PROGRAMME 8
DEVELOP PUBLIC SECTOR CAPACITY FOR AI ADOPTION
Image: Banana / Plant / Flask - Max Gruber / Better Images of AI
FIRST 100 DAYS
3.2 Expand on our AI CivTech Challenge on ethical and explainable AI in the public sector
YEAR 1
3.7 Reach agreement on the development of a public sector AI Charter (including a mechanism for feedback from the public)
YEAR 2 AND BEYOND
2.16 As part of the Digital Strategy, accelerate the use of common digital and data standards across the public sector
3.9 Create a register of trusted algorithms used in the Scottish public sector, learning from best practice around the world
3.10 Improve the capacity of the public sector to adopt AI through innovative procurement, support of CivTech and technology pilots
Public sector Data Transformation Framework
Before an organisation reaches the stage at which it can make safe and effective use of AI, it has to go on a journey where it will put in place the fundamentals required, in particular with regards to data quality and standards. Therefore we are developing a Data Transformation Framework to increase awareness and understanding of what data maturity means for public sector organisations. This will enable those organisations to put in place the data foundations required to improve service delivery and be more transparent and innovative.
As part of this, we are delivering the first Data Maturity Assessment cohort project, which is scheduled to complete at the end of April 2022. A number of public sector organisations in Scotland are involved in this first cohort: six Local Authorities, Health & Social Care Partnership and Transport Scotland. As of March 2022:
the first cohort has completed assessments, carried out analysis of strengths and weaknesses and currently developing data improvement action plans
planning is underway for the second cohort to be launched in late Summer 2022, with plans to scale the Data Maturity Programme with follow-on cohorts
We are also developing public sector personas to enable the Framework maturity pathways for public sector organisations and individuals:
phase 1 persona development completed Summer 2021
phase 2 of this project starts in March 2022, and will include needs, behaviours, scenarios and challenge led journeys to populate the Framework
development of Framework and pathways working with Data Maturity cohort (current & future)
AI best practice in the public sector
Working with public sector organisations on their data and AI journey has changed our initial thinking around developing a public sector AI Charter as was done in New Zealand. While the principles and goals associated with Trustworthy, Ethical and Inclusive AI are now well understood, the real challenge is around their implementation in practice – and there is a global shortage of exemplars to follow. So we have decided to adopt a bottom-up approach and co-design with those organisations what the best practice should be based on lessons learned from concrete use cases. Once this is work has progressed sufficiently, we will re-consider whether an AI charter would be helpful in formalising those.
Giving the citizens of Scotland trust and agency over how AI and algorithms are used in the public sector is one of these fundamental practical challenges, and we launched accordingly in June 2021 a CivTech Challenge on this topic, with a specific focus on children. Following a successful accelerator phase, we signed in March 2022 a Pre-Commercial Agreement with Finnish start-up Saidot, to develop the processes and tools required, including a register of trusted algorithms used in the Scottish public sector. As in all of our work, we are keen to not reinvent the wheel and instead influence the development of widely adopted best practice and standards, and have been collaborating with the Cabinet Office to pilot the UK Government Algorithmic Transparency Standard as part of this project.
Explainable AI is a prerequisite of public trust and agency, and we also building upon the work done during the accelerator phase of our ethical and explainable AI CivTech challenge with the startup MindFoundry, and applying it to the same children use case that we are working on with Saidot.