From Explanation to Interpretations

Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.

Abstract

ML practitioners have traditionally had to make a choice between the most performant models and those that allow for clear explanations of model decisions. With the recent advent of powerful XAI techniques, this is becoming less of an issue as formerly black box models become (more) transparent. Using these tools on large, complex, real-world tabular datasets, however, can lead to disappointing results that fall short of what is necessary for key stakeholders to use and trust system decisions. Using Bayesian networks, we propose a method that can help build a bridge between machine and expert “reasoning.” We show that applied to the classification of stock market investments – a field with a notoriously low signal-to-noise ratio – this method can help bring the best of man and machine working together to tackle new problems in applied data science.

Presentation Video

About the Presenter

Gabriel Andraos jointly leads Voya’s Machine Intelligence group (part of Voya Financial, a leading health, wealth, and investment company — NYSE ticker: VOYA (opens in a new tab)). As the co-head of VMI, he focuses on research and development in the application of AI and machine learning models for fundamental investing. For more than ten years, the VMI team has been using machine intelligence to run virtual employees – analysts, traders, and portfolio managers with transparent, explainable computer models anchored in fundamentals. Gabriel has approximately 26 years of investment experience. Prior to joining Voya, Gabriel was a managing partner and co-founder of G Squared Capital LLP. Before that, he held senior investment roles in Europe, the U.S., and Asia, combining knowledge and experience in fundamental analysis with the latest tools in computing and data science. Gabriel received an MBA from Harvard Business School and a BA in Economics from Georgetown University. He also has a Certificate in Quantitative Finance and several artificial intelligence, data science, and machine learning accreditations.

Previous Conference Presentations

  • Workflow automation in Bayesialab with applications to time series analysis (Paris, 2017)

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