EMPOWERING CITIZENS AGAINST COVID-19 WITH AN ML-BASED AND DECENTRALIZED RISK AWARENESS APP
Friday, May 8, 2020
Speaker : Professor Yoshua Bengio (Mila)
Recording: here
What is contact tracing? How can it help to substantially bring down the reproduction number (number of newly infected individuals per infected person)? How can automated contact tracing act as a complement to existing manual contact tracing? How can ML-based risk estimation generalize contact tracing, moving away from a binary decision about a contact to graded predictions capturing all the clues about being contagious, and how could it enable fitting powerful epidemiological models to the data collected on phones? What are the privacy, human rights, dignity and democracy concerns around digital tracing?
How can we deploy decentralized apps with the strongest possible privacy guarantees, thus delivering both on the side of saving lives by reducing greatly the reproduction number of the virus while making sure that neither governments nor other users can have access to my infection status or my personal data? How do we create trust in both directions and empower citizens with the information needed to act responsibly to protect their community, instead of relying on the authority of the government and the threat of social punishment?
How does it make the problem more challenging from a machine learning perspective because a lot of information is now not accessible or blurred to achieve differential privacy and avoid having a central repository tracking people’s detailed movements and who they met when? What machine learning techniques appear most promising to jointly train an inference machine which predicts contagiousness in the past and the present and at the same time train a highly structured epidemiological model which is a generative engine for running what-if policy scenarios and help public health take the difficult decisions ahead using the scientific evidence as well as the data being collected in a privacy-first way?
How do we set up a form of non-profit data trust which protects citizens and avoids conflicts of interest, keeping the collected data at arm’s length of governments but yet providing them with the information they need for taking policy decisions and managing the public health challenges. Many questions, and hopefully some early answers.