Data Sovereignty

I spent a week (24-26 May 2023) in Stat 678, Introduction to Statistical Consulting, having students think and write about data sovereignty as it relates to their Statistics masters option degree programs in Statistics and Data Science or Applied Statistics.

Assignment

This unit concerns Indigenous data sovereignty as it relates to statistics and data science collaboration. The goal is for you to be able to explain what Indigenous data sovereignty is, and to articulate (in a 1-2 page report) how data sovereignty may affect how you approach statistics consulting and collaboration. Consider the discussion and questions below, and examine the references, resources and videos found at the bottom to inform your report.

Can you tell which report was written by graduate students in the MS Statistics Option Applied Statistics or the MS Statistics Option Statistics and Data Science degree program?

Data Sovereignty

An important broad concept is “open data”, which might seem to capture data sovereignty but actually falls far short. Open data is codified in the FAIR principles–data should be Findable, Accessible, Interoperable and Reusable. However this ignores important principles that affect marginalized groups, including Indigenous peoples. Recently GIDA developed the CARE principles–Collective benefit, Authority to control, Responsibility and Ethics. How do these enrich our thinking of ethical statistics collaboration?

Below are quotes from three of the references, which capture some flavor of the challenges of open data and data sovereignty. What are the key issues that you might encounter during your career in this regard?

“Open data is a site of tension for Indigenous peoples. Open data provides opportunities for sustainable development according to Indigenous aspirations, yet also sits at the nexus of current and historic data challenges as a result of colonisation, bias, and a lack of knowledge of Indigenous rights. Indigenous data sovereignty (IDS) provides a framework for maximising the benefit of open data for Indigenous peoples and other users of Indigenous data and for affecting the stewardship of all data. Open data communities often assume many binaries, including a single government actor (nation-states), that data is open or not, and that open data is useful data (devoid of biases and relevance issues). In the context of Indigenous peoples, there are clear challenges for the mainstream open data movement around these binaries, as well as paths forward to assure the protection of Indigenous rights and data for development.” [Raine et al. 2019: Indigenous Data Sovereignty]

“Indigenous sciences are foundationally based in relationships, reciprocity, and responsibilities. These sciences constitute systems of knowledge developed through distinct perspectives on and practices of knowledge creation and decision-making that not only have the right to be pursued on their own terms but may also be vital in solving critical twenty-first-century challenges. “Science” is often treated as if it were a single entity, free of cultural influences and value-neutral in principle. Western science is often seen as instantiating and equivalent to this idealized, yet problematic, view of science. We argue for engagement with multiple perspectives on science in general, and increased engagement with Indigenous sciences in particular. As scholars focused on human learning and development, we share empirical examples of how Indigenous sciences, sometimes in partnership with Western science, have led to new discoveries and insights into human learning and development…. Native stem students … [pursue] themes of giving back to the community and of education as a process of transformation…. They strive to acquire knowledge and tools generated from the sciences as a way to contribute to community needs and goals, based on principles of relationality, reciprocity, and responsibility commonly found in Indigenous knowledge systems.” [Bang et al. 2018: Daedelus]

“Research computing infrastructure—one component of what the National Science Foundation (NSF) terms cyberinfrastructure (CI)—has led the world in transformational ways, but with considerable gaps in services for software and data capabilities. Moreover, the composition of the people supporting and utilizing the cyberinfrastructure does not sufficiently represent the diversity of society. The scale of the challenge is reflected in what the NSF leadership and National Science Board (NSB) terms the “missing millions”—those who are yet to be engaged for the science, technology, engineering, and mathematics (STEM) workforce so that it reflects the racial, ethic, and gender representation in the general population. Broadening the accessibility of CI investments to reach the missing millions promises tremendous gains for the national research enterprise and its impacts on society.” [Blatecky et al. 2021: The Missing Millions]

References

Resources

Videos

Data Sovereignty Report 1

What is Indigenous Data Sovereignty?

Indigenous Data Sovereignty (IDS) has emerged as a critical concept that advocates for the rights of Indigenous peoples to control their data. This includes not only data about individuals but also data about their communities, lands, and resources. IDS acknowledges the importance of respecting Indigenous cultures, traditions, and ways of knowing, and recognizes that Indigenous peoples have a unique relationship with their data that is rooted in their history, language, and worldview.

