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Operationalizing Digital Self Determination
Dr. Stefaan G. Verhulst
The GovLab, Tandon School of Engineering, New York University, New York, USA
ISI Foundation, Turin, Italy
stefaan@thegovlab.org
October 25, 2022
Abstract
A proliferation of data-generating devices, sensors, and applications has led to
unprecedented amounts of digital data. We live in an era of datafication, one in which life
is increasingly quantified and transformed into intelligence for private or public benefit.
When used responsibly, this offers new opportunities for public good. The potential of
data is evident in the possibilities offered by open data and data collaboratives—both
instances of how wider access to data can lead to positive and often dramatic social
transformation. However, three key forms of asymmetry currently limit this potential,
especially for already vulnerable and marginalized groups: data asymmetries, information
asymmetries, and agency asymmetries. These asymmetries limit human potential, both in
a practical and psychological sense, leading to feelings of disempowerment and eroding
public trust in technology.
Existing methods to limit asymmetries (e.g., consent) as well as some alternatives under
consideration (data ownership, collective ownership, personal information management
systems) have limitations to adequately address the challenges at hand. A new principle
and practice of digital self-determination (DSD) is therefore required.
The study and practice of DSD remains in its infancy. The characteristics we have
outlined here are only exploratory, and much work remains to be done so as to better
understand what works and what doesn’t. We suggest the need for a new research
framework or agenda to explore DSD and how it can address the asymmetries,
imbalances, and inequalities—both in data and society more generally—that are
emerging as key public policy challenges of our era.
Operationalizing Digital Self Determination, S. Verhulst 1
Operationalizing Digital Self Determination
Dr. Stefaan G. Verhulst1
Introduction
Our world is awash in data. Every day, some 2.5 quintillion bytes of data are generated,2
and in 2020, approximately 64.2ZB of data was created or replicated.3According to the
International Data Corporation (IDC), the amount of data created or replicated is growing
at a compound annual growth rate of 23%, driven by the proliferation of Internet of
Things (IoT) devices, remote sensors, and other data collection methods that are now
deeply intertwined with virtually every aspect of people’s professional and social lives.4
We have transitioned to a new era of Datafication—one in which human life is
increasingly quantified, often without the knowledge of data subjects, and frequently
transformed into intelligence that can be monetized for private or public benefit. At the
same time, this new era offers tremendous opportunities: the responsible use and re-use
of data can help address a host of apparently intractable societal and environmental
problems, in large part by improving scientific research, public policymaking and
decision-making.5Yet, it has also become increasingly clear that the datafication is
simultaneously marked by a number of asymmetries, silos, and imbalances that are
restricting the potential of data. This tussle—between potential and limits—is emerging as
one of the central public policy challenges of our times.
In what follows, we outline a number of ways in which asymmetries are limiting the
potential of datafication. In particular, we explore the notion of agency asymmetry,
arguing that power imbalances in the data ecology are in effect disempowering key
5Stefaan Verhulst, Andrew Young, Andrew J. Zahuranec, et al., The Emergence of a Third Wave of Open Data: How To
Accelerate the Re-Use of Data for Public Interest Purposes While Ensuring Data Rights and Community Flourishing
(Brooklyn: The GovLab, 2020), http://dx.doi.org/10.2139/ssrn.3937638.
4Ibid.
3John Rydning, Worldwide Global DataSphere and Global StorageSphere Structured and Unstructured Data Forecast,
2021–2025 (Needham: IDC Corporate USA, 2021), www.idc.com/research/viewtoc.jsp?containerId=US47998321.
2Branka Vuleta, “How Much Data Is Created Every Day? + 27 Staggering Stats,” Seed Scientific (blog), October 28,
2021, https://seedscientific.com/how-much-data-is-created-every-day/.
1The draft is based upon a presentation given to the October 2022 Residents of the Rockefeller Foundation Bellagio
Center, and the author appreciates the input received from the residentes and the support of the Rockefeller
Foundation to spent a month at the Center. The author is also grateful to Sampriti Saxena for her research assistance
and the members of the International Network on Self Determination who provided comments to earlier drafts.
https://idsd.network/)
Operationalizing Digital Self Determination, S. Verhulst 2
stakeholders, and as such undermining trust in how data is handled. We suggest that in
order to address agency asymmetry (along with other forms of asymmetry), we need a
new principle and practice of digital self-determination. This principle is built on the
foundations of long-established philosophical, psychological, and legal principles of
self-determination but updated for the digital era.
In Part I, we explore how datafication has come about, and some of the asymmetries it
has led to. Existing methods of addressing these asymmetries, we suggest, are
insufficient; we need a new concept and practice of digital self-determination (DSD). We
examine DSD in Part II, showing how it builds on a pre-existing intellectual tradition
pertaining to self-determination. Part III contains a case study (on migration) that
illustrates the notion of DSD, and Part IV contains some specific recommendations for
operationalizing the practice of DSD.6
I. Context: Digital Transformation and Datafication
The notion of datafication is sometimes conflated with big data. As we have elsewhere
written, the two phenomena may be said to exist on a spectrum, but are in fact distinct.7
In particular, datafication extends beyond a mere technical phenomenon and has what
may be considered a sociological dimension. As Mejias and Couldry argue in the Internet
Policy Review, datafication includes “the transformation of human life into data through
processes of quantification,” and this transformation, the authors further argue, has
“major social consequences … [for] disciplines such as political economy, critical data
studies, software studies, legal theory, and—more recently—decolonial theory.”8
One aspect of datafication that is particularly relevant for our discussion here is that it is
often intermingled with hierarchy and relationships of power. It exists, as Mejias and
Couldry suggest, at “the intersection of power and knowledge.”9This has tremendous
implications for how data is accessed and distributed, in particular to the creation of data
silos and asymmetries. We return to these challenges below. First, we consider the
potential offered by datafication.
A) Potential of Datafication: Reuse for Public Interest Purposes
9Ibid.
8Ulises A. Mejias and Nick Couldry, “Datafication,” Internet Policy Review 8, no. 4. (2019),
https://policyreview.info/concepts/datafication.
7Stefaan G. Verhulst, “The Value of Data and Data Collaboratives for Good: A Roadmap for Philanthropies to Facilitate
Systems Change Through Data,” in Data Science for Social Good, ed. Massimo Lapucci and Ciro Cattuto (Cham:
Springer, 2021), 9-27, https://doi.org/10.1007/978-3-030-78985-5_2.
6This paper originates from the deliberations and activities of the International Network on Digital Self-Determination.
Learn more about the Network’s work at idsd.network/.
Operationalizing Digital Self Determination, S. Verhulst 3
In order to understand the potential of datafication, we need to explore the possibilities
offered by data reuse. Data reuse takes place when information gathered for one
purpose is repurposed (often in an anonymized or aggregated) form for another
purpose,, generally with an intended public benefit outcome. For example, location data
collected by private telecommunication operators can be reused to understand human
mobility patterns, which can help with public aid responses to mass migration events or
during ecological crises. Likewise, clinical data held by medical practitioners can often be
reused in the development of new drugs and treatments.
Such benefits are often realized through two key vehicles:
●Open Data, which involves data holders (typically in governmental and academic
sectors) releasing data publicly, so that it can be “freely used, reused, and
redistributed by anyone”.10
●Data collaboratives, which are an emerging form of partnership, often between
the public, academic and private sectors, that allow for data to be pooled and
reused across data sets and sectors.11 Data collaboratives can take a number of
forms and allow data holders to provide access to data with other stakeholders for
the public benefit without necessarily losing control or giving up a competitive
advantage (this may be of particular concern to corporations and companies). In
the example cited above, for instance, telecoms firms can provide access to their
data with third-party researchers and responders while still maintaining their own
stakeholder interests in the data.
B) Key Challenge: Data Asymmetries
Open data and data collaboratives offer real potential to address some of the most
intractable problems faced by society. When used responsibly and in the right data
ecology, they can help policymakers by improving situational awareness, drawing clearer
connections between cause and effect, enhancing predictive capabilities, and improving
our understanding of the impact of critical decisions and policies.12 All of this, when
combined, can make a real and tangible difference in public decision-making. Similar
12 Stefaan G. Verhulst, Andrew Young, Michelle Winowatan and Andrew J. Zahuranec, Leveraging Private Data for
Public Good: A Descriptive Analysis and Typology of Existing Practices (Brooklyn: The GovLab, 2019),
https://datacollaboratives.org/static/files/existing-practices-report.pdf.
11 Andrew Young and Stefaan G. Verhulst, “Data Collaboratives,” in The Palgrave Encyclopedia of Interest Groups,
Lobbying and Public Affairs, eds. Phil Harris, Alberto Bitonti, Craig S. Fleisher and Anne Skorkjær Binderkrantz (Cham:
Palgrave Macmillan, 2020), https://doi.org/10.1007/978-3-030-13895-0_92-1.
10 “What is Open Data?,” Open Data Handbook, Open Knowledge Foundation, 2015,
https://opendatahandbook.org/guide/en/what-is-open-data/.
Operationalizing Digital Self Determination, S. Verhulst 4
benefits exist in the scientific community where greater access to data can lead to new
research while allowing experiments to be reproduced and verified by anyone.13
At the moment, though, this potential is often held back. The key restriction stems from
asymmetries in the way data is collected and, especially, stored (or hoarded). An era
marked by unprecedented abundance—of data and other potential public goods—is also
marked by vast disparities and hierarchies in how that abundance is distributed and
accessed. Today, much of our data exists in silos, hidden from public view or usage, thus
limiting the ability of policymakers, researchers, or other actors to benefit from its
possibilities. In addition, the public is often left in the dark about how data is being
collected, for what purpose, and how it is being used.
Three forms of asymmetry are worth highlighting:
●Data asymmetries, in which those who could benefit from access to data or draw
out its potential are restricted from access;
●Information asymmetries, where there is a mismatch in awareness between data
holders, data subjects, and potential users, meaning that data that could be useful
is never sought or deployed; and
●Agency asymmetries, where data relationships between parties are marked by
imbalances and hierarchies, meaning that one party—typically one that is already
vulnerable and disenfranchised—is further disempowered. For instance, large
quantities of data are collected on children daily, tracking their movements,
communications, and more. Yet they (and their caregivers) have little to no agency
over their data, and how it is used and later reused.14
The persistence of such asymmetries has a number of negative consequences. Most
obviously, the potential public benefits of access to and reuse of data (e.g., through
improved research or policymaking) are not fully realized. Lack of access to data may
also contribute to bias in the analysis, especially if data hoarding leads to the exclusion of
certain populations in the datasets.15 In addition, the power imbalances mean that in
effect an extractive relationship often exists between data subjects (e.g., citizens) and
15 This is especially of concern for developing countries and vulnerable groups, where a lack of access to
representative data may further exclude these populations and amplify existing biases favoring specific groups or
countries. See Seastedt et al., 2022 (https://doi.org/10.1371/journal.pdig.0000102).
14 Andrew Young and Stefaan G. Verhulst, “Why we need responsible data for children,” The Conversation, March 23,
2020, https://theconversation.com/why-we-need-responsible-data-for-children-134052.
13 Ed Yong, “How Reliable Are Psychology Studies?,” The Atlantic, August 27, 2015,
https://www.theatlantic.com/science/archive/2015/08/psychology-studies-reliability-reproducability-nosek/402466/.
Operationalizing Digital Self Determination, S. Verhulst 5
data holders (e.g., large companies), posing a number of practical and ethical
consequences (observers have written of a sense of “data colonization”).
All of this leads to a number of less obvious and more psychological, but no less
insidious, consequences. The asymmetries and the sense of colonization lead to a
feeling of disempowerment and a lack of autonomy, especially among populations that
are already vulnerable, and this in turn erodes public trust in both technology and
institutions—one of the defining problems of our times.
For all these reasons, it is essential that steps be taken to address the asymmetries that
are at the heart of our data economy, in the process helping to unlock the value of the
data age and spurring new forms of innovation in public decision-making. In Part II,
below, we examine a principle and practice of digital self-determination that we believe
is central to this process.
C) Existing Methods of Rebalancing Asymmetries—and their Limitations
First, though, we examine some existing methods either being deployed or considered to
address these asymmetries. While these methods are well-intentioned and do sometimes
have at least a marginal effect, we suggest that each has limitations and that, individually
and collectively, they fail to address the underlying magnitude or scope of the problem.
i) Consent
Today, the default approach to addressing information and power asymmetries involves
the concept of informed consent. In this method, information about data handling policies
is shared with data subjects, who then have the option of consenting whether or not to
allow their data to be collected, accessed and (re)used. This has been the primary
vehicle for providing data subjects with a "choice" since the widespread adoption of the
Fair Information Practice Principles, approximately thirty years ago.16 Yet, despite its
widespread use and despite the fact that it provides the bedrock for many legislative
efforts concerning data management,17 informed consent has a number of shortcomings:
17 Including for example, the EU’s GDPR and the OECD’s Guidelines on the Protection of Privacy and Transborder Flows
of Personal Data.
