Workshop on Explainable AI in Finance 

Abstract

In this talk I will present two approaches for making machine learning for time-series forecasting interpretable and actionable.

The first approach uses post-hoc interpretability to turn black-box machine learning into interpretable insights. I will describe here the first methods for feature-based and for example-based interpretability of machine learning for time-series forecasting – Dynamask (ICML 2021) and Simplex (NeurIPS 2021), respectively.

The second approach directly discovers interpretable closed-form ordinary differential equations (ODEs) from data using machine learning. I will describe here the first automated tool to distill closed-form ODEs from observed trajectories, D-CODE (ICLR 2022), which we believe will accelerate the modeling process of dynamical systems in finance, in healthcare, and beyond.