• Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • Core Principles of Responsible AI
    • Accountability
    • Fairness
    • Privacy
    • Safety and Security
    • Sustainability
    • Transparency
  • Special Topics
    • AI in Industry
    • Ethical Implications
    • Human-Centered Design
    • Regulatory Landscape
    • Technical Methods
  • Living Dictionary
  • State of AI Ethics
  • AI Ethics Brief
  • 🇫🇷
Montreal AI Ethics Institute

Montreal AI Ethics Institute

Democratizing AI ethics literacy

In Consideration of Indigenous Data Sovereignty: Data Mining as a Colonial Practice

January 23, 2024

🔬 Research Summary by Jennafer Shae Roberts, a social science researcher with Accel AI Institute specializing in AI and ethics from a decolonial perspective.

[Original paper by Jennafer Shae Roberts and Laura Montoya]


Overview: This research highlights the troubling parallels between data mining practices and colonialism, shedding light on the need to include Indigenous perspectives in data-driven domains like artificial intelligence (AI). It underscores the urgency of embracing Indigenous Data Sovereignty, emphasizing the pivotal role that Indigenous communities could play in asserting their rights and control over their data, charting a path towards more equitable and inclusive technological advancements. The core issue we’re exploring is the lack of inclusion of Indigenous voices in the development of data-dependent technologies, like AI, which perpetuates new forms of colonialism.


Introduction

“Indigenous Peoples have always been ‘data warriors’. Our ancient traditions recorded and protected information and knowledge through art, carving, song, chants and other practices.”

(Kukutai, 2020)

This quote highlights a different perspective on data, which is generally considered a ‘free’ resource. This research explores the reframing of rights related to data and aims to illustrate how data mining is akin to a neo-colonial practice. 

Our solution was to apply the CARE principles for Indigenous Data Governance, which were developed by The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance). The CARE principles include:

  • Collective Benefit
  • Authority to Control
  • Responsibility
  • Ethics

Methods

Our literature review found that much of the research on a decolonial approach to data and AI failed to mention the CARE Principles or Indigenous Data Sovereignty. Additionally, the literature on the CARE Principles was missing a strong critique of data mining as a colonial practice. 

Our research uniquely bridges a gap by establishing a vital connection between the CARE Principles and colonial data mining. This contribution highlights the principles’ significance in securing equitable data rights, safeguarding Indigenous peoples’ rights, preventing the perpetuation of colonial practices, and amplifying marginalized voices.

Key Insights

Introduction to Indigenous Data Sovereignty

Indigenous Data Sovereignty (ID-SOV) is a concept that emerged in 2016. It encompasses the right of Indigenous Peoples to own, control, access, and possess data related to their members, knowledge systems, customs, or territories. This includes cultural knowledge, heritage, and personal information. (Kukutai, 2020)

Understanding what it means to be Indigenous in the Digital Age

While working to decolonize, we must stress that the term ‘Indigenous’ was a separation created by colonists used to determine who was human and who was less than human. (Scott, 2009) (Roberts & Montory, 2023) The fact that we still function from this foundation is inherently detrimental. A common element of Indigenous Peoples is a strong desire to maintain autonomy while also resisting marginalization and discrimination. (Chung & Chung, 2019) That is why the CARE principles are so important to listen to and follow Indigenous rights around data.  

The CARE Principles of Indigenous Data Governance

Our research is about how the CARE Principles of Indigenous Data Governance can be used to address unethical data mining issues. The CARE Principles are centered around people and purpose, emphasizing the role of data in driving innovation, governance, and self-determination among Indigenous Peoples. (Carroll et al. 2020) 

While the FAIR principles (Findable, Accessible, Interoperable, Reusable) primarily focus on data itself and overlook the ethical and socially responsible aspects of data usage, such as power imbalances and historical contexts related to data acquisition and utilization (Wilkinson et al., 2016) the CARE principles prioritize the welfare of Indigenous Peoples and their data. They can be integrated alongside the FAIR Principles across the entire data lifecycle to ensure mutual advantages and address these broader ethical considerations. (RDA, 2020 P57)

