This article critically reviews recent initiatives to employ artificial intelligence (AI), particularly large language models (LLMs), for the revitalization of Indigenous languages. Structured by geographical contexts, the analysis includes Irish Gaelic (Europe), Māori (Aotearoa/New Zealand, Oceania), Guaraní (Paraguay/Bolivia, South America), and Inuktitut (Canada, North America). Applying a theoretical framework grounded in data colonialism and Indigenous data sovereignty, the article examines the key achievements in different regional endeavors, as well as investigate how government-led projects and Big Tech collaborations across these diverse contexts navigate (or fail to navigate) issues of data extraction, community consent, cultural representation, and ownership. Through this lens, the article identifies specific ethical pitfalls as well as commendable practices that either reproduce colonial dynamics or empower Indigenous communities. This critique emphasizes regional and contextual nuances, arguing that authentic community agency and rigorous adherence to Indigenous data sovereignty principles are vital to ensuring ethical AI practices and meaningful linguistic revitalization.

As AI changes the way the world gathers information, some critics say that it is perpetuating stereotypes and erasing cultural nuances for Indigenous groups and people of color. Most mainstream models are trained on the work of Western writers—particularly white men—and regularly mimic those values, writing styles, viewpoints, and biases. Some critics say the data grab is a new form of colonialism, where information gathering replaces Imperial-era land seizures while the AI companies—rather than a conquering nation—reap profits from marginalized groups. Data collection from these groups is often done without their consent or any verification that the information is accurate.

Jorge Perez

Little attention is instead given to what values LLMs may reflect and what behaviours they may assume beyond those relating to safety. How well do the values, beliefs and behaviours of these models align with those of their users?

This paper is concerned with issues of trust and bias in generative AI in
general, and chatbots based on large language models in particular (e.g. ChatGPT).
The discussion argues that intercultural communication scholars must do more to
better understand generative AI and more specifically large language models, as
such technologies produce and circulate discourse in an ostensibly impartial way,
reinforcing the widespread assumption that machines are objective resources for
societies to learn about important intercultural issues, such as racism and discrim
ination. Consequently, there is an urgent need to understand how trust and bias
factor into the ways in which such technologies deal with topics and themes central
to intercultural communication. It is also important to scrutinize the ways in which
societies make use of AI and large language models to carry out important social
actions and practices, such as teaching and learning about historical or political
issues

Are AI systems truly representing human values, or merely averaging across them? Our study suggests a concerning reality: Large Language Models (LLMs) fail to represent diverse cultural moral frameworks despite their linguistic capabilities. We expose significant gaps between AI-generated and human moral intuitions by applying the Moral Foundations Questionnaire across 19 cultural contexts. Comparing multiple state-of-the-art LLMs’ origins against human baseline data, we find these models systematically homogenize moral diversity. Surprisingly, increased model size doesn’t consistently improve cultural representation fidelity. Our findings challenge the growing use of LLMs as synthetic populations in social science research and highlight a fundamental limitation in current AI alignment approaches. Without data-driven alignment beyond prompting, these systems cannot capture the nuanced, culturally-specific moral intuitions. Our results call for more grounded alignment objectives and evaluation metrics to ensure AI systems represent diverse human values rather than flattening the moral landscape.

E. Ramirez Auckland University of Technology

Ineke Hendrika Martine Crezee Auckland University of Technology

Family violence within Aboriginal communities continues to attract considerable scholarly, governmental and public attention in Australia. While rates of victimization are significantly higher than non-Aboriginal rates, Aboriginal women remain suspicious of the ‘carceral feminism’ remedy, arguing that family violence is a legacy of colonialism, systemic racism, and the intergenerational impacts of trauma, requiring its own distinctive suite of responses, ‘uncoupled’ from the dominant feminist narrative of gender inequality, coercive control and patriarchy. We conclude that achieving meaningful reductions in family violence hinges on a decolonising process that shifts power from settler to Aboriginal structures. Aboriginal peoples are increasingly advocating for strengths-based and community-led solutions that are culturally safe, involve Aboriginal justice models, and recognises the salience of Aboriginal Law and Culture. This paper is based on qualitative research in six locations in northern Australia where traditional patterns of Aboriginal Law and Culture are robust. Employing a decolonising methodology, we explore the views of Elders in these communities regarding the existing role of Law and Culture, their criticisms of settler law, and their ambitions for a greater degree of partnership between mainstream and Aboriginal law. The paper advances a number of ideas, based on these discussions, that might facilitate a paradigm shift in theory and practice regarding intervention in family violence.