Welcome to the What is.. series? Bite size blogs on all things AI.
In this instalment we explore the what, where and why of Responsible AI. What is it? Where is it used? Why is it important?
Responsible AI is the practice and process used by an organisation to develop and deploy AI in a measured approach that accounts for the ethical, security, legal and cultural challenges that may result from the failure of AI. For a definition of AI please see the “What is … AI?” Post.
Responsible AI covers the application of a governance framework to each stage of the ML Lifecycle. Key points that this aims to address include:
Currently the main areas of focus for Responsible AI relate to Bias Fairness, and Explainability. This is due to the requirement to ensure that AI models that have been trained on ideally representative datasets, are not disproportionately impacting specific groups of people or individuals. By understanding how the model responds to different data ranges, and how it makes decisions it is possible to promote trust in the use of systems that are built on Responsible AI.
Responsible AI is increasingly being used in institutions that needs to implement AI in a manner that impacts society and people, but also in sectors that have regulatory requirements and are looking to use AI that can generate decisions that can impact the welfare of individuals or groups of people.
Responsible AI is therefore being adopted more in sectors where its use falls into higher risk categories. These include:
The main reason Responsible AI is important is that it ultimately protects the organisations that use or create AI, and also the people that are impacted by Ai driven decisions. Leaders in AI are looking to adopt Responsible AI as a process by which they can demonstrate that they are addressing some of the fundamental concerns, issues and limitations associated with AI. Not just on a technical basis but also in a way that aligns its use with a company’s culture and ethical responsibilities.
Responsible AI will also be necessary where there is a need to demonstrate AI systems and organizational practices fulfil the requirements of agreed-upon regulations, standards, best practices, and laws (such as the EU AI Act). With the advent of new regulations, directives, and principles for how to use AI being released in different regions, having a demonstrable governance framework that looks to address the risks of AI and provide trust, will go from being optional to a requirement.
Responsible AI is also a step by which organisation can protect themselves from legal issues, reputational damage, and many more risks associated with the automation of decision making.
Advai is a deep tech AI start-up based in the UK that has spent several years working with UK government and defence to understand and develop tooling for testing and validating AI in a manner that allows for KPIs to be derived throughout its lifecycle that allows data scientists, engineers, and decision makers to be able to quantify risks and deploy AI in a safe, responsible, and trustworthy manner.
If you would like to discuss this in more detail, please reach out to contact@advai.co.uk
If this topic interested, you please check out some of the below resources for more information:
Here are a few links that you might find useful if you are interested in learning more about AI:
The OECD's AI Policy Observatory https://www.oecd.org/ai/policy-observatory/
