Hey everybody, our guest today is Dr. Norman Fenton who is a professor at risk information

management at Queen Mary University of London. He's a mathematician by training with a current

focus on critical decision-making and in particular on quantifying uncertainty using causal

probabilistic models that combine data and knowledge. And it's known as that system is known

as the base in networks. This approach can be summarized as smart data rather than big data.

It has applications in law and forensic. He's been an expert witness in major criminal and civil

cases, health, security, software, reliability, transport safety and reliability and finance and

support protection. His studies are available at NormanFenton.com and I wanted to have him because

he's had a, he's done a lot of work on the data that it's applicable to the COVID pandemic and he

has been very, very critical from an academic and so astic viewpoint of the government policies

and the use of data which is something that has been a preoccupation. I think of all of us who've

been following the countermeasure. So Dr. Fenton, your group at Queen Mary University of London has

applied itself to a range of challenges and when the COVID pandemic emerged, what kind of challenges

did you specifically see? Well, first of all, I mean, it was clear, I think from the start that

most of the data that governments put out, not just the UK government, but most governments around

the world about COVID and then later about the vaccines was kind of misleading because it was

based on very easily manipulated statistics. So initially, we saw there was an immediate rush to

draw conclusions which were sort of based on oversimplistic data on case numbers and deaths.

When instead there, we believe there was a need to look for causal explanations in the observed

data because that's the kind of work that we do. So the problem was that that data was very easily

used by influencers and decision makers to fit particular narratives that exaggerated the scale

of the crisis. So for example, people were concluding early on that neighboring countries in

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