Ai Head Of Impact And Evaluation (G6)

The Incubator for Artificial Intelligence (i.AI) - is an AI for public good programme to harness the opportunities presented by AI to improve the lives of citizens. It focuses on a number of priority projects in home affairs, health, education and government efficiency projects. The i.AI team uses ethical and secure methods at all times to help deliver better public services.
Building on the work of No10 Data Science Team (10DS) and other leading technology teams across the civil service, i.AI will experiment and prove what is possible in improving the use of AI across the government. It was recently announced that i.AI will expand a further 40 roles to build a scale up function, which will take successful incubated projects into production.
You can see more about our work on ai.gov.uk
i.AI’s Impact and Evaluation team will build their work into each stage of product development, from scoping, to incubation, and onwards to scaling. Team members will embed into product development teams, designing and delivering of randomised control trials, impact evaluations, and qualitative studies primarily into the efficiency and quality impacts of our AI products.

Role Responsibilities

• Setting up and leading i.AI’s Impact and Evaluation function.
• Leading the design and implementation of evaluations for our AI products, including developing evaluation frameworks, identifying appropriate methodologies, and collecting and analysing data.
• Communicating the results of your team’s evaluations in a way that is engaging and supports delivery of the mission of i.AI
  managing and mentoring Impact and Evaluation Leads (3FTE), providing guidance and support to ensure high-quality work.
• Embedding Impact and Evaluation into AI products from the very start and throughout the development cycle.
• Leading the team to deliver wider AI evaluation throughout the development cycle. Experience in this is not required, as these are new and emerging methods that you would be key in forging practical delivery of: safety assessment, algorithmic transparency reporting, red teaming to assess AI bias and safety.
• Building evidence led and data driven cases for i.AI funding bids in processes such as Budgets or Spending Reviews.
• Developing data collection tools, such as surveys, interviews, and focus groups, and ensuring data quality.
• Conducting data analysis, drawing insights and recommendations and presenting them stakeholders.