CARE Principles:

  • Collective Benefit:  Data ecosystems should enable Indigenous Peoples to benefit from their data
  • Authority to Control: Recognize Indigenous Peoples’ rights in data and empower their authority to control it
  • Responsibility: Those handling Indigenous data must share how it supports Indigenous Peoples’ self-determination and collective benefit.
  • Ethics: Indigenous Peoples’ rights and well-being should be the primary concern throughout the data life cycle and across the data ecosystem. (Carroll et al. 2020) 

Integrating CARE principles and Indigenous Data Sovereignty into global data governance can shift us away from harmful colonial data mining, promoting a more balanced relationship with data and advancing the goal of decolonizing data.

Case Study Example

In the paper, we review several case studies globally to exemplify how the CARE principles could be beneficial in particular situations of data collection. One was the story of a European NGO that undertook a mission in Burundi to gather data on water accessibility (Abebe et al., 2021) However, in their efforts, the NGO encountered significant challenges. First, they failed to fully grasp the community’s perspective on the core issues, missing crucial insights. Secondly, they did not anticipate the potential harm their actions could inflict.

By making the collected data publicly available, including specific geographic locations, the NGO unwittingly exposed the local community to risks. This breach of privacy, especially collective privacy, resulted in a substantial loss of trust.

In this case, the violation of the CARE principles, specifically in terms of Collective Benefit and Responsibility, underscores the importance of recognizing Indigenous data rights and considering the broader impact of data-sharing practices.

Is Data Sharing Beneficial?

Balancing transparency and personal security is a challenge for Indigenous communities, as data sharing can have both positive and negative impacts. Responsible utilization includes addressing issues of marginalization, colonialism, discrimination, and power imbalances in government-led negotiations. For it to be beneficial, data producers must gain value from exchanging and using their data. 

Conclusion

Indigenous Data Sovereignty is a critical concept in the quest to decolonize data. It recognizes the rights of Indigenous Peoples to control and own their data and challenges historical frameworks. As data becomes increasingly vital in the digital age, we must respect the autonomy and aspirations of Indigenous Peoples and ensure that data is collected and used responsibly, focusing on ethical and social aspects through the CARE Principles. This approach respects Indigenous rights and contributes to more meaningful, equitable, and responsible data utilization.

Between the lines

The bottom line is that we must shift how we see data as a resource and understand that open data does not benefit everyone. Rather, it perpetuates systems of corporate power. By looking at data mining as a colonial practice, we found that the solution was to look towards the rights of self-determination put forth by Indigenous groups. We wanted to stress the CARE principles as a way to address data mining issues and ensure that collective benefit, authority to control, responsibility, and ethics can guide data practices for better relationships with data and the people behind the data. This research centers on Indigenous peoples in the context of colonization and neo-colonization, recognizing their pivotal role in resisting exploitative and marginalizing practices. While the focus is on Indigenous communities, the overarching goal is to benefit everyone impacted by the collection and use of data, with the aspiration to bring about positive change in the current paradigm of data utilization.

References

Abebe, Rediet, Kehinde Aruleba, Abeba Birhane, Sara Kingsley, George Obaido, Sekou L. Remy, and Swathi Sadagopan. “Narratives and Counternarratives on Data Sharing in Africa.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 329–41. Virtual Event Canada: ACM, 2021. https://doi.org/10.1145/3442188.3445897.

Carroll, Stephanie Russo, Ibrahim Garba, Oscar L. Figueroa-Rodríguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, et al. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19 (November 2020). https://doi.org/10.5334/dsj-2020-043. 

