- Convenors:
-
Benjamin Ibhazukor
(Nigeria Agricultural Quarantine Service)
Oyinlola Ogunpaimo (Teagasc Irish Development Authority)
Olaiwola Ogunpaimo (University of Galway)
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- Format:
- Roundtable
- Stream:
- Digital futures: AI, data & platform governance
Short Abstract
This roundtable unpacks how AI and digital technologies reproduce hierarchies and introduce new inequalities. Moving beyond narratives of innovation and inclusion, it explores decolonial, justice-driven visions for technological futures grounded in solidarity, care and collective agency.
Description
This roundtable examines how artificial intelligence and digital technologies both shape and are shaped by entrenched systems of inequality, colonial legacies, and ecological precarity. It asks whether the current digital revolution can genuinely advance justice and sustainability or whether it reinforces existing hierarchies under the guise of innovation and progress. Framed through political ecology, decolonial theory and feminist technoscience the discussion interrogates how algorithmic systems and digital infrastructures reproduce the unequal geographies of power that structure the twenty-first century.
Bringing together scholars, practitioners, and activists, the roundtable explores the epistemic and political limits of dominant technology discourses those organized around binaries such as human/machine, innovation/regulation, resistance/resilience, and global North/South. Participants will offer critical insights into how data extraction, algorithmic governance, and platform capitalism perpetuate patterns of domination, surveillance, and dispossession, thereby undermining the goals of SDG 10 (Reduced Inequalities).
Rather than seeking reform within existing systems of digital capitalism, this roundtable invites radical alternatives. It considers practices of collective design, technological refusal and the dismantling of digital hierarchies that constrain community agency, care, and ecological interdependence. By shifting the focus from efficiency to ethics and from optimization to solidarity, the discussion aims to cultivate a shared vocabulary and framework for technological justice. Through collaborative reflection, participants will identify pathways toward accountable, transparent and equitable technologies that democratize power in line with the transformative vision of the SDGs.
Accepted contributions
Contribution short abstract
This contribution examines community data sovereignty as a decolonial practice that resists extractive AI systems and reorients digital futures toward care, collective governance, and ecological justice.
Contribution long abstract
This contribution critically examines community data sovereignty as a decolonial and justice-oriented response to the extractive logics of contemporary AI and digital infrastructures. Drawing on decolonial theory, it interrogates how dominant data regimes shaped by platform capitalism, algorithmic governance, and colonial knowledge systems reproduce racialized, gendered, and spatial inequalities. Rather than treating data as a neutral resource to be optimized, this intervention foregrounds data as a relational and contested terrain embedded in histories of dispossession, surveillance, and ecological exploitation.
The contribution explores how community-led data practices challenge prevailing binaries such as innovation versus regulation or inclusion versus exclusion by advancing alternative modes of technological engagement rooted in collective agency, care, and refusal. Through examples from Indigenous data governance initiatives, grassroots environmental monitoring, and feminist tech collectives, it highlights how communities are reclaiming control over data generation, ownership, and use in ways that resist extraction and recentralize accountability.
By situating data sovereignty within broader struggles for ecological justice, this contribution argues that technological justice cannot be achieved through reformist tweaks to existing AI systems. Instead, it requires dismantling hierarchical digital architectures and cultivating solidaristic forms of technological design that prioritize relational ethics over efficiency. The contribution offers a framework for understanding data sovereignty as both a political practice and an ethical horizon, one that reimagines digital futures not as engines of growth, but as infrastructures for collective flourishing and planetary care.
Contribution short abstract
This paper explores how smallholder farmers in Nigeria manage pests and diseases amid climate change. Using a justice-focused lens, it examines power, data control, and whose knowledge shapes farming decisions, highlighting risks and opportunities in technology-driven solutions.
Contribution long abstract
Climate change is worsening pest and disease outbreaks for smallholder farmers in Nigeria, placing increasing pressure on those who already face limited resources and fragile livelihoods. In response, digital technologies for pest detection, disease forecasting, and climate advisory services are often promoted as solutions. While these technologies promise improvements in farm management, focusing only on digital approaches risks overlooking deeper issues of fairness, power, and control in agriculture.
