Skip to content

Participation

'Participation' illustration by Johnny Lighthands, Creative Commons Attribution-ShareAlike 4.0 International. ‘Participation' illustration by Johnny Lighthands, Creative Commons Attribution-ShareAlike 4.0 International.

The pillar of participation promotes the democratisation of data scientific research and data innovation practices. It does so by highlighting the need to involve members of impacted communities, policymakers, practitioners, and developers to collaboratively articulate shared visions for the direction that data innovation agendas should take.

The examination of participation in this framework considers four main dimensions: democratising data and data work, understanding data and data subjects relationally, challenging domination-preserving modes of participation, and ensuring transformational inclusiveness. The following sections explore each of these elements in detail, offering a framework for investigating concerns of participation in the context of data and data-intensive technologies.

For a brief overview of the participation pillar, take a look at the infographic video below.

Democratise data and data work

Prioritise meaningful and representative stakeholder participation, engagement, and involvement from the earliest stages of the data innovation lifecycle to ensure social licence, public consent, and justified public trust. The democratisation of data scientific research and data innovation practices involves bringing members of impacted communities, policymakers, practitioners, and developers together to collaboratively articulate shared visions for the direction that data innovation agendas should take. This entails the collective and democratically based determination of what acceptable and unacceptable uses of data research and innovation are, how data research and innovation should be governed, and how to integrate the priorities of social justice, non-discrimination, and equality into practices of data collection, processing, and use.

Illustrative example: May First Technology Movement, United States and Mexico

May First Technology is a non-profit movement organisation founded in 2005 that shares its technology as a non-profit service provider so it can be used strategically and collectively by organisations and activists in the United States and Mexico working for local struggles and global transformation. It has 850 members who own over 120 servers maintained by May First Technology and have unlimited access to its internet services.

Members also participate in networks, coalitions, and campaigns around topics like net neutrality, data protection, and alternative connection systems. Since 2017, the May First Technology movement has brought together over 1,500 activists in the United States and Mexico through their “Technology and Revolution” series. This has seen participants discuss the ways in which technology can intersect with activism and with revolution. Underlying their work is the conviction that the use, protection, and democratisation of technology is a key element for fundamental change.

Understand data and data subjects relationally

Data collection and use should not be pursued in a way that reifies, objectifies, or commodifies data or data subjects. Where data innovation practices focus only on an individual’s relationship to data (as a possession or form of property) or on an individual’s privacy or data protection rights, these practices lose sight of the wider contexts of their social effects, their population- level impacts, and the interconnectedness of the people and communities who are affected by data innovation ecosystems. A relational view of data practices1, which starts from this broader vantage point, recasts them as involving horizontal and interwoven social relationships in addition to vertical relationships between the individual data subject and the data collector, processor, or user. Understanding data and data subjects relationally entails recognising that data practices need to be situated in their social environments and governed democratically through horizontal, participation-based forms of collective action that provide coverage of a complex and multi-stakeholder ecology of interests, rights, obligations, and responsibilities.

Illustrative example: Sursiendo, Mexico

Sursiendo is a collective of activists working with regional organisations in Southeast Mexico to defend digital communality, collective digital rights, and hackfeminism—a concept used to incorporate intersectionality in the design, development, and use of technology so that designer and activists can ‘open the systems, hack the patriarchy.’ Placing gender and equitable participation at the core of their endeavours, Sursiendo utilises the avenues of activism, communication and design, free software, popular education, art, and cultural management to contribute to their vision.

The organisation has produced extensive literature at the nexus of gender and technology and has published tools for advocacy and the protection of rights. In 2020, they released a tool titled “Herramienta del Registro Incidentes De Seguridad Digital” (translated to “Logging Digital Security Incidents as a Risk Mitigation Practice “). Divided into two parts—a tool for recording digital security incidents and a guide for registration with human rights agencies—the resource is seen to be invaluable for collective action against risks in the digital sphere. The records of cybersecurity incidents (such as malware, phishing, Denial of Service, to name a few) include details on the vulnerabilities of the device being used and the capacities for the organisation or individual to intervene and mitigate the incident. These records are believed to be useful for complaint registration which can serve to prevent future incidents. Moreover, Sursiendo notes that the tool is an important mechanism for reflection, while also assisting in the identification of gaps and opportunities relevant to advocacy and capacity-building for organisations2.

