Valérie Pisano

President and CEO, Leadership Team

Biography

Valerie Pisano is President and CEO of Mila – Quebec Artificial Intelligence Institute. Founded by Professor Yoshua Bengio, Mila is recognized as a global leader in scientific advances that inspires innovation and the development of AI for the benefit of all.

Valerie has 20 years of experience in leadership, strategy and transformation including Chief Talent Officer at Cirque du Soleil as well as cofounder of The Mobïus Bias Project, an initiative focused on diversity and female leadership.

She began her career at McKinsey & Company after a Master Degree in Economics at HEC Montreal, and is a Board Director at Montreal International and at Chartwell.

Publications

COVI White Paper - Version 1.1

Hannah Alsdurf

Yoshua Bengio

Tristan Deleu

Prateek Gupta

Daphne Ippolito

Richard Janda

Max Jarvies

Tyler Kolody

Sekoul Krastev

Tegan Maharaj

Robert Obryk

Dan Pilat

Nasim Rahaman

Jean-François Rousseau

Abhinav Sharma

Brooke Struck … (see 3 more)

Jian Tang

Martin Weiss

Yun William Yu

The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essen… (see more)tial tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.

2019-12-31

arXiv (preprint)

doi.org

arxiv.org

COVI White Paper-Version 1.1

H. Alsdurf

Yoshua Bengio

T. Deleu

Prateek Gupta

Daphne Ippolito

R. Janda

Max Jarvie