Chung, Pyrou, and Mia Chung. “INDIGENOUS DATA SOVEREIGNTY IN THE MEKONG REGION.” 2019 WORLD BANK CONFERENCE ON LAND AND POVERTY”, March 2019. https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5d73f381116f8f338459d0f0/1567880 065875/IDSov+in+the+Mekong+Region.pdf. 

Kukutai, Tahu, and John Taylor. Indigenous Data Sovereignty. Edited by Tahu Kukutai and John Taylor. ANU Press, 2016. https://doi.org/10.22459/CAEPR38.11.2016.

RDA COVID-19 Indigenous Data WG. “Data sharing respecting Indigenous data sovereignty.” In RDA COVID-19 Working Group (2020). Recommendations and guidelines on data sharing. Research Data Alliance. https://doi.org/10.15497/rda00052.

Scott, James C. (2009) The Art of Not Being Governed: An Anarchist History of Upland Southeast Asia. New Haven: Yale University Press, 464 pp. Revista Andaluza De Antropología, 1, 126–129. https://doi.org/10.12795/raa.2011.i01.10

Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).

Want quick summaries of the latest research & reporting in AI ethics delivered to your inbox? Subscribe to the AI Ethics Brief. We publish bi-weekly.

Primary Sidebar

🔍 SEARCH

Spotlight

AI Policy Corner: Singapore’s National AI Strategy 2.0

AI Governance in a Competitive World: Balancing Innovation, Regulation and Ethics | Point Zero Forum 2025

AI Policy Corner: Frontier AI Safety Commitments, AI Seoul Summit 2024

AI Policy Corner: The Colorado State Deepfakes Act

Special Edition: Honouring the Legacy of Abhishek Gupta (1992–2024)

related posts

  • “Cool Projects” or “Expanding the Efficiency of the Murderous American War Machine?” (Research Summa...

    “Cool Projects” or “Expanding the Efficiency of the Murderous American War Machine?” (Research Summa...

  • Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

    Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

  • The Proliferation of AI Ethics Principles: What's Next?

    The Proliferation of AI Ethics Principles: What's Next?

  • A Matrix for Selecting Responsible AI Frameworks

    A Matrix for Selecting Responsible AI Frameworks

  • System Cards for AI-Based Decision-Making for Public Policy

    System Cards for AI-Based Decision-Making for Public Policy

  • Study of Competition Issues in Data-Driven Markets in Canada

    Study of Competition Issues in Data-Driven Markets in Canada

  • The Role of Relevance in Fair Ranking

    The Role of Relevance in Fair Ranking

  • Technical methods for regulatory inspection of algorithmic systems in social media platforms

    Technical methods for regulatory inspection of algorithmic systems in social media platforms

  • Impacts of Differential Privacy on Fostering More Racially and Ethnically Diverse Elementary Schools

    Impacts of Differential Privacy on Fostering More Racially and Ethnically Diverse Elementary Schools

  • Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

    Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

Partners

  •  
    U.S. Artificial Intelligence Safety Institute Consortium (AISIC) at NIST

  • Partnership on AI

  • The LF AI & Data Foundation

  • The AI Alliance

Footer

Categories


• Blog
• Research Summaries
• Columns
• Core Principles of Responsible AI
• Special Topics

Signature Content


• The State Of AI Ethics

• The Living Dictionary

• The AI Ethics Brief

Learn More


• About

• Open Access Policy

• Contributions Policy

• Editorial Stance on AI Tools

• Press

• Donate

• Contact

The AI Ethics Brief (bi-weekly newsletter)

About Us


Founded in 2018, the Montreal AI Ethics Institute (MAIEI) is an international non-profit organization equipping citizens concerned about artificial intelligence and its impact on society to take action.


Archive

  • © MONTREAL AI ETHICS INSTITUTE. All rights reserved 2024.
  • This work is licensed under a Creative Commons Attribution 4.0 International License.
  • Learn more about our open access policy here.
  • Creative Commons License

    Save hours of work and stay on top of Responsible AI research and reporting with our bi-weekly email newsletter.