16 U.S. Federal Trade Commission, Privacy Online: A Report to Congress, by Martha K. Landesberg, Toby Milgrom Levin,
Caroline G. Curtin and Ori Lev (Washington, D.C.: FTC, 1998),
https://www.ftc.gov/sites/default/files/documents/reports/privacy-online-report-congress/priv-23a.pdf.
Operationalizing Digital Self Determination, S. Verhulst 6
●Binary: Generally, opt-in or opt-out regimes dominate the practice of consent.18 Yet
such approaches tend to be binary–for or against collection or sharing—and thus
inappropriately reductive. Some versions do allow for a greater level of granularity
(i.e., more boxes to be checked), but even these fail to capture the true nuances
and complexity of how data is collected, used, and reused.
●Informational Shortcomings: To truly confer agency, informed consent practices
would need to convey a robust understanding of the nature, significance,
implications, and risks of data collection, use and reuse.19 For example, data
subjects should be made aware of the immediate uses of their data, and also
potential future uses. Such “rich information” is generally lacking, thus
compromising citizens’ ability to provide genuine consent.
●Collective vs. Individual: Informational shortcomings are exacerbated by the fact
that consent policies are typically aimed at informing individuals about how their
data will be used. In truth, however, data sets are often combined and repurposed
in ways that have significant consequences for groups or communities. More
responsible forms of consent would pay greater attention to the interests of
communities.20
●Limited Scope: Finally, existing consent mechanisms are limited because much of
the ethical and policy debate focuses on the scope of the original consent and
whether reuse is permissible in light of that scope.21 As a result, most consent
regimes have a difficult time handling repurposing, which is so essential to
fulfilling the potential of data. Recent years have witnessed the development of
more open-ended consent models,22 and several groups, such as the World
Economic Forum,23 have tried to improve on existing methods of consent to
23 Kimberly Bella, Christophe Carugati, Cathay Mulligan and Marta Piekarska-Geater, Data for Common Purpose:
Leveraging Consent to Build Trust (Cologny: World Economic Forum, 2021),
https://www3.weforum.org/docs/WEF_Data_for_Common_Purpose_Leveraging_Consent_to_Build_Trust_2021.pdf.
22 Ibid.
21 Ibid.
20 Leslie P. Francis and John G. Francis, “Data Re-Use and the Problem of Group Identity,” University of Utah College of
Law Research Paper No. 311, Studies in Law, Politics and Society 73 (2017), http://dx.doi.org/10.2139/ssrn.3371134.
19 European Commission - Research Directorate-General, Guidance for Applicants: Informed Consent (Directive
2001/20/EC) (Brussels: European Commission, 2021),
https://ec.europa.eu/research/participants/data/ref/fp7/89807/informed-consent_en.pdf.
18 Yvonne de Man, et al., “Opt-in and opt-out consent procedures for the reuse of routinely recorded health data in
scientific research and their consequences for consent rate and consent bias–A systematic review” (Preprint, 2022),
https://doi.org/10.21203/rs.3.rs-1434893/v1.
Operationalizing Digital Self Determination, S. Verhulst 7
propose new methods. However, these too contain many ethical limitations, and
can even act as bottlenecks to qualitative studies.24
ii) Alternative Consent Mechanisms—and their Shortcomings
In part due to these shortcomings, some have suggested “post-consent privacy,” while
others have suggested the establishment of alternative rights and technologies.
However, each of these also contains certain limitations.25
●Data Ownership Rights: One approach is to treat data as the private
property of data subjects. While in theory this could enhance agency, it
poses the serious problem of undermining the public good properties of
data. Data isnon-rivalrous, non-excludable, and non-depletable, making
it by definition a public good.26 While ownership of data may appear to
solve problems related to consent and control, it raises serious
concerns regarding the marketization and commodification of data.27 In
truth, a lack of clarity regarding the notion of ownership when it comes
to data means that it cannot be treated as solely a public or private
good.
●Collective Ownership: Ownership of data can be at the level of the
individual, the community, or group. Group-level ownership has most
commonly been explored under the rubric of “data sovereignty,” which
places data under the jurisdictional control of a single political entity.28
Collective ownership poses many of the same challenges as those
posed by individual ownership, notably those associated with the
privatization of a public good. In addition, a lack of operationalization in
terms of clear and enforceable legal frameworks around data
ownership makes it difficult to establish or operationalize data
sovereignty.29 National or subnational jurisdictions are often in conflict,
29 Patrik Hummel, Matthias Braun and Peter Dabrock, “Own Data? Ethical Reflections on Data Ownership,” Philosophy
& Technology 34 (2021), https://doi.org/10.1007/s13347-020-00404-9.
28 Théodore Christakis, “European Digital Sovereignty”: Successfully Navigating Between the “Brussels Effect” and
Europe’s Quest for Strategic Autonomy (Grenoble: CESICE, 2020), http://dx.doi.org/10.2139/ssrn.3748098.
27 Jonathan Montgomery, “Data Sharing and the Idea of Ownership,” The New Bioethics 23, no. 1 (2017),
https://doi.org/10.1080/20502877.2017.1314893.
26 Patrik Hummel, Matthias Braun and Peter Dabrock, “Own Data? Ethical Reflections on Data Ownership,” Philosophy &
Technology 34 (2021), https://doi.org/10.1007/s13347-020-00404-9.
25 Solon Barocas and Helen Nissembaum, “Big Data’s End Run around Anonymity and Consent,” in Privacy, Big Data,
and the Public Good: Frameworks for Engagement, eds. Julia Lane, Victoria Stodden, Stefan Bender and Helen
Nissenbaum (Cambridge: Cambridge University Press, 2014), https://doi.org/10.1017/CBO9781107590205.004.
24 Sara Mannheimer, “Data Curation Implications of Qualitative Data Reuse and Big Social Research,” Journal of
eScience Librarianship 10, no. 4 (2021), https://doi.org/10.7191/jeslib.2021.1218.
Operationalizing Digital Self Determination, S. Verhulst 8
and different areas of law operate differently. For example, while
intellectual property rights30 protect certain aspects of data reuse,
criminal law31 may interpret reuse as a form of theft. Overall, the
absence of a legal framework allows data subjects to be exploited,
while also limiting the effective reuse of data for the public good.
