From public data to responsible A.I

César Hidalgo

Viernes 26 de abril de 2019, 17:00 horas

Auditorio – C3, entrada libre.

Abstract:

In recent years advances in big data and algorithms have given rise to a world in which it is finally possible to include algorithmic decision making in the decision pipelines of governments and businesses. But how should governments and companies organize and communicate their data? How and when should they include A.I. in their decision-making process? And how will their employees and customers react to the inclusion of A.I. in their organizations? In this presentation I will discuss recent advances in the creation of data integration, distribution, and visualization tools designed to augment the decision-making pipelines of organizations. These tools include A.I. concepts and techniques that have become relevant for our understanding of economic development, and have open the door to new techniques to collect and distribute public data. I show how these tools are paving the way for A.I. to become an integral part of an organization, and conclude by showing empirical work documenting differences on how people judge human and A.I. actions.

Bio:

César A. Hidalgo is a Chilean physicist and author. He is currently the head of MIT’s Collective Learning group. Prior to joining MIT, César was a research fellow at Harvard. He received his PhD in Physics from the University of Notre Dame and his undergraduate degree from Universidad Católica de Chile.

Professor Hidalgo is a multidisciplinary scholar with experience in Data Science, Artificial Intelligence, and Complex Systems. He has made contributions to the fields of economic development, economic geography, urban computing, data visualization, and collective memory. His contributions to economic geography and economic development include work documenting the path dependencies that govern the process of economic development (Hidalgo et al. Science 2007, Jara-Figueroa et al. PNAS 2018), and the creation of measures of economic complexity that explain and predict differences in income, economic growth, and income inequality (Hidalgo & Hausmann, PNAS 2009, Hartmann et al. 2017). His urban computing work pioneered the use of crowdsourcing and computer vision methods to measure the physical quality of streetscapes and their implications (Salesses et al. Plos One 2013, Naik et al. EECV 2014, AER 2016, & PNAS 2017). His collective memory work has documented the role of language translations (Ronen et al. PNAS 2014) and communication technologies in collective memory (Jara-Figueroa et al. Plos One 2019) and has documented empirical laws of memory decay (Candia et al. Nat. Comm. 2018). His data visualization work includes the creation of numerous platforms that integrate, distribute, and visualize large volumes of public data, including The Observatory of Economic Complexity, Pantheon, DataUSA, DataAfrica, and DataChile, among others.

Hidalgo’s contributions have been recognized with numerous awards, including the 2018 Lagrange Prize, three Webby Awards, and the Bicentennial Medal of the Chilean Congress, among others. Hidalgo is the author of Why Information Grows (Basic Books, 2015), which has been translated to over ten languages, and an author of The Atlas of Economic Complexity (MIT Press, 2014). Hidalgo is also a founder of Datawheel LLC, a company that has professionalized the creation of large data visualization engines.