Artificial intelligence (AI) literacy is being highlighted in education policy as essential due to the rapid incorporation of AI, particularly generative AI, in learning environments. This position paper argues that global AI literacy frameworks are not suitable for Ghana and similar contexts because they view AI literacy primarily as a curricular issue rather than a governance question. Using the Royal Society's framework as an example, it suggests that these models make assumptions about infrastructure and data governance that do not align with Ghanaian realities. The paper identifies blind spots regarding learner agency, data sovereignty, and platform dependence, proposing that AI literacy should be seen as a form of soft governance. It also advocates for ethno-AI literacy as a locally relevant alternative, prompting discussion on the risks of adopting global models that may hinder educational autonomy in the Majority World.
Dodzi Koku Hattoh, S. A. Addo, Abigail Oppong et al.· Annual Conference on Innovat...· 0 citations
Large Language Models (LLMs) have achieved substantial progress in safety alignment, yet their safety guarantees remain significantly weaker in low-resource and multilingual settings than in high-resource languages. In this paper, we conduct a Systematic Literature Review (SLR) of LLM safety alignment in low-resource languages by adopting the PRISMA 2020 methodology. Out of roughly 1,500 papers identified from Semantic Scholar, arXiv, and OpenAlex, 50 relevant studies have been selected and analyzed. Our review is organized around four themes: safety alignment methods, multilingual safety risks, evaluation benchmarks, and cross-lingual transferability. We further propose a taxonomy of safety alignment approaches based on three adaptation mechanisms: data adaptation, objective optimization, and mechanistic alignment. Across literature, translated English benchmarks fail to sufficiently represent culturally rooted harms, and multilingual models are more vulnerable to cross-lingual jailbreaks, code-switching attacks, and safety degradation in underrepresented languages. These failures are driven by several key factors, including uneven multilingual pre-training coverage, insufficient native-language preference data, poor transfer of safety representations, and a lack of culturally aware evaluation frameworks. The review also notes that many low-resource languages, especially African languages, have fewer safety benchmarks available than other multilingual regions. Overall, the results reveal a persistent multilingual safety gap, and suggest that future progress will require culturally grounded benchmarks, participatory data collection, balanced multilingual pre-training, and scalable multilingual alignment methods.
Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Paula Chikaodinaka Anyanwu et al.· 0 citations
This work investigates cross-lingual safety transfer in four African languages, Twi, Hausa, Amharic, and Swahili, using LoDNA, a new safety dataset that pairs literal translations with culturally localized prompts to demonstrate superficial safety alignment.
Abigail Oppong, P SAM SAHIL, Tadesse Destaw Belay et al.· 0 citations