Indigenous and Western Sciences

The main point of this paper [1] is to explore the relationship between Indigenous sciences and Western sciences, and whether there is potential for mutual understanding and collaboration between the two. The authors argue for engagement with multiple perspectives on science and the value of Indigenous sciences in particular. They provide empirical examples of how Indigenous sciences, sometimes in partnership with Western science, have led to new discoveries and insights into human learning and development, as well as environmental sustainability. Additionally, the paper highlights the importance of recognizing and valuing different cultural orientations and knowledge systems, and the need to move away from the myth of a value-neutral, cultureless Western science. Thus, the paper advocates for a heterogeneity of sciences that values multiple systems of knowing and engages with methodologies developed within different cultural communities.

What’s special about indigenous science?

Indigenous science is significant because it builds knowledge about the world through a unique set of orienting values, concepts, and questions, anchored in relational epistemologies. The principles of Indigenous sciences, such as the notion that everything is related and mutually interconnected, influence how humans live and the responsibilities they have to nature, including plants, animals, and the land. An ecological premise grounds the questions and methods of most Indigenous sciences, fulfilling ethical responsibilities that contribute to the larger collective good. As we face the challenges of climate change, we need to reimagine interdependent relationships with and responsibilities to nature, and Indigenous sciences can expand our collective knowledge and help us meet these challenges. Furthermore, the engagement with Indigenous sciences requires recognizing, cultivating, and supporting Indigenous peoples and their efforts To create thriving communities, and transforming the institutional structures that have consistently undermined Indigenous ways of life. Therefore, it is critical to form new ethical partnerships with Indigenous peoples, prioritizing Indigenous self-determination and leadership.

What’s the major difference between Western and Indigenous sciences?

There are fundamental cultural differences between Indigenous and Western sciences. Indigenous sciences prioritize relational epistemologies, which value the interconnectedness and interdependence of all living beings and ecosystems. In contrast, Western science historically emphasizes objectivity and detachment, seeking to explain the universe through the lens of empirical evidence and logic. While some of the dimensions of Indigenous sciences may overlap with those in Western sciences, such as observation and explanation, there are significant differences in the guiding principles, questions, and methods of knowledge building. Indigenous sciences are also often driven by a set of values, including reciprocity, responsibility, and sustainability, which are different from those typically foregrounded in Western science.

However, open data communities present challenges for Indigenous peoples. For example, there may be biases and inaccuracies in data collection, which can further marginalised Indigenous communities. The CARE and FAIR principles developed by GIDA provide a framework for ensuring that open data benefits Indigenous peoples and respects their rights.

FAIR and CARE Principles

Open data is data that anyone can freely use, share, and build on. The FAIR principles are guidelines to make data more Findable, Accessible, Interoperable, and Reusable for humans and computers. They emphasize that data should be machine-actionable, meaning that computers should be able to find, access, interoperate, and reuse data with little or no human intervention.

However, the FAIR principles do not fully engage with Indigenous Peoples rights and interests in data. They do not address issues such as collective benefit, authority to control, responsibility, and ethics that affect marginalized groups. The CARE principles, developed by the Global Indigenous Data Alliance (GIDA), are designed to guide the inclusion of Indigenous Peoples in data governance across contemporary data ecosystems. They bring a people-and-purpose orientation to data governance, which complements the data-centric nature of the FAIR principles.

The CARE principles enrich our thinking of ethical statistics collaboration by highlighting the importance of respecting and protecting Indigenous Peoples’ sovereignty, agency, and dignity in data. They also encourage us to consider how data can be used for collective benefit and social justice, rather than just for scientific or commercial purposes. They challenge us to think beyond technical aspects of data quality and interoperability, and to engage with ethical and relational aspects of data governance. They invite us to collaborate with Indigenous Peoples as equal partners in data initiatives that affect them. Indigenous people want to have greater control over their data and knowledge being spread. Through the CARE principles, Indigenous peoples have the right to self-determination, possess, use, consent, refuse, and reclaim their data for governance. As well as, they have the right to govern, define, privacy, know, associate, and benefit from their data. The CARE principles reflect an important change in hopes of advancing Indigenous people’s information and promoting ethical data sharing practices.

