Outputs (3)

Working towards a greener Britain: Who, where and for whose benefit? (2024)
Conference Proceeding
Whittard, D., Bradley, P., Phan, V., & Ritchie, F. (in press). Working towards a greener Britain: Who, where and for whose benefit?.

Given the urgency of the transition to net-zero, there is a need for a robust evidence base to support green policy interventions. Intelligence in relation to green jobs, however, is partial and fragmented, partially due to the lack of an internation... Read More about Working towards a greener Britain: Who, where and for whose benefit?.

Risk of disclosure when reporting commonly used univariate statistics (2022)
Conference Proceeding
Derrick, B., Green, E., Ritchie, F., & White, P. (2022). Risk of disclosure when reporting commonly used univariate statistics. In Lecture Notes in Computer Science (119-129). https://doi.org/10.1007/978-3-031-13945-1_9

When basic or descriptive summary statistics are reported, it may be possible that the entire sample of observations is inadvertently disclosed, or that members within a sample will be able to work out responses of others. Three sets of univariate su... Read More about Risk of disclosure when reporting commonly used univariate statistics.

Statistical disclosure controls for machine learning models (2021)
Conference Proceeding
Krueger, S., Mansouri-Benssassi, E., Ritchie, F., & Smith, J. (2021). Statistical disclosure controls for machine learning models

Artificial Intelligence (AI) models are trained on large datasets. Where the training data is sensitive, the data holders need to consider risks posed by access to the training data and risks posed by the models that are released. The first problem c... Read More about Statistical disclosure controls for machine learning models.