Trustworthiness of machine learning applications

Machine learning is eating software. As decisions are automated, model interpretability must become an integral part of the ML pipeline rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box.

Machine learning (ML) software differs from traditional software in the sense that outcomes are not based on a set of hand-coded rules and hence not easily predictable, since the behavior of such software changes over time based on data and feedback loops. Salesforce Einstein cares deeply about building trust and confidence in intelligent software programs. Why does a particular email have a higher likelihood of being opened than another? What are the shapes and patterns in the dataset that lead to certain predictions? And can such insights be actionable? As machine learning pervades every software vertical and is increasingly used to automate decisions, model interpretability becomes an integral part of the ML pipeline and can no longer be an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy and other model evaluation metrics. It all starts with cultivating the idea that trustworthiness goes hand in hand with interpretability and is something that needs to be carefully thought through from the get-go. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box. Mayukh explains how interpretability fits into the ML data pipeline, what Salesforce Einstein learned while trying different approaches, and how it has helped drive wider adoption of ML software.