Self Tracking
Venture Labor
Surviving the New Economy

New Project with the ESRC and Women’s Forum


Excited to announce a new collaborative doctoral studentship in partnership with the Women’s Forum for the Economy and Society.

This doctoral research will make use of a range of social science methods to study the gender impact of artificial intelligence (AI) systems in business or work contexts.


Data Work: The Hidden Talent And Secret Logic Fuelling Artificial Intelligence

What happens when new artificial intelligence (AI) tools are integrated into organisations around the world?For example, digital medicine promises to combine emerging and novel sources of data and new analysis techniques like AI and machine learning to improve diagnosis, care delivery and condition management. But healthcare workers find themselves at the frontlines of figuring out new ways to care for patients through, with – and sometimes despite – their data. Paradoxically, new data-intensive tasks required to make AI work are often seen as of secondary importance. Gina calls these tasks data work, and her team studied how data work is changing in Danish & US hospitals (Moller, Bossen, Pine, Nielsen and Neff, forthcoming ACM Interactions).Based on critical data studies and organisational ethnography, this talk will argue that while advances in AI have sparked scholarly and public attention to the challenges of the ethical design of technologies, less attention has been focused on the requirements for their ethical use. Unfortunately, this means that the hidden talents and secret logics that fuel successful AI projects are undervalued and successful AI projects continue to be seen as technological, not social, accomplishments.


Using Social Comparisons to Facilitate Healthier Choices

This exploratory research examines how we might nudge consumers towards making healthier food choices in online grocery shopping or other digitally mediated food consumption contexts. Our pilot study investigated how different forms of social comparisons could be used to encourage consumers to reduce the number of calories contained in their online grocery basket. Our findings show that participants who were less interested in trying new diets were more willing to reduce calories when presented with a comparison to people unlike them, an out-group member comparison, while those who were interested in trying new diets were more willing to reduce calories regardless of social comparison type. These findings imply that one size does not fit all when nudging. More research is needed to see how social comparisons influence the effectiveness of digital health behavior projects.

DiCosola, Blake & Gina Neff. 2020. “Using Social Comparisons to Facilitate Healthier Choices in Online Grocery Shopping Contexts.” CHI 2020, April 25–30, 2020, Honolulu, HI, USA.