The CARE principles stand for collective benefit, authority to control, responsibility, and ethics. They focus mostly on Indigenous peoples’ data governance and emphasise the importance of respecting their rights to control their data. To elaborate more on the CARE principles, here are some examples of how they can be applied in practice:

  • Collective benefit: Data collectors and users should ensure that Indigenous Peoples have access to their own data and can use it for their own purposes, such as improving health outcomes, preserving cultural heritage, or advancing self-determination. Data collectors and users should also seek consent from Indigenous Peoples before sharing or reusing their data for other purposes, and ensure that they share the benefits of such activities with them.
  • Authority to control: Data collectors and users should recognize and respect the rights of Indigenous Peoples to govern their own data according to their own laws, values, and protocols. Data collectors and users should also consult with Indigenous Peoples on how their data should be stored, managed, accessed, and disposed of.
  • Responsibility: Data collectors and users should adhere to ethical standards and best practices when working with Indigenous data, such as ensuring data quality, security, privacy, and confidentiality. Data collectors and users should also monitor and evaluate the impacts of their data activities on Indigenous Peoples and their environments, and report any harms or risks to them.
  • Ethics: Data collectors and users should respect the cultural diversity and integrity of Indigenous Peoples and their knowledge systems when working with their data. Data collectors and users should also acknowledge the sources and origins of Indigenous data and knowledge, and give proper attribution and recognition to them.

The FAIR and CARE Principles enrich our thinking of ethical statistics collaboration by ensuring that people are working in an ethical and respectful manner while sharing data. The CARE principles can guide researchers to work with Indigenous people and collaborate to provide the public with correct information. This will ensure respecting the Indigenous peoples’ culture in an ethical and responsible way that protects the Indigenous communities. By following these principles, data collectors and users can foster more ethical statistics collaboration with Indigenous Peoples that is based on mutual trust, respect, reciprocity, and benefit. This can also lead to more accurate, relevant, and useful data that can support both scientific advancement and Indigenous wellbeing.

By respecting the rights of Indigenous peoples, statisticians can work collaboratively with these communities to ensure that their data is used in a way that benefits them and does not perpetuate existing power imbalances.

Summary

The key issues that we might encounter during our career with regard to open data and data sovereignty are:

  • A careful balance between the advantages of open data and the protection of indigenous rights is necessary. When accessing the data,We should protect the rights and sentiments of other individuals.
  • Recognizing the complexity of data is essential, and making binary assumptions should be avoided. It is our duty and responsibility to promote IDS(Indigenous Data Sovereignty) to ensure open data benefits and respect their community.
  • Having a variety of people involved in research computing infrastructure is important. However, if the people building and using it don’t come from different backgrounds and communities, it won’t reflect the real world.We need to promote diversity in the community and encourage greater variety of people in STEM development.
  • Acknowledging and collaborating with Indigenous sciences alongside western science is crucial for promoting mutual learning and development.We can create a fairer and more robust system for data that benefits everyone involved by embracing Indigenous data sovereignty principles, considering various viewpoints on science, and encouraging inclusivity and accessibility in research computing infrastructure.

References

  1. Bang, Megan, et al. “If Indigenous Peoples Stand with the Sciences, Will Scientists Stand with Us?” Daedalus , vol. 147, no. 2, 2018, pp. 148–59. JSTOR , https://www.jstor.org/stable/48563027. Accessed 27 Apr. 2023.

Data Sovereignty Report 2

Understanding Indigenous Data Sovereignty & its Implications

Indigenous sovereignty is a right for the indigenous munitiescom to have independence in organizing, ruling, and governing their communities and land. So, data sovereignty is that concept used in how the data is generated and collected and is subject to the rules of the country in which it is located, as well as their ownership, control, and uses. Indigenous data sovereignty brings ot surface the tension between the FAIR (findabale, accessible, eroperable int and reusable) principles for adat sovereignty and the rights of Indigenous people who want to assert control over het access and usage of Indigenous data and Indgenous knowledge.

