Flavia Saxler is a PhD candidate in the Sociology Department at the University of Cambridge and a Student Fellow at the Leverhulme Centre for the Future of Intelligence. She holds an undergraduate degree in Media and Communications from the University of Mannheim and an MPhil in the Sociology
of Media and Culture from the Department of Sociology at the University of Cambridge. Currently, she is a Recognised DPhil Student in Social Data Science at the Oxford Internet Institute supervised by Vicki Nash.
Flavia's research focuses on how AI and data-driven technologies - such as reporting platforms, chatbots, safety apps, CCTV, and predictive policing - are (re)shaping the sociotechnical imaginaries of women’s safety. Drawing on feminist AI and data ethics, as well as human-computer interaction, she
examines the ethical and societal implications of technological interventions aimed at addressing violence against women and girls in the United Kingdom.
In her master’s research, Flavia engaged with the user experience of digital investigators discovering and verifying images and videos of human rights violations and their impact on power and care. She conducted interviews 20 with volunteers, project managers, and leading figures in the open-source investigation space, including those who founded the Digital Verification Corps (Amnesty International) and the Human Rights Center Investigations Lab at the University of California, Berkeley.
In addition to her PhD research, Flavia has experience in the FemTech startup space, venture capital, and NGOs. She was a product owner in the FemTech startup ‘The Blood,’ where she worked on developing an ethical product for menstrual self-tracking based on biomarkers of period blood, ensuring the
design aligns with user experiences, motivations, and pain points.
Flavia presented her work at the ICA Media Sociology Conference in 2024 contributing to discussions on the ethical and social dimensions of AI and safety technologies.
She is always interested in interdisciplinary collaboration, especially across data and computer science.
