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AG Audience

English

60 Minutes

Kaiserslautern

From finance and healthcare to criminal justice and transportation, various domains that require critical decisions, traditionally made by humans, have increasingly incorporated artificial intelligence (AI) systems into the decision making process. Despite unprecedented advances in AI capabilities, in most real-world contexts, AI systems do not make highly consequential decisions autonomously; instead, they operate in conjunction with a human decision maker. The promise of human-AI collaboration lies in the complementary strengths of each to lead to better decisions overall. In this thesis, I present contributions on multiple dimensions of AI-assisted decision making that range from technical methodologies to empirical findings. I start by focusing on ways that AI systems can support strategic reasoning—the ability of humans to make decisions based on expectations about others’ behavior—and I introduce algorithmic methods that support a human decision maker in allocating resources to a population of other (strategic) humans. Then, I switch focus to counterfactual reasoning—the ability to reflect on how different decisions could have led events of the past to turn out differently. In that context, I propose algorithmic methods to help a human decision maker improve their future decisions by highlighting specific past decisions that could have led to better outcomes and may serve as a good learning signal. I conclude the thesis by proposing a computational model based on counterfactual simulations to predict the extent to which people hold a human and an AI agent responsible for an outcome in cases where the two agents are working together towards a common goal. The performance of this model is evaluated through an experiment with human participants conducted online.

Susanne Girard

+49 631 9303 9605

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