●Personal Information Management Systems: Personal Information
Systems (PIMS)32 are sometimes proposed as alternative systems of
data management to empower individuals with greater control over
their personal data. PIMS are typically centralized or decentralized
systems through which individuals can choose to share (or not share)
their personal data. This method also faces some important limitations.
First, there is a danger that, rather than conferring control on individual
subjects, PIMS will simply transfer control to owners and operators of
large PIMS systems. In this argument, a PIMS-based system of consent
will end by replicating (and perhaps aggravating) existing hierarchies
and asymmetries.
Second, PIMS remain highly susceptible to the many vulnerabilities of
the existing data ecosystem. In particular, they are prone to hacking and
breaches, and are only as robust as the surrounding legal and policy
ecosystem that protects how data is collected, stored, and shared.33
Finally, the adoption of PIMS has been stunted by a lack of adequate
use cases and, consequently, an insufficiently proven business case.34
Without stronger models and stress-tested best practices, the potential
of PIMS remains more conceptual than proven.
II. Need for new principle: Digital Self Determination
34 Heleen Janssen and Jatinder Singh, “Personal Information Management Systems,” Internet Policy Review 11, no. 2
(2022), https://policyreview.info/glossary/personal-information-management-systems.
33 “Personal information management systems: A new era for individual privacy?,” Privacy Tech, International
Association of Privacy Professionals (IAPP), March 21, 2019,
https://iapp.org/news/a/personal-information-management-systems-a-new-era-for-individual-privacy/.
32 “Personal Information Management System,” European Data Protection Supervisor, European Union, 2021,
https://edps.europa.eu/data-protection/our-work/subjects/personal-information-management-system_en.
31 Kathleen Liddell, David A. Simon and Anneke Lucassen, “Patient data ownership: who owns your health?,” Journal of
Law and the Biosciences 8, no. 2 (2021), https://doi.org/10.1093/jlb/lsab023.
30 Sara Mannheimer, “Data Curation Implications of Qualitative Data Reuse and Big Social Research,” Journal of
eScience Librarianship 10, no. 4 (2021), https://doi.org/10.7191/jeslib.2021.1218.
Operationalizing Digital Self Determination, S. Verhulst 9
All these shortcomings, of both existing and hypothetical methods of agency, call out for
a new approach to addressing the asymmetries of our era. Our proposed solution rests
on the principle of digital self-determination (DSD). As noted, DSD is built on the
foundations of existing practices and principles about self-determination. As a working
definition, we propose the following:
Digital Self-Determination is defined as the principle of respecting, embedding,
and enforcing people's and people's agency, rights, interests, preferences, and
expectations throughout the digital data life cycle in a mutually beneficial manner
for all parties involved.
A) The Concept of Self Determination
The above definition may be a relatively new concept, but it stems from a historical body
of exploration that involves philosophy, psychology, and human rights jurisprudence. The
term “self-determination” is often attributed to the German philosopher Immanuel Kant,
who wrote in the 19th century about the importance of seeing humans as “moral agents”
who would respect rules over their own needs and emotions because of an innate
feeling of social “duty.”35 Regardless of personal feelings, he argued, humans “have the
duty to respect dignity and autonomy”—the self-determination—of others.36 More
generally, Kant’s philosophy affirmed the importance of treating individuals as ends
rather than means, and of the importance for individuals to be able to remain
eigengesetzlich (autonomous).37
Self-determination can also be explored through the prism of psychology, where the
ability to make decisions for oneself is often considered central to people’s motivations,
well-being, and fulfillment.38 For instance, Ryan and Deci (1980) explore
self-determination theory, which argues that there is a dichotomy between “automated,”
instinctive behaviors and consciously “self-determined behaviors” to achieve a specific
outcome.39
39 Edward L. Deci and Richard M. Ryan, “Self-determination Theory: When Mind Mediates Behavior,” The Journal of
Mind and Behavior 1, no. 1 (1980): 33-43, https://www.jstor.org/stable/43852807.
38 Richard M. Ryan and Edward L. Deci, “Self-Regulation and the Problem of Human Autonomy: Does Psychology Need
Choice, Self-Determination, and Will?,” Journal of Personality 74, no. 6 (2006): 1557-1586,
https://doi.org/10.1111/j.1467-6494.2006.00420.x.
37 Kimberly Hutchings, “The question of self-determination and its implications for normative international theory,”
Critical Review of International Social and Political Philosophy 3, no. 1 (2000): 91-120,
https://doi.org/10.1080/13698230008403304.
36 Immanuel Kant, “The Metaphysics of Morals,” in Practical Philosophy, ed. Mary J. Gregor (Cambridge: Cambridge
University Press, 1997), https://doi.org/10.1017/CBO9780511813306.
35 Nydia Remolina and Mark Findlay, “The Paths to Digital Self-Determination–A Foundational Theoretical Framework,
Singapore Management University Centre for AI & Data Governance Research Paper 03/2021 (2021),
https://dx.doi.org/10.2139/ssrn.3831726.
Operationalizing Digital Self Determination, S. Verhulst 10
Finally, it is also worth considering international law, which upholds the notion of
self-determination as it applies to both states and their constitutive members, i.e.,
individuals. Self-determination is for instance closely associated with the decolonization
movement, as well as with movements for the autonomy and independence of
indigenous people. In 2007, the UN Declaration on the Rights of Indigenous Peoples
(UNDRIP) acknowledged the right of peoples to practice customs and cultures “without
outside interference” while also taking “part in the conduct of public affairs at any level,”40
thereby asserting the fundamental importance of autonomy for individuals and groups of
people. The basis for this importance can be extended even further back, to Article 1 of
the 1966 International Covenant on Economic, Social and Cultural Rights and the
International Covenant on Civil and Political Rights, which state that: “All peoples have
the right to self-determination. By virtue of that right they freely determine their political
status and freely pursue their economic, social, and cultural development.”41
B) Digital Self Determination
The increased digitization and datafication of society lead us to extend these notions of
self-determination to a concept of digital self-determination. DSD includes the following
key aspects and components:
DSD is mainly concerned with agency about data
First, with the advent of digital technologies rapidly advancing the collection, storage and
use of data, the need for DSD has become increasingly more pressing in recent years. As
a concept, it rests essentially on the understanding that, in a digital society, data and
individuals are not separate entities, but mutually constitutive.42 Thus control over one’s
data representation is fundamentally a matter of individual agency and liberty.
