Peer review determines which scholarship is legitimized; however, review biases often disadvantage scholarship that diverges from the norm. Human–Computer Interaction (HCI) lacks a systemic inquiry into how such biases affect underrepresented Global South (GS) scholarship. To address this critical gap, we conducted four focus groups with 16 HCI researchers studying the GS. Participants reported experiencing reviews that confined them to development research, dismissed their theoretical contributions, and questioned situated knowledge from GS communities. Both as authors and reviewers, participants reported experiencing the epistemic burden of over-explaining why knowledge from GS communities matters. Further, they noted being tokenized as “cultural experts” when assigned to review papers and pointed out that the hidden curriculum of writing HCI papers often gatekeeps GS scholarship. Using epistemic oppression as a lens, we discuss how review practices marginalize GS scholarship and outline actionable strategies for nurturing equitable epistemological evaluation of HCI scholarship.

As languages evolve, we risk losing ancestral languages. In this paper, we explore Historical Language Reconstruction (HLR) for Proto-Sabean languages, starting with the identification of cognates–sets of words in different related languages that are derived from the same ancestral language. We (1) collect semantically related words in three Afro-Semitic languages from a three-way dictionary (2) work with linguists to identify cognates and reconstruct the proto-form of the cognates, (3) experiment with three automatic cognate detection methods and extract cognates from the semantically related words. We then experiment with in-context learning with GPT-4o to generate the proto-language from the cognates and use Sequence-to-Sequence (Seq2Seq) models for HLR.

Hellina Hailu Nigatu, Nikita Mehandru, Negasi Haile Abadi, Blen Gebremeskel, Ahmed Alaa, Monojit Choudhury. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Henok Biadglign Ademtew, Hizkiel Mitiku Alemayehu, Negasi Haile Abadi, Tadesse Destaw Belay, Seid Muhie Yimam. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Online Knowledge Repositories (OKRs) like Wikipedia offer communities a way to share and preserve information about themselves and their ways of living. However, for communities with low-resourced languages—including most African communities—the quality and volume of content available are often inadequate. One reason for this lack of adequate content could be that many OKRs embody Western ways of knowledge preservation and sharing, requiring many low-resourced language communities to adapt to new interactions. To understand the challenges faced by low-resourced language contributors on the popular OKR Wikipedia, we conducted (1) a thematic analysis of Wikipedia forum discussions and (2) a contextual inquiry study with 14 novice contributors. We focused on three Ethiopian languages: Afan Oromo, Amharic, and Tigrinya. Our analysis revealed several recurring themes; for example, contributors struggle to find resources to corroborate their articles in low-resourced languages, and language technology support, like translation systems and spellcheck, result in several errors that waste contributors’ time. We hope our study will support designers in making online knowledge repositories accessible to low-resourced language speakers.

The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced.However, there is limited consensus on what exactly qualifies as a 'low-resource language.'To understand how NLP papers define and study 'low resource' languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speechprocessing conferences that mention the keyword 'low-resource.' Based on our analysis, we show how several interacting axes contribute to 'low-resourcedness' of a language and why that makes it difficult to track progress for each individual language.We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.