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Machine Learning for Complex Systems
Artificial intelligence (AI) can help humans solve hard problems by harnessing vast amounts of data. However, current AI tools may not reliably behave when deployed in the context of real-world complex systems. This is because such systems undergo transformations that are difficult to comprehend and anticipate. We build causal machine learning tools, uncovering the internal structure and transformations of complex artificial, physical and socioeconomic systems. We demonstrate the potential of these tools for promoting trustworthy and interpretable AI.