Counterfactual Reasoning and Uncertainty Quantification for AI-Assisted Decision Making
Nina Corvelo Benz
Max Planck Institute for Software Systems
15 Oct 2025, 4:00 pm - 5:00 pm
Kaiserslautern building G26, room 111
SWS Student Defense Talks - Thesis Defense
Artificial intelligence (AI) systems are increasingly being used to support
human experts in
various domains such as healthcare, education, and the judicial system. The aim
of these systems is
complementarity—leveraging the strengths of each side, human and AI, to
compensate for the
weaknesses of the other. In most such systems, the human expert makes decisions
based on a
prediction by the AI model and their own judgment. However, models designed for
automated
decision making are typically trained in isolation and do not take into account
the human decision
maker when making predictions. ...
Artificial intelligence (AI) systems are increasingly being used to support
human experts in
various domains such as healthcare, education, and the judicial system. The aim
of these systems is
complementarity—leveraging the strengths of each side, human and AI, to
compensate for the
weaknesses of the other. In most such systems, the human expert makes decisions
based on a
prediction by the AI model and their own judgment. However, models designed for
automated
decision making are typically trained in isolation and do not take into account
the human decision
maker when making predictions. As a result, when these AI models are used in
decision support
systems, their predictions may not be helpful, undermining the human expert’s
trust in the AI
model and leading to no improvement in their decisions. To address this, this
thesis focuses on the
design of AI-based decision support systems that leverage the interaction with
the expert through
counterfactual reasoning and uncertainty quantification. It proposes decision
support systems
for three distinct decision-making contexts, where each one is based on a novel
methodological approach and is evaluated with experiments using real-world data
or a human subject
study.
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