Ms Áine Kerr:

There are a couple of levels. On the technology level, we require fundamental rewiring of the recommendation or algorithm I talked about in terms of transparency. This is so people will understand why they are seeing certain recommendations. They should have the ability to reset their preferences. We need to accelerate our AI machine-learning efforts ultimately to be preventive and remove bad actors who are trying to spew confusion. There is a human layer on top, whether it is through the fact-checkers and efforts like First Draft, which I mentioned, whereby one is ultimately accelerating the efforts of the fact-checkers but ensuring the fact check travels back to those who might have viewed something false. A problem with many of the fact-checking efforts mentioned earlier is that they often occur long after the viral peak window. We are not capturing the people in the first 12 hours when there is peak virality. Therefore, we need to build systems and technology that identifies and goes back to the people and asks them whether they have seen a certain other perspective, which is from a trustworthy source. On that, there is a considerable amount of work happening across the industry on trust metrics and nutrition labels. We keep talking about transparency today. What does it look like if one can click on a little icon across multiple platforms or websites that indicates who owns information and how long the owner has been up and running. Thus, people can engage in critical thinking to determine whether they want to trust the information.