Responsible Data Science

Introduction

In this book, we will review some of the harmful ways artificial intelligence has been used and provide a framework to facilitate the responsible practice of data science. While we will touch upon mitigating legal risks, in this book we will focus primarily on the modeling process itself, especially on how factors overlooked by current modeling practices lead to unintended harms once the model is deployed in a real-world context.

Three core themes will be developed through this book:

• Any AI algorithm can have a harmful, dark side: once they are applied in the real world, AI algorithms can cause any number of harms. An algorithm designed to help police catch murderers can later be appropriated by totalitarian states to persecute dissidents; an algorithm that expands the availability of financial credit for the vast majority of people may nonetheless intensify bias against minorities.
• The dark sides of AI algorithms are created or deepened by current modeling approaches. By focusing only on technical considerations like maximizing predictive performance, data scientists ignore the potential for their model to aggravate biases against certain groups, generate harmful predictions, or otherwise be used by other groups in the future for malicious purposes.
• New modeling approaches are needed if we want to use AI more responsibly. If data scientists and their users are going to continue to use AI algorithms to make consequential decisions, then they ought to do so with ...