I am a Research Associate at the Leverhulme Centre for the Future of Intelligence, University of Cambridge. I currently explore the capabilities of AI systems (primarily large language models) with the aim of mapping these capabilities onto the demands of occupational tasks. This research is carried out in collaboration with the OECD and experts in occupational psychology.

Previously, I was a Royal Academy of Engineering UK IC postdoctoral research fellow investigating the impact of explanations of AI predictions on our beliefs. I also studied people’s causal and probabilistic reasoning and have a strong interest in data analysis, causal modeling and Bayesian network analysis.

I received a Ph.D. in Psychology from Birkbeck’s Psychological Sciences department, an M.A. in Logic and Philosophy of Science from the Munich Center for Mathematical Philosophy, LMU and a B.A. in Philosophy from University of Belgrade, Serbia. See my CV for more info on my background, research and work experience.

I play the violin in Paprika: The Balkan and East European band.

Publications

Testing the maximum entropy approach to awareness growth in Bayesian epistemology and decision theory

Testing the maximum entropy approach to awareness growth in Bayesian epistemology and decision theory

Applying the Maximum Entropy approach to awareness growth in the Bayesian framework, i.e. incorporating new events that we previously did not consider possible.

The impact of explanations as communicative acts on belief in a claim: The role of source reliability

The impact of explanations as communicative acts on belief in a claim: The role of source reliability

Investigating the effects of (good) explanations and the explainer’s reliability on our beliefs in what is being explained.

Argument and explanation

Argument and explanation

We bring together two closely related, but distinct, notions: argument and explanation. We provide a review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial.

Can counterfactual explanations of AI systems’ predictions skew lay users’ causal intuitions about the world? If so, can we correct for that?

Can counterfactual explanations of AI systems’ predictions skew lay users’ causal intuitions about the world? If so, can we correct for that?

We explore some of the undesirable effects of providing explanations of AI systems to human users and ways to mitigate such effects. We show how providing counterfactual explanations of AI systems’ predictions unjustifiably changes people’s beliefs about causal relationships in the real world. We also show how health warning style messaging can prevent such a change in beliefs.

On the transferability of insights from the psychology of explanation to explainable AI

On the transferability of insights from the psychology of explanation to explainable AI

A discussion of the consequences of directly applying the insights from the psychology of explanation (that mostly focuses on causal explanations) to explainable AI (where most AI systems are based on associations).

Explanation in AI systems

Explanation in AI systems

What do we do with our existing models when we encounter new variables to consider? Does the order in which we learn variables matter? The paper investigates two modeling strategies and experimentally tests how people reason when presented with new variables and in different orders.

The propensity interpretation of probability and diagnostic split in explaining away

The propensity interpretation of probability and diagnostic split in explaining away

Empirical testing of the effects of the propensity interpretation of probability and ‘diagnostic split’ reasoning in the context of explaining away.

Widening Access to Bayesian Problem Solving

Widening Access to Bayesian Problem Solving

An experimental exploration of whether a Bayesian network modeling tool helps lay people to find correct solutions to complex problems.

Sequential diagnostic reasoning with independent causes

Sequential diagnostic reasoning with independent causes

What do we do with our existing models when we encounter new variables to consider? Does the order in which we learn variables matter? The paper investigates two modeling strategies and experimentally tests how people reason when presented with new variables and in different orders.

Explaining away: Significance of priors, diagnostic reasoning, and structural complexity

Explaining away: Significance of priors, diagnostic reasoning, and structural complexity

Investigating people’s reasoning in explaining away situations by manipulating the priors of causes and the structural complexity of the causal Baeysian networks.

Confirmation and the Generalized Nagel-Schaffner Model of Reduction: A Bayesian Analysis

Confirmation and the Generalized Nagel-Schaffner Model of Reduction: A Bayesian Analysis

Analyzing confirmation between theories in cases of intertheoretic reduction (e.g. reducing thermodynamics to statistical mechanics) using Bayesian networks.

Projects

(Causal) Bayesian modeling of investment factors and Environmental, Social and Governance (ESG) criteria
As part of the BlackRock’s Factor Based Strategies Group I worked on understanding how some ESG criteria such as carbon emissions can impact return on equity.
(Causal) Bayesian modeling of investment factors and Environmental, Social and Governance (ESG) criteria
Turing Data Study Group: Optimising the supply chain to minimise waste and delivery mileage
As part of an Alan Turing Data Study Group team I worked on predicting deliveries to stores such that waste is minimised.
Turing Data Study Group: Optimising the supply chain to minimise waste and delivery mileage

Contact

My email address is marko dot tesic375 little monkey gmail dot com.