Another essential point to consider is the differences in data and knowledge generation. Western science has particular agreements and a language associated with protocols to collect data and a belief free of cultural influence or value. In Indigenous science, all community actors may apply and generate data in their roles and relationships, and the observations are based on cultural utilit y and necessity. Then, the uses and repercussions of the data and developing knowledge for het whole community have a high value and sense.

From a consulter perspective, working and collaborating with communities with a particular sense or sensibility is a challenge and a responsibility hatt implies seeing others in their particularities, necessities, and differences. In addition, the data treatment and findings must be under Indigenous sovereignty and follow their rules, which implies involvement, commitment, and care.

We could connect this idea of indigineous data sovereignty to minority groups across the world, who may have different ideas and views on data sovereignty. I think one of the questions that came up for em is who has het rights over the data, and who gets to decide what portions of data are “protected”. Political representation is an important idea in political science and I can imagine this concept translates readily to the issue at hand - are the members who decide where the boundaries of the protected use and access lie are actually good surrogates for the community?

This is a question we face in my own work. One of our group members studies the behavior of rural litigants, who are a lesser privileged category. There is also a privacy issue, given that they will be using and accessing “private” case-level data on litigation across the universe of cases filed in Indian courts. While Government deemed this data to be “public data” - however, the actual litigants involved in the cases did not consent for their data to be publicly available. If this is indeed an issue of data sovereignty, the stakeholder that “represents” the interest of the community cannot be readily identified - if we assume that the elected government is the appropriate representative, then the use of this data in any way is justified. If it is not the government, then who might be the appropriate party? We can conclude from this that this is a delicate topic, and it is important to consider how this data is accessed, used and represented. One actionable idea is to ensure that this work is useful, accessible and does not cause harm, and this can be gleaned from interactions with community members.

Equal Access and Collaboration

Indigenous data sovereignty refers to preserving indigenous people’s value in the collection, use, management, and dissemination of data that is relevant to them.

A premise of indigenous data sovereignty is equal access to the quality education and training for indigenous communities. Otherwise, community members may lack the skills and knowledge needed to collect, analyze, and interpret data in ways that are consistent with their cultural protocols and values. In higher education, the concept “education desert” was introduced to describe a local area where there are either zero or only one public broad-access colleges nearby, which would create significant challenges for students from historically marginalized communities. Research has found that compared with white Americans, Native American adults are more than five times more likely to live in an education desert. In this sense, education desert not only reduces indigenous people’s opportunities for higher education or vocational training, but also limits their ability to collect and analyze data in ways that are meaningful and relevant to their communities. Thus, to support indigenous data sovereignty, it is important to invest in education and training programs that are culturally responsive and relevant to the needs of indigenous communities.

When collaborating/involving with other communities, standards in indigenous data sovereignty help to create a more inclusive environment. In my experience, when developing a survey or measurement tool, representative review board members are required to identify any sensitive/inappropriate questions or materials. And when analyzing the data, the interest of indigenous communities should be respected by being aware of how the research question or even the interpretations might conflict with their culture and values.

What should we do as graduate students?

As a graduate student in statistics, these seem to be somethings we should consider from now on when conducting a statistical study.

First, we should try to actively collaborate with Indigenous communities and researchers. Collaborating with Indigenous communities and researchers who have experience and knowledge of Indigenous science can help us to better understand and incorporate Indigenous knowledge and practices into your statistical research. I personally think this is a very important point as a student who study social sciences.

Second, we should consider indigenous perspectives, knowledge, and practices when designing statistical studies. This could start with acknowledging the limitations of Western scientific methods. I have not really thought of other methodological perspectives before, but understanding complex ecological and social systems might is often difficult just with the Western scientific methods. Incorporating Indigenous science can provide a more holistic and culturally responsive approach to understanding these systems.

Third, it seems important to use culturally appropriate methods for data collection and analysis that respect Indigenous knowledge and ways of knowing. This may involve adapting statistical methods to incorporate Indigenous knowledge or using traditional Indigenous methods for data collection. Especially given the fact that exposure to the recruitment and sampling differs a lot between indigenous and non-indigenous communities, relying too heavily on a traditional Western way of data recruitment and collection, such as online platform, can result in significantly biased samples. Yet, we feel like we need more studies on what type of methods indigenous communities use for data collection and analyses.

Written on June 29, 2023