (1964-2008)
• CIOMS
• Stipulating principles of conduct in
human subjects research
• Upheld individual participant’s
rights and interests
• E.g. Belmont report: respect for persons;
beneficence; justice
• Introduced notions of risk/benefit
trade-off (with little specificity)
Traditional research ethics standards
• Generally aim to protect
individual dignity and
autonomy, but also individual
and institutional reputations as
well as research programmes
• Some basic principles include
• Quality
• Informed consent
• Confidentiality
• Data protection
Ethical issues in research – not a thing of
the past!
• Lots of loopholes for unscrupulous
organisations to escape ethical
checks
• Move to the Global South (Cooper,
Sunder-Rajan), in the private sector,
and online
• Private-funded research and data reuse research often exempt
• and even for the ethically minded it
is difficult to be specific about
possible uses of the data/AI
innovation
• Scandals are still frequent, even at
top public and private institutions
Ethics, regulation and governance
• Some practices can be outright regulated through legal text and
consequent compliance
• The need for ethics remains: not everything can be left to explicit
legal regulation as it can stifle innovation
• Research governance to interpret and stipulate ethics and
regulation according to the context of each research endeavour
• Governance to negotiate ways to ensure data science research is
harmless, beneficial to society, and innovative
Key references
•
•
Aicardi, C., Del Savio, L., Dove, E.S., Lucivero, F., Tempini, N., Prainsack, B., 2016. •
Emerging ethical issues regarding digital health data. On the World Medical
Association Draft Declaration on Ethical Considerations Regarding Health
•
Databases and Biobanks. CMJ 57, 207–213.
https://doi.org/10.3325/cmj.2016.57.207
Ashurst, C., Barocas, S., Campbell, R., Raji, D., 2022. Disentangling the
Components of Ethical Research in Machine Learning, in: 2022 ACM
Conference on Fairness, Accountability, and Transparency, FAccT ’22.
Association for Computing Machinery, New York, NY, USA, pp. 2057–2068.
https://doi.org/10.1145/3531146.3533781
Kitchin, R., 2014. The Data Revolution: Big Data, Open Data, Data
Infrastructures & Their Consequences. SAGE Publications Ltd, London.
Leonelli, S., 2016. Data-Centric Biology: A Philosophical Study. University of
Chicago Press, Chicago, IL.
•
Petermann, M., Tempini, N., Kherroubi-Garcia, I., Whitaker, K., Strait, A., 2022.
Looking before we leap: Expanding ethical review processes for AI and data
science research. Ada Lovelace Institute, London.
https://www.adalovelaceinstitute.org/report/looking-before-we-leap/
•
Ruckenstein, M., Schüll, N.D., 2017. The Datafication of Health. Annual Review
of Anthropology 46, 261–278. https://doi.org/10.1146/annurev-anthro102116-041244
•
Sharon, T., 2016. The Googlization of health research: from disruptive
innovation to disruptive ethics. Personalized Medicine.
https://doi.org/10.2217/pme-2016-0057
•
Birhane, A., 2021. Algorithmic injustice: a relational ethics approach. Patterns
2, 100205. https://doi.org/10.1016/j.patter.2021.100205
•
Boyd, D., Crawford, K., 2012. Critical Questions for Big Data. Information,
Communication & Society 15, 662–679.
•
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V.,
•
Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., Vayena, E.,
2018. AI4People—An Ethical Framework for a Good AI Society: Opportunities,
Risks, Principles, and Recommendations. Minds & Machines 28, 689–707.
https://doi.org/10.1007/s11023-018-9482-5
•
Floridi, L., Taddeo, M., 2016. What is data ethics? Philosophical Transactions of •
the Royal Society A: Mathematical, Physical and Engineering Sciences 374,
20160360. https://doi.org/10.1098/rsta.2016.0360
Tempini, N., 2020. The Reuse of Digital Computer Data: Transformation,
Recombination and Generation of Data Mixes in Big Data Science, in: Leonelli,
S., Tempini, N. (Eds.), Data Journeys in the Sciences. Springer International
Publishing, Cham, pp. 239–263.