Challenge existing, domination-preserving modes of participation

Where current justifications and dynamics of data practices reinforce or institutionalise prevailing power structures and hierarchies, the choice to participate in such practices can be counterproductive or even harmful. When options for a community’s participation in data innovation ecosystems and their governance operate to normalise or support existing power imbalances and the unjust data practices that could follow from them, these options for involvement should be approached critically. A critical refusal to participate is a form of critical participation3 4 5 6 and should remain a practical alternative where extant modes of participation normalise harmful data practices and the exploitation of vulnerability.

Illustrative example: Feminist Data Manifest-No

The Feminist Data Manifest-No provides a clear depiction of both gaps and harmful practices in the existing data landscape. This document intersects with notions of data feminism as they relate to advancing data justice research and practice. The Manifest-No is defined as ‘a declaration of refusal and commitment...it refuses harmful data regimes and commits to new data futures’6. The Manifest-No sets out to refute harmful data practices, while calling for a new future in which Latinx, Black, queer, trans- and Ingenious feminists are both celebrated and listened to. Based in critical refusal, the authors of the Manifest-No claim that refusal ‘can help different feminisms recognise interlocking struggles across domains, across contexts and cultures, and that enables us to work in solidarity to prop up and build resilience with one another—to generate mutually reinforcing refusals’6. Therefore, the series of refusals and commitments set out in the Feminist Manifest- No acknowledge the importance of both shared refusal and that ‘systemic patterns of violence and exploitation produce differential vulnerabilities for communities’6. Within the document, there are also commitments to mobilise data by working ‘with minoritised people in ways that are consensual, reciprocal, and that understand data as always co-constituted’6. These practices engage in critical refusal as participation, combat discriminatory and racialised politics of data collection and use, question binaries, and critique existing forms of power.

Ensure transformational inclusiveness rather than power-preserving inclusion

Incorporating the priority of inclusion into sociotechnical processes of data innovation can be detrimental where existing power hierarchies are sustained or left unaddressed. Where mechanisms of inclusion normalise or support existing power imbalances in ways that could perpetuate data injustices and fortify unequal relationships, these should be critically avoided. Transformational inclusiveness demands participatory parity so that the terms of engagement, modes of involvement, and communicative relationships between the includers and the included are equitable, symmetrical, egalitarian, and reciprocal.

Illustrative example: Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, Australia

The Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective was formed in 2017 in response to the isolation of Indigenous Australians from the language, control, and production of data, as well as the neglection of their knowledge, worldviews, and needs. The Collective seeks to progress Indigenous Data Sovereignty and Indigenous Data Governance through the development of data sovereignty principles, data governance protocols, and the identification of strategic data assets.

The Maiam nayri Wingara Data Sovereignty Collective and Australian Indigenous Governance Institute created a Communique as a result of the 2018 Indigenous Data Sovereignty Summit. The Communique aims to advance Indigenous Data Sovereignty through the initiation of Indigenous data governance protocols. The Communique claims that Indigenous communities ‘maintain the right to not participate in data processes inconsistent with the principles asserted in this Communique’. Actions taken by the collective are founded on the understanding that the exercise of Indigenous data governance will enable an accurate and informed picture of the realities, needs, and aspirations of Indigenous people.

  1. Viljoen, S. (2021). A relational theory of data governance. The Yale Law Journal, 131(2), 573-654. https://www.yalelawjournal.org/pdf/131.2_Viljoen_1n12myx5.pdf ↩

  2. Sursiendo. (2020, October 20). Registrando Incidentes de Seguridad Digital como Práctica de Mitigación del Riesgo. Sursiendo.https://sursiendo.org/2020/10/registro-y-analisis-de-incidentes-de-seguridad-digital/ ↩

  3. See Ahmed, S. (2012). On being included. Duke University Press and Ahmed, S. (2018, June 28). Refusal, resignation, and complaint. Feministkilljoys. https://feministkilljoys.com/2018/06/28/refusal-resignation-and-complaint/ ↩

  4. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Books. ↩

  5. Cifor, M., Garcia, P., Cowan, T.L., Rault, J., Sutherland, T., Chan, A., Rode, J., Hoffmann, A.L., Salehi, N., Nakamura, L. (2019). Feminist data manifest-no. https://www.manifestno.com/ ↩

  6. Cifor, M., Garcia, P., Cowan, T.L., Rault, J., Sutherland, T., Chan, A., Rode, J., Hoffmann, A.L., Salehi, N., Nakamura, L. (2019). Feminist data manifest-no. https://www.manifestno.com/ ↩↩↩↩↩