What is Robust AI

To fully understand Robust AI we must understand the interplay between Robustness and Resilience too, and how to balance the trade off. Robustness is solid and brittle behaviour, while Resilience is malleable and flexible behaviour.

Developing robust AI systems requires addressing a number of technical challenges, such as ensuring that the AI system is able to adapt to changing environments and handle unexpected inputs and ensuring that it is able to continue to function even if some of its components fail. In addition, robust AI systems must be designed in a way that is transparent and explainable, so that it is clear how they are making decisions and why.

Robust AI can be used in a wide range of industries and applications and should apply to all industries, but it is most critical in areas where there are often unexpected or changing condition, and it is related to mission critical implementations.

1. Autonomous Vehicles
2. Healthcare
3. Finance
4. Policing and Security
5. Manufacturing Control Systems

These are just a few examples of where robust AI might be used. In general, robust AI can be applied in any context where it is important for AI systems to be able to function effectively and adapt to changing conditions.

Robust AI is important because it can help to ensure that AI systems are reliable and able to function effectively even when faced with unexpected or changing conditions. This is crucial for critical applications where the failure of an AI system could have serious consequences, such as in healthcare, transportation, or finance.

In addition, robust AI can help to build trust in AI by ensuring that AI systems are transparent and explainable, and that their decision-making processes are understandable. This helps build confidence and trust in AI and contributes to the responsible deployment of these systems. From a policy and regulation perspective this also provides a clearer view of boundaries of safe operation which allows companies to better understand how a system deals with critical failure and how to mitigate the issues.

Overall, robust AI is an important area of research and development that is focused on ensuring that AI systems are reliable, adaptable, and able to function effectively in a wide range of environments.

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

Here are a few links that you might find useful if you are interested in learning more about Robust AI:

1. https://www.ibm.com/cloud/learn/strong-ai
2. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems: https://standards.ieee.org/industry-connections/ec/autonomous-systems.html
3. The Association for Computing Machinery's Special Interest Group on Artificial Intelligence: https://www.acm.org/sigs/special-interest-groups/ai