Jacqueline Rowe

Research keywords: Bias and fairness, multilinguality, AI safety

Personal website: https://jacquelinerowe.github.io/

Bio:

Jacqueline has a long-standing interest in linguistic fairness and inclusion, originally grounded in her experience as an English teacher in West Africa (2014-2016). As an undergraduate (BA in Linguistics, University of Cambridge, 2016-2019), she conducted sociolinguistic research on the use of creoles and indigenous languages in administrative and educational settings in Guinea-Bissau, and during her LLM in Human Rights (Birkbeck, University of London, 2020-2021), she focused her dissertation on how the rights to education and non-discrimination apply to minority language speakers who wish to be taught in their first language. Later, pivoting to NLP (MSc in Computer Science with Speech and Language Processing, University of Sheffield, 2023-2024), she developed a novel dataset and series of low-resource machine translation models for Bissau-Guinean creole, investigating cross-lingual transfer between creole and Portuguese.

Jacqueline also holds several years’ experience in technology policy research and advocacy, as Coordinator of the International Law Programme at Chatham House and as Policy Lead at Global Partners Digital. Her research focused on the application of international human rights law in cyberspace, with projects on online content governance and platform regulation, methods to address online disinformation and hate speech, and transparency and accountability for automated systems.

PhD research:

Jacqueline’s PhD research focuses on the gap between theoretical NLP bias evaluation metrics and the tangible disparities that emerge when these systems are deployed in real-world contexts. She works to make bias assessments more inclusive and realistic, including by representing a broader range of languages and cultural perspectives; incorporating users’ lived experiences and perspectives on bias and fairness; and grounding computational bias evaluations in interdisciplinary expertise and validated research. Her work also touches on measurement practices and standardisation trade-offs, and the broader evaluation ecosystems and incentives that shape how fairness is defined and enforced in AI systems.

Supervisors: Alexandra Birch, Shannon Vallor