AI Explainability and Tech Trust & Safety

Presenter: Theodoros Evgeniou, INSEAD
Date: 19 December 2022


Discussions about trustworthy and responsible AI have become central across multiple communities in recent years - machine learning, law, social sciences, among others. A key challenge regarding trust in AI - also considered important by regulators as part of transparency for some AI applications - is to understand why “black boxes” may be making specific predictions. As a result, explainable AI (XAI) has been a growing topic of research. In this talk, I will discuss some potential drawbacks XAI may have - including the potential to erode safety in practice - and also present some work that takes into account behavioural aspects researchers and practitioners may need to consider when developing XAI.

Speaker bio

Theos Evgeniou is a professor of Decision Sciences and Technology Management at INSEAD and director of the INSEAD Executive Education program on Transforming your Business with AI.

He has been working on Machine Learning and AI for the past 25 years, on areas ranging from AI innovations for business process optimization and improving decisions in Marketing and Finance, to AI regulation, as well as on new Machine Learning methods. His research has appeared in leading journals, such as in Science Magazine, Nature Machine Intelligence, Machine Learning, Lancet Digital Health, Journal of Machine Learning Research, Management Science, Marketing Science, Harvard Business Review magazine, and others.

Professor Evgeniou is a member of the OECD Network of Experts on AI, an advisor for the BCG Henderson Institute, an advisor for the World Economic Forum Academic Partner for Artificial Intelligence, and together with three INSEAD alums also a co-founder of Tremau, a B2B SaaS company whose mission is to build a digital world that is safe & beneficial for all. He gives talks and consults for a number of organisations in his areas of expertise, and in the past he has been involved in developing hedge fund strategies with more than $100 million invested. He 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 degree in Computer Science.