XPlainIT is about opening the “black box” decision making of machine learning algorithms so that decisions are transparent and understandable. The users of this capability to explain decisions are data-scientists, end-users, company personnel, regulatory authorities, or indeed any stakeholder who has a valid remit to ask questions about the decision making of such systems. The focus of XplainIT is on Deep Learning Models for structured data.
XPlainIT is a Web-based tool which offers a series of functionalities for explaining the decision-making process of Deep Learning models applied to structured data in a user-friendly manner, including:
Three cutting-edge methods: LRP (mainly), LIME and SHAP.
Two scenarios: Telecom Customer Churn prediction and Credit Card Fraud Detection.
Three types of Analysis: Local (Individual), Global (Exploring Patterns) and New Record (Exploring New Inputs).
Explanation based on intuitive and interactive Charts: Heatmaps, Bar plots, Range plots, Box plots.
Explanation based on Text and Infoboxes.
The tool is relevant to any company or organisation who needs to discover how explainability can explain the decision making of a deep neural network (in this case 1D-CNN). This can help them in understanding certain issues in a system, such as bias, critical decision features, unnecessary features that can slow down processing and increasing equipment costs, and/or more specifically enforcing or taking care of GDPR.
Dr Susan Mckeever, Technological University Dublin,