Unlocking the value of Government’s knowledge

Working with Cabinet Office we are exploring how machine learning and natural language processing technology can be applied to help understand the contents of “digital heaps” of documents, so that policymakers can learn from previous work and KIMs (knowledge and information managers) can meet their legislative obligations.

Across a 9-week discovery we engaged with more than 40 stakeholders across 13 government departments in order to surface user needs and develop value propositions with a technology strategy that would address the need for better exploitation of knowledge across government.

In the subsequent alpha phase our team rapidly prototyped a product in an iterative process using one-week sprints, refining the design based on 4-5 usability testing sessions within each sprint. Our data scientists undertook three separate experiments to test the fundamental ML and NLP approaches that would underpin the product in a parallel stream of work.

Within very tight timeframes we demonstrated the value of three different core technologies that would underpin the beta product, co-design and prototype an MVP beta product and validate our work with a wide cross-government audience.

Using NLP we were able to successfully identify high-value documents, improving on the accuracy of the current processes with a >80% reduction in incorrectly identified docs (i.e. false positives).

Our work on this project has been described by Luke Sands (Head of Digital at the Cabinet Office) as the best application of AI he’s seen within government and received positive attention from The Times, quoted below:

atchai the times ai data science article