Sarah Kiden

This paper uses the theory of decoloniality to critically examine how African scholars have interrogated the dominant, global narratives surrounding responsible Artificial Intelligence (AI). Through a multivocal literature review, the paper reveals a range of critical perspectives that expose systemic exclusions and imbalances in the current responsible AI discourse. Key themes identified include the marginalisation of African epistemologies, values, and principles; the absence of representative African datasets, lack of locally grounded regulatory frameworks; the limited inclusion and or availability of African expertise and funding; and the persistent power asymmetries and coloniality embedded in global responsible AI ethics and governance. The review highlights emerging efforts towards reconstructing responsible AI from an African contextual lens. Central to this constructive turn is the proposal of a decoloniality framework that puts emphasis on African knowledge systems, reclaims agency in AI development, and reimagines responsible AI in ways that are contextually grounded, pluriversal, and socially acceptable. This review not only contributes to critical AI scholarship but also charts pathways for more inclusive and equitable AI futures on the continent.

Evidence shows that Text-to-Image (T2I) models disproportionately reflect Western cultural norms, amplifying misrepresentation and harms to minority groups. However, evaluating cultural sensitivity is inherently complex due to its fluid and multifaceted nature. This paper proposes a participatory and community-based evaluation approach to assess cultural sensitivity in T2I models, developed and validated through workshops involving 59 individuals from 19 different countries, thus embracing first-person perspectives. We describe our systematic six-step evaluation approach consisting of a reflection of evaluators’ positionality, definition of topics to be evaluated, initial reflections and expectations, image generation, quantitative and qualitative image evaluation, and final reflections. Quantitative scores and qualitative responses in our evaluation expose convergence and disagreement within and across communities, illuminate the downstream consequences of misrepresentation, and trace how training data shaped by unequal power relations distort visual depictions. The paper provides recommendations for stakeholders, highlighting pathways to investigate the sources, mechanisms, and impacts of cultural (mis)representation in T2I models.

This workshop paper examines challenges in designing agentic AI systems from a citizen-centric perspective. Drawing on three participatory workshops conducted in 2025 with members of the general public and cross-sector stakeholders, we explore how societal values and expectations shape visions of future AI agents. Using constructive design research methods, participants engaged in storytelling and lo-fi prototyping to reflect on potential community impacts. We identify three key challenges: enabling meaningful and sustained public engagement, establishing a shared language between experts and lay participants, and translating speculative participant input into implementable systems. We argue that reflexive, long-term participation is essential for responsible and actionable citizen-centric AI development.

Evidence shows that text-to-image (T2I) models disproportionately reflect Western cultural norms, amplifying misrepresentation and harms to minority groups. However, evaluating cultural sensitivity is inherently complex due to its fluid and multifaceted nature. This paper draws on a state-of-the-art review and co-creation workshops involving 59 individuals from 19 different countries. We developed and validated a mixed-methods community-based evaluation methodology to assess cultural sensitivity in T2I models, which embraces first-person methods. Quantitative scores and qualitative inquiries expose convergence and disagreement within and across communities, illuminate the downstream consequences of misrepresentation, and trace how training data shaped by unequal power relations distort depictions. Extensive assessments are constrained by high resource requirements and the dynamic nature of culture, a tension we alleviate through a context-based and iterative methodology. The paper provides actionable recommendations for stakeholders, highlighting pathways to investigate the sources, mechanisms, and impacts of cultural (mis)representation in T2I models.

In the last few years, we have seen an increased interest by communities in developing technologies, including telecommunication infrastructures. In HCI, we have seen work around food sharing and the role of technology in supporting sustainability efforts to reduce food waste. We bring these two debates together, reflecting on our experience of working with a Community Fridge to explore possibilities of technologies to support ongoing efforts. In finding our definition for community building, our contributions are twofold: (1) we must meaningfully consider community priorities in digital developments; and (2) introduce locally relevant technologies that relate to genuine priorities of grassroots organisations.