Principles and Practice of Explainable Machine Learning - CRC

UEBS Research

Join us for our Credit Research Centre Seminar Series with guest speaker Dr Vaishak Belle, from  School of Informatics, UoE.

Auditorium (Ground Floor), University of Edinburgh Business School

This will be a hybrid event. For those attending in-person, there will be drinks after the talk served at the Concourse. Please note, you are only required to register for a ticket if you are attending in-person. About the speakerDr Vaishak Belle is a Chancellor’s Fellow and Faculty at the School of Informatics, University of Edinburgh, an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. At the University of Edinburgh, he directs a research lab on artificial intelligence, specialising in the unification of logic and machine learning, with a recent emphasis on explainability and ethics. He has given research seminars at academic institutions, tutorials at AI conferences, and talks at venues such as Ars Electronica and the Samsung AI Forum. He has co-authored over 60 scientific articles on AI, along with his co-authors, and he has won the Microsoft best paper award at UAI, the Machine learning journal best student paper award at ECML-PKDD, and the Machine learning journal best student paper award at ILP. In 2014, he received a silver medal by the Kurt Goedel Society. Recently, he has consulted with major banks on explainable AI and its impact in financial institutions.AbstractArtificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with significant challenges: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods -- machine learning (ML) and pattern recognition models in particular -- so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature, or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions.If you are unable to attend in-person, and would like the link to the zoom call then please email the Research Office on research@business-school.ed.ac.uk, and we can provide it directly. You are only required to purchase a free ticket if you are attending in-person.

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Principles and Practice of Explainable Machine Learning - CRC

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Principles and Practice of Explainable Machine Learning - CRC

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