DSD has both an individual and collective dimension
Second, like physical self-determination, DSD has an external collective dimension that
accounts for the influence that other people, peoples and communities have on the
virtual social self. Vice versa, DSD also has an internal individual dimension that defines
the whole online self as the sum of three elements: one’s virtual persona, one’s data, and
42 See Remolina and Findlay (2021), who propose a five-step DSD framework.
41 UN General Assembly, International Covenant on Economic, Social and Cultural Rights (Resolution 2200A - XXI),
December 16, 1966,
www.ohchr.org/en/instruments-mechanisms/instruments/international-covenant-economic-social-and-cultural-rights.
40 Daniel Thürer and Thomas Burri, “Self-Determination,” in Max Planck Encyclopedias of International Law, ed. Rüdiger
Wolfrum (Heidelberg: Max Planck Institute for Comparative Public Law and International Law, 2008),
https://opil.ouplaw.com/view/10.1093/law:epil/9780199231690/law-9780199231690-e873?prd=EPIL.
Operationalizing Digital Self Determination, S. Verhulst 11
data about oneself. Each of these elements is essential in considering the notion of DSD
and how best to apply it.
DSD can especially benefit the vulnerable, marginalized, and disenfranchised
Third, DSD is ethically desirable for the way in which it protects subject rights, whether
individually or collectively. It is also worth noting that the need for DSD is particularly
important to protect the rights of society’s most marginalized and disenfranchised—those
who are typically less included in, and aware of, the emerging processes of social
datafication. These populations are often the most vulnerable to digital asymmetries, and
already excluded from various aspects of social and economic life in the digital era. As
such, there is a strong redistributive dimension to DSD.
DSD can leverage existing practices of principled negotiation
Fourth, the notion of determination creates a new avenue for negotiation beyond
traditional institutional levers. Based on learnings from principled negotiation theory, DSD
can help to establish objective criteria, focus on specific individual and collective
interests, and unite common options.43 Negotiations framed around objective criteria are
more efficient and productive, as they highlight mutual gain and help balance power
asymmetries. DSD plays a role in establishing objective criteria by empowering
individuals to advocate for and establish their pre-existing interests in a broader
negotiation process.44 This not only helps focus on important interests that may
otherwise be ignored, but also helps to frame negotiations in a way that unites common
interests to achieve more fair outcomes.
DSD will need to be flexible and context-specific, yet enforceable
Finally, it is important to emphasize that DSD cannot be achieved in a strictly pro-forma or
prescriptive way but will often need to be approached in a voluntary, contextual and
participatory manner (in this sense, it is conceptually reminiscent of notions of
self-regulation). Such an approach of “productive ambiguity” aligns more closely to the
principles of self-determination and can help to ensure the successful adoption of DSD in
the long run. How DSD is implemented will depend on what data is being handled, the
stage of the data lifecycle that is being considered, who the actors are, and how interests
are being addressed. Each context will call for its own set of stakeholders, processes,
and systems. At the same time, because of the well-established weaknesses of
enforcement in self-regulatory contexts, special attention will need to be given to how to
enforce the negotiated conditions of DSD.
44 Ibid.
43 Tanya Alfredson and Azeta Cungu, Negotiation Theory and Practice: A Review of the Literature (Rome: FAO, 2008),
https://www.fao.org/3/bq863e/bq863e.pdf.
Operationalizing Digital Self Determination, S. Verhulst 12
III. Case-Study: Migrants
The concept of DSD can be productively explored through case studies. In this section,
we explore an example related to migrant populations, the challenges they face
concerning data, and how DSD can protect and help them flourish.45
Migrant populations today account for an estimated 3.6% of the world’s population, a
number that continues to grow as global crises increase.46 Already in 2022, the
COVID-19 pandemic, the war in Ukraine, and the floods in Pakistan have displaced
millions of people. As the number of migrants around the world increases, so too do the
number of technologies associated with their journeys. These tools generate and use
huge amounts of data, often without the express consent of the data subjects.47 Consider
the following examples:
●In 2013, at a refugee camp in Malawi, the UNHCR launched the Biometric Identity
Management System (BIMS). This system holds “body-based”
identifiers—including fingerprints, iris scans, and facial scans—to accredit refugees
and grant a service access to food rations, housing, and spending allowances.48
Additionally, UNHCR employs blockchain to link individuals with transaction data.
●The EUMigraTool uses data from migrants sourced from video content, web news,
and social media text content to generate modeling and forecasting tools to help
manage migrants’ arrival and support needs in a new country.49 Through its
algorithms, the tool can help predict migration flows and detect risks and tensions
related to migration, allowing migration service organizations to prepare for the
appropriate amount of human and material resources needed when responding to
a migration event.
49 “EUMigraTool,” IT Flows, 2022, https://www.itflows.eu/eumigratool/.
48 Biometric Identity Management System: Enhancing Registration and Data Management (Geneva: UN Refugee
Agency (UNHCR), 2015, https://www.unhcr.org/550c304c9.pdf.
47 Kenneth Neil Cukier and Victor Mayer-Schoenberger, “The Rise of Big Data: How It’s Changing the Way We Think
About the World,” Foreign Affairs, May/June 2013, https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data ; Jos
Berens et al., “The Humanitarian Data Ecosystem: the Case for Collective Responsibility,” Stanford Center on
Philanthropy and Civil Society (PACS) (2016),
https://pacscenter.stanford.edu/publication/the-humanitarian-data-ecosystem-the-case-for-collective-responsibility/.
46 World Migration Report 2020, eds. Marie McAuliffe and Binod Khadria (Geneva: International Organization for
Migration, 2020), https://publications.iom.int/system/files/pdf/wmr_2020.pdf.
45 Based upon the findings of a studio we conducted in 2021/2022 with the International Network on Digital Self
Determination and the Big Data for Migration Alliance. To learn please see this article.
Operationalizing Digital Self Determination, S. Verhulst 13
●X2AI, a mental healthcare app, developed ‘Karim,’ a Chatbot to provide virtual
psychotherapy50 to Syrians in the Zaatari refugee camp. The non-profit Refunite51
assists refugees in locating missing family members via mobile phone or
computer, and currently has over 1 million registered users. And Mazzoli et al.
(2020)52 demonstrate how geolocated Twitter data can help identify specific
routes taken, as well as areas of resettlement, by migrants during migrant crises.
These are just a few examples that illustrate how data is both generated by migrant
movements and also used to channel aid, resettle populations, and generally inform the
policy response. Without a doubt, there are many potential benefits to such usage. Much
of the generated data can be leveraged in the pursuit of evidence-based policy-making
to alleviate the sufferings and marginalization of this vulnerable population.