Tempini, N., 2023. The ethics of data self-reporting: important issues and best
practices. F1000Research. https://doi.org/10.12688/f1000research.128911.1
Digital publics
Participatory culture
• Jenkins (2009), theorist of convergence, characterizes
participatory culture
• With relatively low barriers to artistic expression and civic engagement
• With strong support for creating and sharing one’s creations with others
• With some type of informal mentorship whereby what is known by the
most experienced is passed along to novices
• Where members believe that their contributions matter
• Where members feel some degree of social connection with one another
(at the least they care what other people think about what they have
created). (p. 7)
P2P (peer to peer) projects
Very large, knowledge-intensive
Distributed, piecemeal contributions
No obligations, no formal labouring
Collective intelligence (Levy)
Hive mind metaphor
User-created content
“How much could we accomplish if we edit the Wikipedia, or input data on
PatientsLikeMe, with the time we use to binge-watch tv shows?”
Participation
Community informatics
• Open Street Map; maintained by
millions of users
• Used by major corporations in their
products: Apple, Nokia, Microsoft,
Google, etc. etc.
• Volunteer citizens use web
interfaces to input new data,
validate existing data, maintain an
up to date resource
• People are increasingly ready and
proficient at using digital tools for
organizing in the wake of emergency
or wide social unrest
Open Street Map
Data Activism
• Online forums and discussion groups have been used by activist organisations
of patients to reshape the research agenda, organize research, create
momentum
• Online forums as both communities of practice and epistemic communities (Akrich)
• Data activism as source of alternative epistemologies (Milan and Velden; Bruno et al.)
• Rise of patient-led research (Vayena)
• Data journalism can involve long-term projects of data collection to inform
media reporting and apply pressure on policy makers
• Control of the data is key; data governance models are key
• Proprietary models vs data cooperatives (Hafen; Tempini & Del Savio) where citizens
decide together what to do with the data they pool and share
• Participation gap again: big data rich vs big data poor (boyd and Crawford)
• Who is ready to take most advantage of the new opportunities?
Citizen science
• Participation in science, scientist and
citizen collaboration
• Bottom-up initiatives: “patient-led
research” and patient activism e.g.
PatientsLikeMe
• Top-down citizen science:e.g.
Zoouniverse; Fold it
• vs Amazon’s Mechanical Turk –
used for crowdsourcing menial
scientific work
Could you make science the way that
Wikipedia makes encyclopedia?
Qualities of participation
• Ladder of Citizen Participation
(Arnstein 1969)
• Focus on decision making in urban
planning
• Other important debate for the
participatory production of
knowledge is about the governance
of the Lay vs Expert divide:
• Competencies, ‘abstract’
knowledge (expert)
• Knowledge of the local setting,
‘context’ outside formalisms
(lay), stakes
• Both build from ‘experience’
From participatory medicine to patient-led
research
• Startups emerge that fuse networking
(finding others and interacting) with
self-report data collection:
curetogether; 23andme
• In PatientsLikeMe things go one step
further when patients use selfreporting tools and networking features
to organize new research ‘bottom-up’
• The rise of patient-led research
(Vayena); promises and risks
• Tension between rhetoric of
empowerment and commercial
interests (Prainsack); need to
understand the infrastructural
foundations or participatory medicine
Wicks et al. 2008
Eric Valor
(via diymedicine.wordpress.com)
The real story here, which you ignore or dismiss, is
patient empowerment. For too long have we been isolated
and told to just wait to die. We can use technology now to
organize and, more importantly, mobilize. We now can
and will take action to promote OUR agenda, not the feeble
selfish agenda of certain advocacy organizations which do
very little beside provide employment for their directors. We
are trained in highly-skilled technology trades. We might not
be doctors, but we are engineers trained to research and
resolve highly complex problems. We are capable of
learning and comprehending complicated concepts. We
understand the limits of our abilities and, further, that
without our efforts the pace of the fight against disease is
much too slow for people living today. And we have no
delusion that anything we are doing is intended to
replace clinical trials. Rather we intend to augment and
push forward the actual science.
