Digital Policy Salon: Our Virtual Identities

Joni: The PCTF 1.0 is a first of its kind framework, as it has been developed in collaboration with the public and the private sectors to be built around people and the organizations that they represent.

It is the first of its kind for that kind of collaboration. It is the first of its kind also because it is focused on the economic and social-benefits drivers that are important for Canadian prosperity. The framework provides structure and guidance for assurance practices in the verification of information of people, organizations, and the relationships between all parties. The framework itself is technology and model neutral. The framework references different technologies based on the kind of risk profile that is associated to the transactions that would be performed. So, the framework has application, whether you are looking for a classical federated network or whether you are looking at a permissioned network or an operator network like Interac or the Verified. Me solution. And it also provides application to distributed models such as self-sovereign identity. The framework is most powerful for the verification of assurance that information is valid and subsequently how that information gets into a different technology stack.

Joni: Our conservative estimates suggest digital IDs can unlock 3% to 6% of GDP. That is around $48 billion to $97 of unrealized potential. What is the basis of the estimate? It is made up of reduced fraud coupled with process and transaction efficiencies accrued. For example, with secure and privacy-respecting digital ID, the efficiencies of a tell-us-once approach could be fully realized. Pre-pandemic, the most talked about use cases involved conveniences that could be realized through strong identity services for cybersecurity and frictionless identity for consumer interactions with businesses. The pandemic has changed the conversation by bringing forward the importance of identity services for the remote- and tele-workforce. Remote and tele-workforce capabilities have significant economic impact while improving national security protection. Ensuring the economy can adapt in trying times, like the COVID-19 pandemic, is part of the promise of a PCTF.

Dr. McCradden: Originally, I think many thought that the errors AI would make would be very obvious and easily dismissed by clinicians with the help of explainability. Recent research testing the use of explainability solutions has shown that sometimes an explanation might persuade a clinician to change their mind even when that explanation is wrong. This is particularly evident when there is medical uncertainty. In many ways this makes sense. If you’re a clinician and you’re interpreting an image, for example, and it’s really obvious that the AI system is wrong about there being a presence of an abnormality, you’re going to quickly reject that prediction. But if you’re not sure yourself and you see an explanation that seems convincing, it may seem reasonable to go with the computer output because it might be picking up on something that you are missing. Sometimes it is reasonable, and it is through research that we can demonstrate whether, how, and in what context the AI system is better than the human eye. But over-trust can also lead to unintended consequences, so we’re doing research to try and figure out how we should go about balancing the need for understanding of the AI tool with the desire to avoid this new form of bias.

We want explanations for the purposes of informed consent as well. Ethically, clinicians must provide their reasoning for medical recommendations to support patient and substitute decision-making. But clinicians don’t need all the information about a model to do this. For example, they don’t need to know all the technology behind how an MRI generates images to explain the benefits and risks of MRIs to a patient. The same is true for AI, but we haven’t quite sorted out exactly what information does need to be provided in a given case. I think this is a key place for engagement between computer scientists, patients, parents, clinicians, and regulators to sort this out and I hope our research will support that discussion.