But as in virtually every other aspect of our digital era, data also poses a threat to migrant
populations, notably by potentially infringing upon their rights and creating new power
structures and inequalities.53 Migrants face power imbalances when it comes to agency
over their data, choice in how their data is used, and control over who has access to their
data.54 These asymmetries are often further exacerbated by a lack of digital literacy,
limiting migrants’ ability to use digital tools to achieve self-determination. For example,
with little to no agency over the use of their data, migrant populations are often exploited
as test subjects for new technologies, rather than benefiting from these rapid
developments.55 In addition, in many use cases, there exists a very weak framework for
how data is collected, stored, and generally used, leading to ample scope for abuses.
55 Petra Molnar, “New technologies in migration: human rights impacts,” the ETHICS issue, Forced Migration Review,
June 2019, https://www.fmreview.org/ethics/molnar ; Aaron Martin et al., “Digitisation and Sovereignty in Humanitarian
Space: Technologies, Territories and Tensions,” Geopolitics (2022), https://doi.org/10.1080/14650045.2022.2047468.
54 Stefaan Verhulst, Marine Ragnet and Uma Kalkar, “Digital Self-Determination as a Tool for Migrant Empowerment,”
Big Data for Migration (blog), May 26, 2022,
https://data4migration.org/articles/digital-self-determination-studio-digital-self-determination-as-a-tool-for-migrant-empo
werment/index.html.
53 Jessica Bither and Astrid Ziebarth, AI, Digital Identities, Biometrics, Blockchain: A Primer on the Use of Technology in
Migration Management (Washington, D.C.: The German Marshall Fund, 2020),
https://www.gmfus.org/news/ai-digital-identities-biometrics-blockchain-primer-use-technology-migration-management ;
The use of digitalisation and artificial intelligence in migration management: Joint EMN-OECD Inform (Brussels:
European Migration Network, 2022),
https://www.oecd.org/migration/mig/EMN-OECD-INFORM-FEB-2022-The-use-of-Digitalisation-and-AI-in-Migration-Mana
gement.pdf.
52 Mattia Mazzoli et al., “Migrant mobility data flows characterized with digital data,” PLoS ONE 15, no. 3 (2020),
https://doi.org/10.1371/journal.pone.0230264.
51 “Refunite.” Refunite,https://refunite.org/.
50 Nick Romeo, “The Chatbot Will See You Now,” Annals of Technology, The New Yorkers, December 25, 2016,
https://www.newyorker.com/tech/annals-of-technology/the-chatbot-will-see-you-now.
Operationalizing Digital Self Determination, S. Verhulst 14
DSD may offer some potential solutions to these growing asymmetries. Applied
responsibly, DSD can help address power and agency asymmetries between migrants
and various stakeholders by empowering migrants with the ability to control how their
data is collected, stored and used. It also creates avenues for negotiation, whereby
trusted intermediaries can advocate for migrants and for other stakeholders in shared
ecosystems. DSD’s focus on the “self” helps direct discussions and frameworks around
the unique experience of various migrant populations, thus making DSD more effective in
addressing the specific vulnerabilities and contextual factors facing different populations
today. DSD is also useful because it can help engage migrants in the process of data
generation, collection, use and reuse, thus opening avenues for their engagement in the
policy process and widening the range of insights brought to bear on the data policy
process.56
These are just some of the ways in which DSD can be useful in addressing a pressing
global socio-economic problem. In the next section, we examine how these insights can
be operationalized more generally, across sectors and domains.
IV. Operationalizing Digital Self-Determination
In order for DSD to have a social impact and help mitigate the asymmetries of our era, it
is critical for theory to be translated into practical implementation. This represents a
critical step in moving from concept to concrete policy implementation. As always within
the data ecology, the task is not simply to blindly apply the concepts explored above but
to understand how to do so responsibly—in a manner that maximizes agency, and
balances the potential benefits with the possible harms of any possible policy or
technical intervention.
Responsible implementation of DSD can be explored through a four-pronged framework:
processes, people and organizations, policies, and products and technologies.
56 Hannah Chafetz, Uma Kalkar, Marine Ragnet, Stefaan Verhulst and Andrew J. Zahuranec, “How Can We Ensure the
Digital Self-Determination of Migrants,” Big Data for Migration (blog), July 18, 2022,
https://data4migration.org/articles/digital-self-determination-studio-how-can-we-ensure-the-digital-self-determination-of
-migrants/index.html.
Operationalizing Digital Self Determination, S. Verhulst 15
Operationalizing Digital Self-Determination: a four-pronged framework
A) Processes: Exploring the role of Data Assemblies
Processes are essential to enable principled negotiation, ensuring transparency and
predictability, and conveying methods or approaches that can be used across concepts.
Some key processes to can be considered in the operationalization of DSD include
citizen data commons,57 citizen engagement programs,58 public deliberations, and
participatory impact assessments.59
One process holding particular potential involves the use of data assemblies, or citizen
assemblies or juries around the reuse of data. Data assemblies bring together
policymakers, data practitioners, and key members of communities to co-design the
conditions under which data can be reused, as well as various other associated issues.60
As an example, in 2020, the GovLab launched The Data Assembly initiative, a citizens
60 The Data Assembly, The GovLab, 2020, https://thedataassembly.org/.
59 Reema Patel et al., Participatory data stewardship: A framework for involving people in the use of data (London: The
Ada Lovelace Institute, 2021),
https://www.adalovelaceinstitute.org/wp-content/uploads/2021/11/ADA_Participatory-Data-Stewardship.pdf.
58 “Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation),” GIZ, 2021,
https://www.giz.de/en/worldwide/98533.html.
57 Liton Kamruzzaman, “Net zero precincts: Citizen data commons and technological sovereignty,” Monash University,
https://www.monash.edu/mada/research/net-zero-precincts.
Operationalizing Digital Self Determination, S. Verhulst 16
assembly based in New York City. Through this approach, participants were able to
understand how different stakeholders perceive the challenges and risks of data reuse,
as well as the diverse value propositions data reuse promises each actor. Among the key
lessons of this project was the finding that data assemblies not only create space for
public engagement but also offer avenues through which data practitioners can secure
responsibly informed consent from the public—an essential step in building a more
trusted and engaged data ecology.61
B) People and Organizations
People and organizations also play a key role in operationalizing DSD. Among other
functions, they are essential to building a culture of data agency and ensuring a
cross-silo commitment to easing asymmetries and ensuring responsible reuse. People
and organizations are in essence the building blocks of responsible data use and reuse.
