Professor Gregory O'Hare:

On the suggestion made several times during the meeting about the need for perhaps an Oireachtas joint committee, I respectfully disagree with some of my colleagues. It should relate not to AI in the workplace but to AI in society. After all, some people are not sufficiently fortunate to have employment and I do not think we should disadvantage them. AI permeates every aspect of life, not simply the workplace.

On the surveillance point, while I do not like to state the obvious, many aspects of our everyday lives are currently monitored, recorded and stored. Every time you go through a toll gate, your car registration number is stored and the ownership of that car is known. Likewise, every time you pass a vehicle number plate registration camera, that information is stored. What is really significant, however, is not so much the surveillance but the purpose of the data being accrued as a result of the surveillance.

I might give one or two illustrative examples. Various applications that exist in the automotive industry allow persons to avail of a preferential driving insurance rate, but to do so, they have to adhere to certain driving conditions. They might not be able to drive on a motorway, perhaps, or they might be allowed to drive only below a certain speed, and onboard technology that already exists in all cars could monitor their compliance with those terms and conditions. As a result of that, they will, one hopes, drive more safely and, therefore, they will benefit from the dividend of a reduced car insurance premium. Sometimes surveillance can have a very positive result.

To take another example, some time ago in research, we looked at the collection of data from citizens. I will intentionally omit the names of the companies for all the obvious reasons. The committee is probably aware that in the US, private health insurance is almost mandatory but, in addition to that, a relatively limited number of health insurance companies compete in that space. One of these large companies was collecting data to try to give preferential health insurance rates to some of its customers. Clearly, these were people who were younger and more active and who allowed themselves to present in health-monitoring booths in large corporate employers on a six-monthly basis. Their data were collected and those data on their general wellness were used to calculate their insurance risk and the rate of their insurance payments, but it did not stop there. This company engaged in a corporate relationship with a large supermarket chain, and every time individual citizens went to the supermarket, all the data harvested at the cash register, pertaining to their eating habits and those of their family, were captured. That was then conflated with other health-recorded data to build up a picture of the risk associated with that individual and his or her family. Of course, if they were perceived to be low risk and ate only vegetables, for example, and no processed foods or whatever others might buy every week, they would receive a benefit, but if they happened to be less aware of what was going on, their health insurance would increase incrementally.

In summary, the surveillance of people, the collation of data from disparate, varied sources and the kind of additionality of information that can result from that combination of those data sources and how they are used are what we need to be exercised by.