Extract from Aidan Randle-Conde’s article “Navigating AI Success Metrics Series”
Introduction
In the ever-evolving landscape of artificial intelligence (AI), the ability to accurately measure the success of AI applications is paramount. Whether you’re a business leveraging AI for managing legal and investigation processes, an academic researching AI efficacy, or simply an AI enthusiast, understanding how to evaluate AI performance is crucial.
Over the coming weeks, we will embark on a journey through the intricate world of AI metrics, focusing specifically on two of the most common applications of AI in the business world: email and conversational datasets, such as those found in Slack or Microsoft Teams. Our goal is to demystify the traditional key metrics of recall, rejection, and precision and to provide you with a clear understanding of how these metrics can be used to measure AI success in your real-world applications.
Part I: Navigating AI Success Metrics – Precision and Recall in Email and Document Analysis
In our first post, we will dive into the world of email datasets. Emails are a critical component of business communication, and AI plays a significant role in managing, sorting, and even responding to them. We will explore how precision and recall can be applied to document-based datasets to evaluate the effectiveness of AI in handling email communications. This post is designed to set a strong foundation for understanding how to measure AI success in processing and analyzing written content.
