Understanding artificial intelligence

AI has the potential to change the way we live and work.

Embedding AI across all sectors has the potential to create thousands of jobs and drive economic growth. By one estimate, AI’s contribution to the United Kingdom could be as large as 5% of GDP by 2030.

In a specific Wales context, this could equate to a total impact of £7.9bn (9.8% of GDP) by 2030, according to PWC UK.

Read PWC UK’s report, The economic impact of artificial intelligence on the UK economy.

Different public sector organisations in the UK are already successfully using AI for tasks ranging from fraud detection to answering customer queries.

The potential uses for AI in the public sector are significant, but have to be balanced with ethical, fairness, safety and social partnership considerations.

At its core, AI is a research field spanning philosophy, logic, statistics, computer science, mathematics, neuroscience, linguistics, cognitive psychology and economics.

AI can be defined as the use of digital technology to create systems capable of performing tasks commonly thought to require human intelligence.

Generally speaking, there are 3 broad types of AI:

• perception: understanding or interpreting a situation, detecting or sensing.
• prediction: analysing a situation, forecasting an outcome.
• generation: using AI to create new content.

AI is constantly evolving, but generally it:

• involves machines using statistics to find patterns in large amounts of data
• is the ability to perform repetitive tasks with data without the need for constant human guidance

Check the AI glossary on the Alan Turing Institute. 

A subset of AI is machine learning: the development of digital systems that improve their performance on a given task over time through experience.

Machine learning is the most widely-used form of AI, and its contribution to innovations include self-driving cars, speech recognition, and machine translation.

Recent advances in machine learning are the result of:

• improvements to algorithms
• increases in funding
• huge growth in the amount of data created and stored by digital systems
• increased access to computational power and the expansion of cloud computing

Machine learning can be:

• supervised learning which allows an AI model to learn from labelled training data, for example training an AI model to help tag content on GOV.UK
• unsupervised learning which is training an AI algorithm to use unlabelled and unclassified information
• reinforcement learning which allows an AI model to learn as it performs a task

AI can benefit the public sector in a number of ways. For example, it can:

• provide more accurate information, forecasts, and predictions leading to better outcomes - for example, more accurate medical diagnoses
• produce a positive social impact by providing solutions for some of the world’s most challenging social problems
• simulate complex systems to experiment with different policy options and spot unintended consequences before committing to a measure
• improve public services - for example, personalising public services to adapt to individual circumstances
• automate simple, manual tasks which frees staff up to do more interesting work

AI is not a general purpose solution which can solve every problem. Current applications of AI focus on performing narrowly defined tasks. 

AI generally cannot:

• be creative
• perform well without a large quantity of relevant, high quality data
• infer additional context if the information is not present in the data

Even if AI can help you meet some user needs, simpler solutions may be more effective and less expensive. For example, optical character recognition technology can extract information from scans of passports. 

However, a digital form requiring manual input might be more accurate, quicker to build, and cheaper. You’ll need to investigate alternative mature technology solutions thoroughly to check if this is the case.

Check the guidance on choosing an appropriate technology on GOV.UK.

With an AI project, consider different factors including AI ethics and safety, legal,  administrative and Welsh-language concerns. 

The factors to consider include:

• data quality: the success of your AI project depends on the quality of your data
• fairness: are the models trained and tested on relevant, accurate, and generalisable datasets and is the AI system deployed by users trained to implement them responsibly and without bias
• accountability: consider who is responsible for each element of the model’s output and how the designers and implementers of AI systems will be held accountable
• privacy: complying with appropriate data policies, for example the General Data Protection Regulations (GDPR) and the Data Protection Act 2018
• explainability and transparency - so the affected stakeholders can know how the AI model reached its decision
• costs: consider how much it will cost to build, run and maintain an AI infrastructure, train and educate staff and if the work to install AI may outweigh any potential savings
• training: ensure that the end-user understands the limitations of the project - and how to effectively use it - are fundamental in ensuring the success of your project

Your AI system must comply with General Data Protection Regulation (GDPR) and the Data Protection Act 2018 (DPA 2018), including the points which relate to automated decision making. Discuss this with legal advisors.

Automated decisions in this context are decisions made without human intervention, which have legal or similarly significant effects on ‘data subjects’. 

For example, an online decision to award a loan, or a recruitment aptitude test which uses pre-programmed algorithms.

If you want to use automated processes to make decisions with legal or similarly significant effects on individuals, you must follow the safeguards defined in the GDPR and DPA 2018. 

This includes making sure you provide users with:

• specific and easily accessible information about the automated decision-making process
• a simple way to obtain human intervention to review, and potentially change the decision

Make sure your use of automated decision-making does not conflict with any other laws or regulations.

Consider both the final decision and any automated decisions which significantly affected the decision-making process.

Read the guidance on algorithmic management systems in the Welsh public sector from the Workforce Partnership Council.