To operationalize DSD, two critical functions or roles for individuals and groups need to
be highlighted:
i) Data Stewards
Individuals or groups of individuals occupying the emerging function of data stewards
within organizations play important roles in facilitating DSD and responsible data reuse. A
data steward is a leader or team “empowered to create public value by re-using their
organization’s data (and data expertise); identifying opportunities for productive
cross-sector collaboration and responding proactively to external requests for functional
access to data, insights or expertise.”62 Their roles and responsibilities include engaging
with and nurturing collaborations with internal and external stakeholders, promoting
responsible practices, implementing governance processes, and communicating insights
with broader audiences.63 Data stewards are key actors in enabling the
operationalization of DSD due to their ability to promote the adoption of processes and
practices that empower data subjects to effectively assert agency.
ii) Data Intermediaries
63 Stefaan G. Verhulst, “Data Stewardship Re-Imagined – Capacities and Competencies,” Data Stewards Network
(blog), October 8, 2021, https://link.medium.com/dJWWpcvQnub.
62 Stefaan G. Verhulst, Andrew J. Zahuranec, Andrew Young and Michelle Winowatan, (Re-)Defining the Roles and
Responsibilities of Data Stewards for an Age of Data Collaboration (Brooklyn: The Governance Lab, 2020),
http://www.thegovlab.org/static/files/publications/wanted-data-stewards.pdf.
61 Andrew Zahuranec, Andrew Young and Stefaan G. Verhulst, “How can stakeholder engagement and mini-publics
better inform the use of data for pandemic response?,” Participo (blog), February 19, 2021,
https://link.medium.com/Y9COUWwQnub.
Operationalizing Digital Self Determination, S. Verhulst 17
If data stewards facilitate responsible data reuse, then data intermediaries are emerging
as potential solutions to the challenges posed by unbalanced collective bargaining.
These individuals or teams mediate transactions between the supply and demand of
data. For example, they may help match a private sector organization that currently
stores large sets of (siloed) data with a non-profit organization that can apply that data
toward the public good. In the context of DSD, data intermediaries can help balance the
need for data subjects to maintain agency over their own data while at the same time
enabling a robust data-sharing ecosystem. Data intermediaries do pose certain
challenges, notably the risks of higher transaction costs and the creation of new power
asymmetries. These can be mitigated by regulatory frameworks that ensure that data
intermediaries remain neutral, fair, and secure.64
C) Policies
The third element needed to operationalize DSD relates to governance and policies. We
focus on three areas in particular: charters, social licenses, and codes of conduct.
i) Charter
DSD is, in essence, about balancing power and agency asymmetries. These imbalances
center around agency, choice, and participation. The creation of a charter (or statements
of intent) nuanced by the types of actors, data collection and data usage may provide a
potential policy-based solution to this imbalance. Past data charters, such as the
International Open Data Charter65 or the Inclusive Data Charter,66 could act as models for
a DSD charter. Such a charter would serve as a unifying step to define, scope, and
establish DSD for data actors. In order to be most effective, it is important that any
charter for DSD takes a life cycle, multi-stakeholder approach. Additionally, a charter
ought to be context specific and human-centered to ensure that the rights of the data
subject are protected.
ii) Social License
Social licenses are another policy tool that can help operationalize DSD. A social license,
or social license to operate, captures multiple stakeholders’ acceptance of standard
practices and procedures, across sectors and industries.67 Social licenses help facilitate
67 Will Kenton, “Social License to Operate (SLO),” Investopedia, May 31, 2021,
https://www.investopedia.com/terms/s/social-license-slo.asp.
66 “Inclusive Data Charter,” Global Partnership for Sustainable Development Data, 2018,
https://www.data4sdgs.org/initiatives/inclusive-data-charter.
65 “The International Open Data Charter,” Open Data Charter, https://opendatacharter.net/.
64 “Governing Data Intermediaries – The Data Governance Act: Principles, Frictions, and Perspectives,” Florence School
of Regulation (EUI), April 13, 2022,
https://fsr.eui.eu/governing-data-intermediaries-the-data-governance-act-principles-frictions-and-perspectives/.
Operationalizing Digital Self Determination, S. Verhulst 18
responsible data reuse by establishing standards of practice for the sector as a whole.
They also empower individual actors to exert more proactive control over their data,
which is critical to the adoption of DSD.
Broadly, there exist three approaches to secure social licenses for data reuse: public
engagement; data stewardship; and regulatory frameworks.68 In the context of DSD, data
stewards play an especially important role, given their position as facilitators of
responsible data reuse.
iii) Codes of Conduct
Codes of conduct are another policy element that can help operationalize DSD. A code
of conduct interprets a policy and lays the foundation for its implementation in a specific
sector.69 By bringing together diverse stakeholders, or “code owners,” a code of conduct
is able to account for the many different interests as well as technical and logistical
requirements at play in the ecosystem. Moreover, a code of conduct can lead to the
creation of a monitoring body, which is responsible for ensuring compliance, reviewing
and adapting procedures, and sanctioning members who break the code. The monitoring
body not only helps implement the code in a dynamic and effective manner, but can also
foster secure data sharing by acting as a third-party intermediary.
D) Products and Technological Tools
While the preceding elements are largely focused on human or human-initiated
processes, it is important to recognize that technology also plays an important role in
operationalizing DSD. Technology operates at many levels: at the layer of code,
infrastructure and hardware, and as products and product design that offer interfaces to
both consumers and organizations. User-led design experience,70 informed by the needs
of both consumers and larger beneficiaries of data, can help implement DSD principles in
practice by promoting digital access and action across stakeholders.71
One technological product that can play a significant role is a trusted data space. A data
space can be defined as an “organizational structure with technical and physical
71 Sandra Ponzanesi, “Migration and Mobility in a Digital Age: (Re)Mapping Connectivity and Belonging,” Television &
New Media 20, no. 6 (2019), https://doi.org/10.1177/1527476419857687.
70 Reema Patel et al., Participatory data stewardship: A framework for involving people in the use of data (London: The
Ada Lovelace Institute, 2021),
https://www.adalovelaceinstitute.org/wp-content/uploads/2021/11/ADA_Participatory-Data-Stewardship.pdf.
69 Mathias Vermeulen, “The Keys to the Kingdom,” Knight First Amendment Institute at Columbia University Law and
Political Economy Project essay series (2021), https://knightcolumbia.org/content/the-keys-to-the-kingdom.
68 Stefaan G. Verhulst and Sampriti Saxena, “The Need for New Methods to Establish the Social License for Data
Reuse,” Data & Policy Blog (blog), May 20, 2022, https://link.medium.com/uZ9IayFQnub.
