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AI and Explainability: Discover Why Your Models Make Their Decisions

This is an article from DZone's 2022 Enterprise AI Trend Report.

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Explainable artificial intelligence, sometimes referred to as XAI, is exactly what it sounds like — explaining how and why a machine learning model makes a prediction. While models are usually classified as either "black box" or "glass box," it isn't quite as simple as that; there are some that fall somewhere in between. Some models are more naturally transparent than others, and their uses depend on the application. 

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The 10 Commandments for Performing a Data Science Project

In designing a data science project, establishing what we, or the users we are building models for, want to achieve is vital, but this understanding only provides a blueprint for success. To truly deliver against a well-established brief, data science teams must follow best practices in executing the project. To help establish what that might mean, I have come up with ten points to provide a framework that can be applied to any data science project.

1. Understand the Problem 

The most fundamental part of solving any problem is knowing exactly what problem you are solving. Make sure that you understand what you are trying to predict, any constraints, and what the ultimate purpose for this project will be. Ask questions early on and validate your understanding with peers, domain experts, and end-users. If you find that answers are aligning with your understanding, you know that you are on the right path. 

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The 10 Commandments for Designing a Data Science Project

Introduction

As businesses across industries seek to improve workflows and the delivery of products and services through increased automation, there is an ever-growing demand for the adoption of more advanced data science capabilities and projects. 

Artificial intelligence and machine learning can, of course, deliver great ROI — but only under the right conditions. In every instance, a data science project must be framed in the right way, both from a business and a technical point of view. To help provide this framework, I have devised the following “10 commandments” for designing a data science project.