arXiv subjects

Te Taka Keegan

Publications and source records attributed to Te Taka Keegan.

3 recordsLinked to original sources

arXiv2026

Data Findability, Governance, and Community Engagement for Māori Research Data Sovereignty

Māori data sovereignty (MDSov) has established important principles for recognising Māori rights and interests in data. Relatively little attention has been given to how these principles can be operationalised within research institutions who, as a function of their operations, collect and use Māori data. This paper introduces the concept of Māori Research Data Sovereignty (MRDSov), extending existing understandings of MDSov into the specific context of research data and the research data lifecycle. Drawing on Indigenous Data Sovereignty scholarship and research data management literature, we define Māori research data and position MRDSov as the application of MDSov principles to research data produced by, about, or for Māori. We argue that operationalising MRDSov requires three interdependent elements: data findability, data governance, and community engagement. Data findability enables Māori research data to be identified and contextualised; governance provides mechanisms through which Māori authority over research data can be exercised; and community engagement grounds both in enduring relationships with Māori communities. We further demonstrate how these elements collectively enact the principles of MDSov within institutional research settings, providing a practical pathway for aligning research data practices with Te Tiriti o Waitangi obligations and Indigenous data governance expectations. The paper contributes a conceptual framework for universities and other research organisations seeking to embed Māori authority, accountability, and relationships throughout the research data lifecycle.

cs.CY↗

arXiv2023

Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies

The benefits and capabilities of pre-trained language models (LLMs) in current and future innovations are vital to any society. However, introducing and using LLMs comes with biases and discrimination, resulting in concerns about equality, diversity and fairness, and must be addressed. While understanding and acknowledging bias in LLMs and developing mitigation strategies are crucial, the generalised assumptions towards societal needs can result in disadvantages towards under-represented societies and indigenous populations. Furthermore, the ongoing changes to actual and proposed amendments to regulations and laws worldwide also impact research capabilities in tackling the bias problem. This research presents a comprehensive survey synthesising the current trends and limitations in techniques used for identifying and mitigating bias in LLMs, where the overview of methods for tackling bias are grouped into metrics, benchmark datasets, and mitigation strategies. The importance and novelty of this survey are that it explores the perspective of under-represented societies. We argue that current practices tackling the bias problem cannot simply be 'plugged in' to address the needs of under-represented societies. We use examples from New Zealand to present requirements for adopting existing techniques to under-represented societies.

cs.CY↗

arXiv2023

Māori algorithmic sovereignty: idea, principles, and use

Due to the emergence of data-driven technologies in Aotearoa New Zealand that use Māori data, there is a need for values-based frameworks to guide thinking around balancing the tension between the opportunities these create, and the inherent risks that these technologies can impose. Algorithms can be framed as a particular use of data, therefore data frameworks that currently exist can be extended to include algorithms. Māori data sovereignty principles are well-known and are used by researchers and government agencies to guide the culturally appropriate use of Māori data. Extending these principles to fit the context of algorithms, and re-working the underlying sub-principles to address issues related to responsible algorithms from a Māori perspective leads to the Māori algorithmic sovereignty principles. We define this idea, present the updated principles and subprinciples, and highlight how these can be used to decolonise algorithms currently in use, and argue that these ideas could potentially be used to developed Indigenised algorithms.

cs.CY↗