The GOV.UK feedback form was receiving a lot of spam requests. We developed a machine learning model to detect spam responses — here is how we created it.
The Race Disparity Unit at the Cabinet Office Equalities Hub have analysed different approaches taken by national governments to understanding how they compare on issues such as ethnic diversity and cultural identity.
Richard Laux outlines the 3 main reasons for collecting data about people’s ethnicity and identifies 6 principles for collecting these to meet all users’ needs.
...look for specific accessibility content issues. We wrote it in Python, as it’s a GDS-supported programming language, and structured the code in a way that allowed us to write and...
...government users in different departments share the same understanding of what information should represent, how trusted it is and how best to find it. For data standards to be really...
...techniques to obscure sensitive or private information in datasets. These include statistical methods, deep learning techniques and natural language processing for the data types above. Typically they can be summarised...
...of the victim. New pages that we’d like to develop include loneliness, and access to gardens and other green spaces. New data pages in development ‘Ethnicity facts and figures’ includes...
...and the public sector. Machine Learning is a method for creating algorithms that enable computers to learn from data to make predictions. Examples of machine learning techniques are; reinforcement learning,...
...for example our work with the Office for National Statistics (ONS) on linked data - setting out how we might use linkage, (i) to understand or to tackle quality limitations,...
...percentages, so it's useful to be able to see both for the full picture. For example, in 5 workforces – armed forces, firefighters, prison officers, tribunal judges and non-legal members...
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