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First, I use causal models to construct and discuss formal definitions of actual causation and their properties. Instead of only looking at how well such definitions match up with alleged intuitions about causation, I aim to justify my definitions in a more systematic and principled way.
Second, I use the combination of causal models and definitions of actual causation to formally define other important notions, such as harm, responsibility, and explanation. The underlying motivation to formally define these notions is twofold: they offer useful tools to develop ethical AI, and they contribute to the more general philosophical literature on these topics.
Third, I work on extending the framework of causal models itself, so that they can express a wider range of relations that we are interested in. For example, my work on causal abstraction makes precise how to represent the relation between two causal models that describe the same system at different levels of abstraction. Other work in progress aims to generalize even further, so that we can combine variables which share causal and non-causal relations with each other into a single model. This line of work also holds promise for discussions in the philosophy of science on reductionism and inter-level causation.
Second, I use the combination of causal models and definitions of actual causation to formally define other important notions, such as harm, responsibility, and explanation. The underlying motivation to formally define these notions is twofold: they offer useful tools to develop ethical AI, and they contribute to the more general philosophical literature on these topics.
Third, I work on extending the framework of causal models itself, so that they can express a wider range of relations that we are interested in. For example, my work on causal abstraction makes precise how to represent the relation between two causal models that describe the same system at different levels of abstraction. Other work in progress aims to generalize even further, so that we can combine variables which share causal and non-causal relations with each other into a single model. This line of work also holds promise for discussions in the philosophy of science on reductionism and inter-level causation.
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论文共 27 篇作者统计合作学者相似作者
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CLeaRpp.177-196, (2023)
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IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligencepp.363-371, (2023)
arXiv (Cornell University) (2022)
Conference on Causal Learning and Reasoning (CLeaR)pp.90-109, (2022)
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