This paper examines how these technologies are designed and applied in smallholder farming. Many rely on external data, standardized models, and privately managed platforms that often fail to reflect local environmental conditions or farmers’ knowledge. As a result, farmers are often positioned as users or data contributors rather than active participants in shaping these systems.
Framing the analysis around technological justice, the paper emphasizes participation, accountability, and knowledge inclusion. It considers whose insights inform pest and disease models, how climate risks are interpreted, and who benefits when technology guides farming decisions. Without meaningful farmer involvement, such technologies can reinforce existing inequalities instead of strengthening resilience to climate challenges.
The paper critiques policies that equate fairness with tool access or training, arguing that data ownership, transparency, and power relations must also be addressed. It concludes by proposing principles for justice-focused technologies in Nigerian smallholder agriculture, supporting climate adaptation while respecting farmers’ knowledge, autonomy, and rights.
Contribution short abstract
This contribution questions whether AI solutions to climate and environmental crises serve justice or entrench inequality, examining who benefits, who pays, and how AI can be reoriented to support equity, climate justice, and truly sustainable futures.
Contribution long abstract
Artificial intelligence (AI) is increasingly framed as a transformative tool for development and climate action. Yet, these narratives often assume that digitalisation and automation inherently advance progress, ignoring how AI systems can reproduce or exacerbate existing inequalities. This contribution critically examines the potential and limitations of AI in addressing climate and environmental crises, with a particular focus on climate justice and sustainable futures. It explores the intersections between emissions, climate change, and disease outbreaks, highlighting how technological interventions can shape welfare outcomes unevenly across populations and geographies. By interrogating who benefits from AI-enabled solutions and who bears the costs, the discussion challenges dominant assumptions that AI is a neutral tool for environmental problem-solving. Drawing on examples from both Global North and Global South contexts, the contribution considers how AI can be designed, governed, and deployed in ways that prioritize equity, accountability, and local knowledge. The roundtable aims to move beyond technical efficiency and digitalisation hype, asking what a just and sustainable AI-enabled development pathway might look like in practice. Participants will be invited to critically reflect on the ethical, social, and political dimensions of AI in environmental governance, and to discuss strategies for ensuring that technological innovation contributes to fair and effective climate action rather than deepening existing vulnerabilities. Ultimately, the discussion seeks to generate forward-looking perspectives on aligning AI development with broader goals of climate justice, environmental sustainability, and inclusive development.
Contribution short abstract
Inequalities in uses, access and reach of AI in South Asian society is hard to ignore. If we have to understand these inequalities related to AI, then we need to explore it on the basis of case study.. This study is trying to do the same.
Contribution long abstract
AI’s real value lies in its collaboration with human expertise, complementing rather than replacing human effort. This ties to another myth about AI being unbiased (Nussbaum, 2023). In reality, AI's fairness and productivity depends on the data and algorithms behind it. An example of this is the growing research on gender bias in machine translation systems like Google Translate.(Farkas & Németh, 2022)
The need for deeper exploration of AI's capabilities and limitations is often driven by tech enthusiasts, predominantly males, who spend more time engaging with AI technologies (Humlum & Vestergaard, 2024). On the other hand, females tend to use AI more frequently but exhibit greater concerns (Bouzar et al., 2024).
These concerns are often rooted more in fear of the unknown than in factual experiences, but there is a cost to this.
All predictive models, AI included, are more accurate when they incorporate diverse human experiences, much in line with the Diversity prediction theorem. The fact that AI is growing at such a pace while leaving behind many under-representative groups, because of these apprehensions, is dangerous for the society, as disparities like these could exacerbate economic inequality among diverse groups. (Aldasoro et al., 2024)
Underscoring the need for data-driven analysis and anecdotal research to clarify AI’s true capabilities and limitations, this study will be an attempt at showing what are the costs of AI moving forward with this racial and gender gap by using Case Study method.