Search engines are powerful epistemic actors. A substantial body of critical literature has drawn attention to the tendency of search engines to reproduce historical bias and marginalisation through filtering, ranking and search assistance techniques. The integration of new modes of information curation supported by large language models into search engine systems poses novel and enhanced threats to information diversity and the visibility of historically marginalized perspectives and knowledge.

The extent to which search engines promote harmful representations, and marginalise or centre Indigenous self-representation and knowledge, warrants attention. The growing shift in emphasis – from ranked blue links to direct, ‘authoritative’ answers on search engine result pages (SERPs) – make such inquiries more pressing. What does the trend started by formats like featured snippets and accelerated by AI-generated answers imply for Indigenous representation and truth-telling online?

The aim of this study is to provide a basis for reflection on that question by examining how Google presents and prioritises information related to Aboriginal identities and histories in two answer formats – AI overviews and featured snippets. It argues that direct answers in both formats sever the links between knowers, knowledge and context in different ways, with implications for Indigenous self-representation, rereading and reframing.

Electronic sports (esports) have emerged as a crucial space for Indigenous players in Brazil, particularly through the game Free Fire (FF), which is accessible via mobile devices. The increasing adoption of smartphones has enabled the formation of Indigenous teams across different regions of the country, creating new spaces for resistance and digital visibility. This ethnographic study explores the knowledge practices and gaming approaches of players from four Indigenous groups in Brazil: Apunirã, Ava-Guarani, Guarani, and Xakriabá. Grounded in experiential ethnography and decolonial Oral History, the research examines how these players negotiate their identities and advocate for Indigenous rights within the esports ecosystem, which has traditionally been dominated by non-Indigenous actors. Indigenous players utilize esports as a tool for digital activism, challenging stereotypes and denouncing political threats such as the “temporal framework” thesis, which underpins Brazil’s Law 14.701/23. Through esports platforms and digital social networks, they amplify their struggle for territorial rights and environmental justice, demonstrating that Indigenous participation in technology does not contradict their cultural identities but rather reinforces their presence in digital spaces. In this way, esports move beyond being merely a site of entertainment and become a space for digital sovereignty and decolonial resistance. This study contributes to the understanding of digital games through an ethno-racial perspective, highlighting how Indigenous players reframe gaming spaces to challenge colonial structures and assert their political and cultural existence.

This paper discusses project INDG (a pseudonym), which explored what the digital good looks like for Indigenous peoples in Latin America. INDG explored how Indigenous understandings of ‘good living’ – ‘Buen Vivir’ in Spanish, a concept which subordinates economic measures of good living to criteria such as human dignity, social justice, and ecology – can advance thinking about the ‘digital good’. One output was a manifesto for indigenous good digital living, which summarises participants’ visions of digital good living for them.

We are now extending the work of INDG, asking: “how can we put into action the principles present in the manifesto created in the INDG project?” by engaging with people from indigenous communities in four Latin American countries who have not previously encountered the manifesto, discussing and iterating it with them, asking whether and how it would enable good digital living for them, and how we can enact the manifesto’s principles.

Our project will ensure that emerging notions of the digital good are informed by knowledge and lived experiences from the Global South. By focusing on indigenous knowledges and perspectives, the project ruptures Global North thinking, contributes to the decolonization of conceptualisations of the digital good, and of internet research more generally, and points towards possible sustainable futures.

The rise of generative AI (Gen-AI) models in cultural production has enabled broader access to image-making, particularly for underrepresented communities. Tools like MidJourney, DALL-E, and Stable Diffusion allow users with little technical expertise to create images from text prompts. However, these models reproduce biases embedded in their training datasets, raising concerns about representation, visibility politics, and data exploitation. This paper investigates these issues through the lens of Indigenous self-representation in Abya Yala (the Americas), and draws from initial findings from a project involving Indigenous artists from South America that explored Gen-AI’s limitations and its potential for greater autonomy in self-representation. The project identified that Gen-AI often reinforced stereotypes but also provided participants with a sense of control over their images. To address these limitations, the team developed a custom Indigenous Gen-AI model emphasizing autonomy and ethical data use. The model, based on Stable Diffusion, integrates Indigenous design elements and operates offline to ensure data sovereignty. The project also emphasizes a co-creative process involving scholars, Indigenous artists, and technologists. The initiative aligns with the principles of "Digital Buen Vivir," advocating for ethical, sustainable, and community-driven technology use. While the model empowers Indigenous creators some challenges remain, including technological accessibility, digital literacy gaps, and the structural constraints of Gen-AI. We conclude with recommendations for Indigenous engagement with AI and the future development of self-representational Gen-AI technologies.