Book Image

AI & Data Literacy

By : Bill Schmarzo
Book Image

AI & Data Literacy

By: Bill Schmarzo

Overview of this book

AI is undoubtedly a game-changing tool with immense potential to improve human life. This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you’ll need to exploit to thrive amid the current and future developments in the AI landscape. We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions. Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed. Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it.
Table of Contents (14 chapters)
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Real-world use case: AI in the world of job applicants

In 2018, about 67% of hiring managers and recruiters used AI to pre-screen job applicants. By 2020, that percentage has increased to 88% [2].Poorly constructed and monitored AI models introduce biases in the decision process, lack accountability and transparency, and do not necessarily even guarantee to be effective in hiring the right applicants. These AI hiring models fail by:

  • Accepting (hiring) a candidate whose resumes and job experience closely matches the AI model metrics, behavioral characteristics, and performance assumptions, but for other reasons not captured by the model does not work out and most be fired (False Positive)
  • Rejecting highly-qualified candidates whose resumes and job experience don’t closely match the metrics, behavioral characteristics, and performance assumptions underpinning the AI models (False Negative).
Figure 6.7: AI-based Hiring Model Type I and Type II Errors

The good news is that these...