Operationalizing Digital Self Determination, S. Verhulst 19
components that connects data users and data providers with sources of data.”72 A
trusted data space gives stakeholders a degree of control over this space and thus over
their data, while still encouraging sharing practices. This balance between agency and
the right to reuse is a step toward DSD, as it aims to protect and empower data subjects
without hampering open data.
V. Considerations and Reflections
In addition to outlining these four areas of operationalization, we wish to offer some
additional considerations and observations on the operationalization of DSD—both in its
current incipient state, and in the more fleshed-out version that may yet emerge.
A) Life Cycle Approach:
In order for that more fleshed-out, operational version to take shape, it is going to be
essential to identify opportunities for DSD that exist at each stage of the data life cycle.
The data life cycle follows data from its creation to its transformation into an asset across
five stages: collection, processing, sharing, analyzing, and using.73 Each stage of the
process could benefit from DSD. As we’ve seen above, the collection and the sharing
stages of the life cycle must overcome challenges posed by agency asymmetries, which
can be mitigated by the principle and practice of DSD. Similarly, during the processing,
analysis, and use of data, opportunities for DSD emerge in terms of the use (and reuse) of
data and the sharing of insights.
By taking a data life cycle approach to DSD, stakeholders will also be well-positioned to
account for the variety of asymmetries, both in terms of data and in terms of power, that
exist across different levels of the ecosystem. Starting from the level of the individual and
extending all the way to the national stage, each actor defines different notions of
self-determination to address unique asymmetries. The life cycle approach allows for
stakeholders to reflect on and respond to the varied asymmetries that exist at each stage
to help achieve a greater balance in power.
B) Symmetric Relationships
73 Andrew Young, Andrew Zahuranec and Stefaan Verhulst, “A Layered Approach to Documenting How the Third Wave
of Open Data Can Provide Societal Value,” Open Data Policy Lab (blog), August 21, 2021,
https://opendatapolicylab.org/articles/the-onion-model-a-layered-approach-to-documenting-how-the-third-wave-of-ope
n-data-can-provide-societal-value/index.html.
72 Federal Department of the Environment, Transport, Energy and Communications (DETEC) and the Federal
Department of Foreign Affairs (FDFA), Creating trustworthy data spaces based on digital self-determination: Report
from the DETEC and the FDFA to the Federal Council on 30 March 2022 (Bern: The Swiss Confederation, 2022): 16,
https://digitale-selbstbestimmung.swiss/home/en/245-2/.
Operationalizing Digital Self Determination, S. Verhulst 20
As we have seen, DSD offers many benefits. One of the most important is its role in
building symmetric relationships between stakeholders by re-balancing existing power
and agency asymmetries. Symmetric relationships are important ethically, but they also
have the potential for greater stability in the long term, and help prevent the exploitation
of weaker parties by stronger ones.74
In the context of a data ecosystem, symmetric relationships can help data subjects more
effectively leverage their self-determination to exert a greater degree of control over the
ways in which their data is used and reused. This is especially important to vulnerable
minorities, who may also be disempowered in other ways. Future systems ought to be
designed with these permanent minorities in mind within the broader context of human
rights, justice and democracy.75
C) DSD and Disintermediation
Finally, DSD can help limit the creation of new power asymmetries by preventing the
emergence of new chokepoints and loci of control in the form of new intermediaries. By
returning power and agency to individual stakeholders, the need for dominant
intermediaries is minimized. One important consequence is a lowering in the risk of new
power imbalances, which so often stem from the disproportionate power of
intermediaries.
Removing dominant intermediaries also simplifies the process of developing symmetric
relationships, which are easier to achieve without the role of middlemen. In this way,
disintermediation can play a vital role in increasing subject agency, and in empowering
data subjects to exert control over their own data while also promoting safe data sharing.
VI. Further Research and Action
Self Determination is a historical concept with an impressive intellectual and juridical
pedigree. It is imperative that this concept, like so many others, be updated to the digital
era. The notion of DSD we have outlined here is preliminary, more exploratory and
conjectural than finalized. It sets the foundations for a fuller exploration of DSD, as well as
the vital roles of agency, asymmetries, and other power dynamics within the data
ecology. Our hope is that this paper sets an agenda or framework for further action and
research into, and ultimately operationalization of, DSD across sectors and industries in
our era of rapid and unrelenting datafication.
75 Mahmood Mamdani, Neither Settler nor Native: The Making and Unmaking of Permanent Minorities (Cambridge:
Harvard University Press, 2020), https://www.hup.harvard.edu/catalog.php?isbn=9780674987326.
74 Frank R. Pfetsch, “Power in International Negotiations: Symmetry and Asymmetry,” Négotiations 16, no. 2 (2011):
39-56, https://doi.org/10.3917/neg.016.0039.
Operationalizing Digital Self Determination, S. Verhulst 21
In conclusion, we therefore offer some key questions and areas for further enquiry that
may help shape a DSD research agenda. A non-exhaustive list of questions could
include:
Conceptual and Operational
●How does DSD differ or align across sectors, geographies, communities,
and contexts?
●What can be learned from other (self-)governance practices in the further
development and enforcement of DSD?
●What are the conditions and drivers that can enable a principled
implementation of DSD?
Processes
●What design principles should inform the creation and implementation of
“data assemblies” or other deliberative processes?
●What can be learned from recent deliberative democracy practices and/or
innovations in collective bargaining processes to operationalize DSD?
Policies
●How are the components of a possible charter or statement of intent
regarding DSD? And who should be involved in drafting these?
●How to deepen and operationalize the concept of “social license” across
contexts and sectors?
●What can be learned from existing code-of-conducts in the development of
a DSD code of conduct?
People and Organizations
●How to train “data stewards” who have a responsibility to define and
comply with DSD conditions?
●What is the role of existing institutions and intermediaries (such as unions,
community organizations and others) in representing vulnerable groups
when DSD is negotiated or determined?
●How to ensure that new disintermediaries do not become new
choke-points?
Products and tools
●What guidelines should be in place to steer transnational trustworthy data
spaces that can provide legal certainty and accountability?
●How can products and tools be used to bridge gaps in digital literacy to
empower DSD?
Operationalizing Digital Self Determination, S. Verhulst 22
Competing interests: the authors declare none
Data availability: Not applicable to this paper
Funding statement: The GovLab received support from the Swiss Federal Government
to hold some studios on migration and Digital Self determination; and I spend a month at
the Rockefeller Foundation's Bellagio Center where the current draft was written
Operationalizing Digital Self Determination, S. Verhulst 23
