Thursday, February 18, 2021 – 12:00 – 13:00




Theodoros Evgeniou – INSEAD

Abstract: Since the development of the early recommender systems, in the mid 90s, a lot of AI research has been focusing on developing machine learning algorithms to better understand human behaviour – with mega-marketing firms such as Google or Facebook driving part of the research agenda.  Very recently the discussion, largely also driven by regulators, has been shifting towards understanding what these algorithms do (e.g., AI explainability) and how they may impact human behaviour – and society – instead. For example, we see new research directions and regulations towards fair, accountable, and transparent AI, while regulators now consider how to manage (“new”) AI risks – a couple of decades after the AI community developed massive commercial AI systems. Research questions now shift from developing algorithms to understanding the “humans and machines system”: it is not only about developing algorithms to analyse human behaviour data (e.g., what products, movies, or ads people like, what reviews they write, how they connect with others, trade, drive, etc), but also about developing algorithms to understand AI algorithms themselves (e.g., explainability methods) and, more broadly, methods to understand how humans and machines co-evolve – affecting each other. This talk will be about algorithms to understand human behaviour, algorithms to understand machine behaviour, and regulatory and research questions to explore about how humans and machines may best work together and co-evolve while considering both AI opportunities and risks.


BIO Theodoros Evgeniou – INSEAD

Theos Evgeniou is a Professor of Decision Sciences and Technology Management at INSEAD, and was Academic Director of INSEAD eLab, a research and analytics center at INSEAD that focused on data analytics for business.

Professor Evgeniou has received four degrees from MIT, two BSc degrees simultaneously, one in Computer Science and one in Mathematics, as well as a Master and a PhD degrees in Computer Science. He graduated first in the MIT class of 1995 dual degrees in Mathematics, won medals in international mathematical Olympiads, and European awards for business case studies.

He has authored several academic and business articles which have been cited by more than 10000 other publications. At INSEAD, Theos has been focusing on data analytics (and “Big Data”) applied to a range of areas from customer insights and marketing to finance. He has been developing and teaching courses on Data Analytics, Statistics and Decision Making. Professor Evgeniou gives talks and consults for a number of organisations in his areas of expertise. He recently developed a novel Data Analytics for Business course for MBA and Executive Education participants, which is based on cloud technologies and state of the art open source analytics tools.