Valérie Pisano

Présidente et cheffe de la direction, Équipe de direction

Biographie

Valérie Pisano est la présidente et cheffe de la direction de Mila – Institut québécois d’intelligence artificielle. Fondé par le professeur Yoshua Bengio, Mila est reconnu comme leader mondial d’avancées scientifiques qui inspirent l’essor de l’IA au bénéfice de tous.

Comptant près de 20 ans d’expérience en leadership, en stratégie et en transformation, elle a été cheffe de la direction du Talent au Cirque du Soleil et a cofondé le Projet Mobïus sur les biais, une initiative axée sur la diversité et le leadership au féminin.

Elle a commencé sa carrière chez McKinsey & Compagnie après avoir obtenu une maîtrise en économie à HEC Montréal. Elle siège aux conseils d’administration de Montréal International et de 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 … (voir 3 de plus)

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… (voir plus)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 (prépublication)

doi.org

arxiv.org

COVI White Paper-Version 1.1

H. Alsdurf

Yoshua Bengio

T. Deleu

Prateek Gupta

Daphne Ippolito

R. Janda

Max Jarvie