(Eric Valor, July 2012 – emphasis added)
Key points on participatory research
platforms
• The web platform is engineered to support participation in data
collection (empty container ready to be filled by users – Nardi and
Ekbia)
• But, designed to co-opt particular kinds of participation
• On the one hand, various issues related to the governance of
patient self-report data, and the ownership of the research
projects – the cause of patient empowerment is exploited
• Issues of long-term evolution; business model of data monopolization;
hijacking of patient initiative
• On the other hand, other spontaneous initiatives at the fringes are
less accountable, ephemeral, and rife of ethical issues and harms
Data governance models
• Proprietary: criticized as extractive, neo-colonial, close to surveillance,
tied to multi-sided markets
• Public-centralized: typical of public administration and national health
system datasets; data governance managed by a mix of representatives
(oft including members of the public)
• Alternative data governance models
• Data Cooperatives (cfr. American vs EU model): a cooperative managed by
member representatives by (Hafen 2019; Tempini and Doughty under review)
• Data Trusts: a trust governed by trustees is charged (Delacroix and Lawrence,
2019)
• And others, Personal Information Management Systems, Data Unions,
Marketplaces and Sharing Pools (Micheli et al. 2023)
Cited references
•
Akrich, M., 2009. From Communities of Practice to Epistemic Communities: Health
Mobilizations on the Internet. SRO 15, 10.
•
Shirky, C., 2010. Cognitive Surplus. Penguin Press, London.
•
Benkler, Y., 2007. The wealth of networks: How social production transforms markets and
freedom. Yale University Press, New Haven & London.
•
Tempini, N., 2015. Governing PatientsLikeMe: information production and research
through an open, distributed and data-based social media network. The Information
Society 31, 193–211.
•
Bruno, I., Didier, E., Vitale, T., 2014. Statactivism: Forms of action between disclosure and
affirmation. Partecipazione e conflitto 7, 198–220.
•
Tempini, N., Del Savio, L., 2019. Digital orphans: Data closure and openness in patientpowered networks. BioSocieties 14, 205–227.
•
Delacroix S and Lawrence ND (2019) Bottom-up data Trusts: disturbing the ‘one size fits
all’ approach to data governance. International Data Privacy Law 9(4): 236–252.
•
Tempini, N., Teira, D., 2019. Is the genie out of the bottle? Digital platforms and the future
of clinical trials. Economy and Society 48, 77–106.
•
Hafen, E., 2019. Personal Data Cooperatives – A New Data Governance Framework for Data •
Donations and Precision Health, in: Krutzinna, J., Floridi, L. (Eds.), The Ethics of Medical
Data Donation, Philosophical Studies Series. Springer International Publishing, Cham, pp.
•
141–149.
•
Jenkins, H., 2006. Convergence culture: where old and new media collide. New York
University Press, New York.
•
Micheli M, Farrell E, Carballa-Smichowski B, et al. (2023) Mapping the landscape of data
intermediaries: emerging models for more inclusive data governance. JRC133988. LU: Joint
Research Centre (European Commission), Publications Office of the European Union.
Available at: https://data.europa.eu/doi/10.2760/261724 (accessed 9 November 2023).
•
Milan, S., Velden, L. van der, 2016. The Alternative Epistemologies of Data Activism. Digital
Culture & Society 2, 57–74.
•
Rosenblum, N.L., Muirhead, R., 2020. A Lot of People Are Saying: The New Conspiracism
and the Assault on Democracy. Princeton University Press.
Vayena, E., Tasioulas, J., 2013. The ethics of participant-led biomedical research. Nat
Biotech 31, 786–787.
Zittrain, J.L., 2008. The Future of the Internet and How to Stop It. Yale University Press, New
Haven.