Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery task to learn a causal graph by combining observational data from an open-source dataset with prior knowledge. The resulting causal graph shows that only a few variables effectively influence the analysed feedback signals. This contrasts with the recent trend in the machine learning community to include more and more variables in massive models, such as neural networks.

Abstract: After more than 30 years in academia researching in the area of AI, as a student and as a faculty, I joined JPMorgan to create and head an AI research group. In this talk, I will present several concrete examples of the projects we are pursuing in engagement with the lines of business. I will focus on areas related to data, learning from experience, explainability, and ethics. I will conclude with a discussion of my current understanding of the transformational impact that AI can have in the future of financial services. Bio: Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction.