IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: Francis de Véricourt
2026-02-06 10:00 - 11:30 (Asia/Singapore)
Name of Speaker

Francis de Véricourt

Schedule

6 Feb 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-2

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/KcwXsVRZSI2rLe4DvkXNFQ

Title

Beyond the Black Box: Unraveling the Role of Explainability in Human-AI Collaboration

Abstract

Explainable Artificial Intelligence (AI) models have been proposed to mitigate overreliance and underreliance on AI, which reduce the effectiveness of human-AI collaborative tools. Yet, empirical evidence is mixed, and the impact of explainable AI on a decision-maker (DM)’s cognitive load and fatigue is often ignored. This paper offers a theoretical perspective on these issues. We develop an analytical model that incorporates the defining features of human and machine intelligence, capturing the limited but flexible nature of human cognition with imperfect machine recommendations. Crucially, we represent how AI-based explanations influence the DM’s belief in the algorithm’s predictive quality. Our results indicate that explainable AI has varying effects depending on the level of explainability it provides. While low explainability levels have no impact on decision accuracy and reliance levels, they lessen the cognitive burden of the DM. In contrast, higher explainability levels enhance accuracy by improving overreliance but at the expense of increased underreliance. Further, the relative impact of explainability (c.f. a black-box system) is higher when the DM is more cognitively constrained, the decision task is sufficiently complex or when the stakes are lower. Importantly, higher explainability levels can escalate the DM’s cognitive burden and hence overall processing time and fatigue, precisely when explanations are most needed, i.e. when the DM is pressed for time to complete a complex task and doubts the machine’s quality. Our study elicits comprehensive effects of explainability on decision outcomes and cognitive effort, enhancing our understanding of designing effective human-AI systems in diverse decision-making environments.

About the Speaker

Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin. He also holds the Joachim Faber Chair in Business and Technology, and is the co-author of Framers, a Penguin Random House book listed on Financial Times’ Best Books. He lived and worked in France, USA, Germany and Singapore.

Francis was the first Associate Dean of Research and holder of the President’s Chair at ESMT Berlin. He held faculty positions at Duke University and INSEAD, where he was the Paul Dubrule Chaired professor in Sustainable Development, and was a post-doctoral researcher at Massachusetts Institute of Technology (MIT). His general research interest is in the area of decision science, analytics and operations, with applications in health care, sustainability and human-AI interaction. He is the author of numerous academic articles in prominent management, analytics and economics journals such as Management Science, Operations Research, American Economics Review and others. He received several outstanding research awards and is currently an Area Editor at Operations Research.

Francis has been the recipient of many teaching awards for delivering classes to MBA and Executive MBA students at ESMT and INSEAD. He has extensive experience in executive education and corporate learning solutions, and is a regular speaker in academic and industry forums.

DAO-ISEM-IORA Seminar Series: Georgina Hall
2026-02-27 10:00 - 11:30
Name of Speaker

Georgina Hal

Schedule

27 Feb 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/MSVeTEDGTSGxi0TGgyLmNg

Title

Sum of Squares Submodularity

Abstract

We introduce the notion of t-sum of squares (sos) submodularity, which is a hierarchy, indexed by t, of sufficient algebraic conditions for certifying submodularity of set functions. We show that, for fixed t, each level of the hierarchy can be verified via a semidefinite program of size polynomial in n, the size of the ground set of the set function. This is particularly relevant given existing hardness results around testing whether a set function is submodular (Crama, 1989). We derive several equivalent algebraic characterizations of t-sos submodularity and identify submodularity-preserving operations that also preserve t-sos submodularity. We further present a complete classification of the cases for which submodularity and t-sos submodularity coincide, as well as examples of t-sos-submodular functions. We demonstrate the usefulness of t-sos submodularity through three applications: (i) a new convex approach to submodular regression, involving minimal manual tuning; (ii) a systematic procedure to derive lower bounds on the submodularity ratio in approximate submodular maximization, and (iii) improved difference-of-submodular decompositions for difference-of-submodular optimization.
This is joint work with Anna Deza (Georgia Tech).

About the Speaker

Georgina Hall is an Assistant Professor at INSEAD in the Decision Sciences Area. Her research focuses on convex relaxations of NP-hard problems, particularly those that arise in polynomial optimization and problems on graphs. Prior to joining INSEAD in 2019, she was a postdoctoral student at INRIA. She completed her PhD in Operations Research and Financial Engineering at Princeton University in 2018. She is the recipient of the 2018 INFORMS Optimization Society Young Researcher’s Prize and the 2020 Information Theory Society Paper Award, among other awards.
