Erin Lorelie Young is a postdoctoral research fellow on the Women in Data Science and AI project, within the Public Policy programme. She has a DPhil (PhD) from the University of Oxford, where she studied the socio-technical practices of interdisciplinary research and development projects. Prior to joining the Turing, Erin was an H-STAR Visiting Researcher at Stanford University, and a Research Assistant at the Oxford Internet Institute, working on various projects including investigating the potential of artificial intelligence (AI) for lifelong learning.

Erin has also held positions as a consultant at the International Institute for Educational Planning (IIEP) at UNESCO in Paris, and as an analyst for Kantar Consulting (WPP) in London, and for Thomson Reuters in New York City. She has a PGC in International Business Administration and Practice and Organisational Behaviour, and earned scholarships to study at the British Schools of Athens and Rome. She holds an MSc (Distinction) from the University of Oxford in Education (Learning and Technology), and an MA in Classics from the University of Cambridge.

Erin’s selected invited lectures, talks, and presentations include:

‘Mapping gender gaps and inequalities in data and AI’, Invited talk, Covid-19 Task Force Gender Equality Group, Cabinet Office, September 2021.

‘Women in Data Science and AI: Mapping the gender job gap,’ Invited Lecture, Turing Talks for GCHQ Staff, GCHQ, June 2021.

‘Where are the women? Mapping the gender job gap in AI and data science’, Turing Catch-Up, Invited talk, The Alan Turing Institute, June 2021.

‘If it’s not diverse, it’s not ethical’, Invited panel member, Ethics & Society stage, CogX, June 2021.

‘Does it matter that new technology development is so male-dominated?’, Invited panel member, ‘Ethics of New Tools and Technologies’, LIS Bibliometrics Conference, May 2021.

“Gender Bias in AI: ‘Women in Data Science and AI’ at The Alan Turing Institute”, Invited Lecture, ‘Research in Real Worlds’ seminar series, Oxford Internet Institute (OII), University of Oxford, May 2021.

‘Where are the Women? Mapping the Gender Job Gap in AI’, Invited talk, ‘Let's Multiply: Data Science Community of Interest’ (International Day of Women and Girls in ICT), Data Science Campus, Office for National Statistics (ONS), April 2021.

‘AI and Ethics’, Panel moderator, Austrian Cultural Forum, April 2021.

‘Gender Bias in AI: Women in Data Science and AI at The Alan Turing Institute,’ Invited Lecture, Civil Service Fast Stream, Digital, Data, Technology and Cyber Winter Conference (DDaTCon), February 2021.

‘Online learning for all: Can digital literacy be used to leapfrog illiteracy?,’ Invited Lecture, Second Chance Education and Vocational Learning Programme (SCE), United Nations (UN Women), December 2020.

‘Building inclusive technology and diverse workplaces through data,’ Invited Lecture, ‘Diversity, inclusion and fairness in safety tech’, Safety Tech Innovation Network, KTN, Department for Digital, Culture, Media and Sport (DCMS), November 2020.

‘Addressing Gender Bias in AI: The Women in Data Science and AI project at The Alan Turing Institute,’ Invited talk, CogX Conference, June 2020.

‘Gender Bias in AI’, Invited Lecture, ‘Research in Real Worlds’ seminar series, Oxford Internet Institute (OII), University of Oxford. June 2020.

‘Women in Data Science and AI’, Invited talk, Turing Town Hall, The Alan Turing Institute, January 2020.

Research interests

Erin is interested in the social and ethical implications of technologies, particularly artificial intelligence (AI), machine learning and other data-driven innovations. Her work sits at the intersection of technology and society and draws from Science and Technology Studies (STS), in particular Actor-Network Theory (ANT), organisational sociology, ethnography, intersectional techno-feminism and the social shaping of technology. Her research at the Turing Institute examines the gendered practices mediating the data science and AI fields, considering the political and socio-economic roots of the networks that shape, deploy and govern AI systems and their applications. She investigates the factors which impact the position and role of women in data science and AI professions, and the systemic conditions and structural inequality of opportunity that perpetuate patterns of discrimination against women in these areas.