A major issue facing companies that use AI, algorithmic bias can perpetuate social inequalities — as well as pose legal and reputational risks to the companies in question. New research at HEC Paris offers a statistical method of tracking down and eliminating unfairness.
By Christophe Pérignon
With online shopping, loyalty programs, smart devices and many other aspects of business and daily lives, companies collect vast amounts of our personal data. The risk is that they may be leaked or misused. A team of researchers designs measures that regulators and companies can undertake to preserve consumer privacy.
By Ruslan Momot
Investor uncertainty plays a key role in economics, affecting asset prices and investment decisions. Getting a useful measure can be important to financial professionals and also government actors, to establish monetary policy. An HEC Paris researcher and two economists of the US Federal Reserve’s Board of Governors found a new way to gauge uncertainty: using data on internet clicks related to specific news.
By Thierry Foucault
Many call for a systematic publication of scientific negative results in order to make the production of scientific knowledge more efficient. Raphaël Lévy, Assistant Professor in the Economics and Decision Sciences Department, explains why such dissemination of knowledge may actually be beneficial, but also points to potential undesired consequences.
Legal Data Mining Conference gathered professionals and academics from the technology, Artificial Intelligence and Law fields to discuss the future of Law. The two-day workshop focused on both the fundamental and practical issues of legal data mining. The event was organized by David Restrepo Amariles (Assistant Professor of Law at HEC Paris) and Ken Satoh (Professor at the National Institute of Informatics of Japan) in March 2019 at the Barreau de Paris.
While scientific findings need to be assessed by peers and journal referees, the confidentiality of original data often makes the process arduous. An accredited organization launched by Christophe Pérignon (HEC Paris) and colleagues with access to the original research data can now ensure reproducibility of results. This not only promises huge gains in time and effort for researchers but will also shore up trust in scientific results.
Based on his recent research and work for the EU Commission, "Unlocking Privately-Held Data to Create Public Value", Alberto Alemanno, Professor of European Union Law and Regulation at HEC Paris, discusses the concept of "Data for Good", whereby private data such as those collected by social media, telecoms and banks, can be shared with public authorities so as to save lives, such as in natural disasters, and improve them through better designed and more effective policies.
In the following interview, two researchers at HEC Paris, Professor of Marketing Peter Ebbes and CNRS Research Professor Emmanuel Kemel, explain how they use the web apps R and Shiny*, to visualize results of data analysis, to help students in understanding the formulas used for those analysis, and to help disseminate the research among academics and practitioners.
Hugues Langlois, Assistant Professor of Finance at HEC Paris, shares the practical applications of his research about big data methodologies in finance, and how it provides a new tool to measure expected returns in international stock markets. A big step in the world of finance for asset managers and risk managers.
By Hugues Langlois-Bertrand
This special issue of Knowledge@HEC highlights several research projects and teaching initiatives at HEC Paris in the context of big data and business analytics. Nowadays it does not take much to convince students or managers alike of the importance of data for businesses. As Wedel and Kannan (2016) put it, “data is the oil of the digital economy”. Indeed, data is completely transforming organizations, and data-driven decision making is becoming more and more a part of a company’s core. In an increasing digital world, all of us are walking data generators, leaving long data trails: we have more data on everything.
