Open but Incompatible: A License Compatibility Analysis of Corpora for Low-Resource African Languages
arXiv:2606.28867v1 Announce Type: new Abstract: Creative Commons licenses dominate African NLP corpus releases, but their compatibility rules are rarely applied. CC-BY-SA and CC-BY-NC cannot be combined in a single published dataset; a NoDerivs clause silently prohibits tokenisation and annotation. This paper audits the license provenance of over twenty corpus families used in African NLP, constructs a six-tier compatibility matrix, and applies it to three case-study languages: Kituba/Munukutuba, Zarma, and Moore. Four failure modes are documented with primary-source evidence: outright prohibi
The proliferation of AI models reliant on diverse datasets, particularly for low-resource languages, makes license compatibility a critical and increasingly urgent issue for researchers and developers.
Incorrect or unapplied licensing rules for NLP corpora can undermine dataset development, restrict model training, and lead to legal challenges, hindering progress in AI for underrepresented language communities.
The explicit documentation of incompatible licenses reveals significant legal and technical hurdles for integrating diverse datasets, potentially forcing a reevaluation of data sharing practices in NLP.
- · Legal tech specializing in AI data licensing
- · Developers of custom, permissively licensed datasets
- · Organizations promoting open science and standard licensing practices
- · Researchers relying on ambiguously licensed data
- · NLP projects combining multiple datasets without legal review
- · African NLP communities facing data synthesis challenges
Research efforts to develop AI models for low-resource African languages will face increased difficulty in combining existing datasets due to licensing conflicts.
This incompatibility will drive demand for clearer licensing frameworks or the creation of new, explicitly compatible datasets, potentially leading to a fragmentation of available data resources.
The long-term consequence could be a slower pace of AI development for these languages, exacerbating the digital divide and potentially impacting 'sovereign AI' ambitions for African nations.
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Read at arXiv cs.CL