Public sector AI has the potential to harm citizens, with risks increasing as its use expands. Recent work positions public procurement as a way to shape public sector AI in line with public interests, using the state’s purchasing power to influence which AI systems are procured and under what conditions. In this presentation, I explore how this potential could be realised in practice by drawing on semi-structured interviews with UK/EU buyers, providers, and procurement experts.

The AI & Equality community is organizing an inaugural Festival of Ideas (May 21-22, 2026, online). 
This global, community-led festival will be a space to share disruptive and novel ideas, and to collectively examine the power dynamics of this moment. 

Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI's potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face inadequate community engagement and sustainability challenges. While existing literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the funding agenda and rhetoric that influences downstream approaches.

This 30-minute talk is grounded in two complementary research trajectories:
(1) extensive digital ethnography with 247 women-led technology communities, focused on platforms, communication, participation and power (not AI-specific), and (2) parallel academic studies on AI, inequality and the Global South, examining how automated systems interact with structural asymmetries, governance gaps and cultural contexts.

How do large language models understand the lived experiences of stigmatized groups, and when does this understanding differ from the human perspective? Can this lead to bias, and if so, do our existing safety tools help mitigate such bias? This work investigated open-source language models for bias against 93 stigmatized groups, identifying that specific types of biases (especially those deemed by humans to be 'threatening' such as having HIV or a criminal record) experience significantly more bias than other types of stigmatized identities.

The digital world suffers from a profound linguistic disparity, particularly in Africa where a lack of local language content and traditional, Global North-led language technology models fail to meet community needs, often resulting in data extraction and inequitable solutions. In an 18-month research project, in collaboration with the Distributed AI Research Institute (DAIR), we highlight a powerful alternative: a growing grassroots movement of community-based language technology initiatives across Africa that adopt a bottoms-up approach, prioritizing local needs and incorporating indigenous philosophies.