Dr. Kris Shrishak:

I might have cited separate work that I have done on bias and AI systems. I need to emphasise that data is just one component of bias that goes into the system. There are things that we call algorithmic bias that often involve choices made by the designers of systems. Choices could also be not looking for certain issues. That is also a choice and there are logics in all of that. As far as I know, I would not use AI to prevent bias but what we can do, and that comes back all the way to the beginning of the development process, is look at who are the people involved in development and where is the data, for instance, coming from because in many of these systems the data that we are getting and is used are actually proxy data for what the actual concern is or what the system is being built for. That is the key thing. I would not use AI to fix it but there are various technical methods that take us to an extent to address the issue of bias in AI systems, and the kind of example that the Chair gave is one.

The example, for instance, was from the US. We have plenty of examples from the US. We have some examples in Europe as well. What we really need to look after when it comes to data is the representativeness of the data going into the AI system to where it is being deployed. If it is deployed in Ireland, it needs to represent people in Ireland, not just the EU overall.