15 May 2024
by Gareth Workman, Suzanne Brink

A three-step approach for businesses adopting AI

Guest blog from Gareth Workman and Suzanne Brink at Kainos as part of our #UnleashInnovation campaign week 2024.

Since the 30th of November 2022, the day OpenAI launched ChatGPT, the AI race has been on. The pace of development and capability has been phenomenal. Never have we seen another technology developing at such a pace. As with the launch of public cloud which democratised access to large scale compute, access to AI is easier than ever, with only a credit card being needed to start using the vast array of AI services.  

Many organisations and individuals have experimented, some with great results, but the majority are still seeking their eureka moment. 

 A government report states that around “15% of all businesses have adopted at least one AI technology, which translates to 432,000 companies”. So, there are still a lot of organisations out there that are yet to take the leap.  

Keeping pace with the unrelenting advances in AI makes it difficult to comprehend and apply in an organisational context. Coupled with the fear of doing the wrong thing even when the intention is to do the right thing, it can be hard to know where to start. Two reports, published early in 2024, reveal that AI adoption has slowed in some cases, “as business and IT leaders consider the legal and practical ramifications”.  

Responsible AI is a term that has gained prominence, at its core is the idea of making sure that AI usage is ethical and safe. But what does it mean to do the right thing? Whilst not exhaustive, we believe the following three steps are important to consider in the adoption and application of AI. 

 1) Setting a North Star  

As an organisation it’s important to work out your goals and values around AI. What are you hoping to achieve? What are your non-negotiables in terms of human and environmental impact?  

There is power in creating a code of ethics that captures the values and principles that AI use in your organisation should adhere to. New use cases can be held against this code and those cases that are ambiguous can be discussed via an ethics board or committee. Supplement this with an AI usage policy that explains the hard do's and don'ts in language that your people can understand and then give people the freedom to experiment within the guardrails. 

 2) Guide the development AI comes  

with inherent risks that need to be managed. When algorithms are trained on biased data, they become biased. AI models can be complex and multi-layered, making it difficult to understand how a specific conclusion was reached. Human oversight and accountability are needed to avoid scenarios where decisions are made without those impacted being able to understand or contest it. When developing and deploying AI solutions, teams need to manage such risks. Bias can be limited by ensuring representative training data, together with testing models for fairness and continual monitoring. Human oversight should be defined before a model is brought to deployment. Foreseeable harms should be identified and mitigated from the very beginning of the AI development life cycle, for example via an algorithmic impact assessment or harms workshops.  

The multitude of actions needed to make AI ethical and safe can best be covered in the form of a framework that guides your AI development teams in a step-by-step manner. This makes it digestible for them to embed an ethics-by-design approach.  

3) Scale the approach  

To truly mature and unleash the opportunity of AI at scale, adherence to your code of ethics, AI usage policy and AI development framework should be monitored. It should be embedded in the wider governance approach. Often, this does not require introducing a range of completely new methods, tools and processes. Instead, existing processes such as those that manage security, data protection or data governance can be extended to also manage AI risk at an enterprise level. 

To get AI governance set up, it is key to ensure accountability is allocated at an enterprise level and to get the right people involved. With responsible AI principles and themes making their way into UK regulation and EU law, one of the groups you must involve is your legal team.  

The AI journey can be daunting and exciting at the same time, we encourage you to take it a step at a time. Working through the above three steps will help you to navigate the journey and do your version of the right thing. Lean on experienced partners to help you where you can, it can make all the difference. If you’d like to find out how Kainos can accelerate your AI journey and assist you on embedding a responsible AI approach, contact us today.


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Laura Foster

Laura Foster

Head of Technology and Innovation, techUK

Rory Daniels

Rory Daniels

Programme Manager, Emerging Technologies

Tess Buckley

Tess Buckley

Programme Manager - Digital Ethics and AI Safety, techUK

Usman Ikhlaq

Usman Ikhlaq

Programme Manager - Artificial Intelligence, techUK

Related topics

Authors

Gareth Workman

Gareth Workman

Kainos

Suzanne Brink

Suzanne Brink

Kainos