Designing Data-Intensive Applications, 2nd Edition

Chapter 14. Doing the Right Thing

Feeding AI systems on the world’s beauty, ugliness, and cruelty, but expecting it to reflect only the beauty is a fantasy.

Vinay Uday Prabhu and Abeba Birhane, Large Datasets: A Pyrrhic Win for Computer Vision? (2020)

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 14th chapter of the final book. The GitHub repo for this book is https://github.com/ept/ddia2-feedback.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out on GitHub.

In the final chapter of this book, let’s take a step back. Throughout this book we have examined a wide range of different architectures for data systems, evaluated their pros and cons, and explored techniques for building reliable, scalable, and maintainable applications. However, we have left out an important and fundamental part of the discussion, which we should now fill in.

Every system is built for a purpose; every action we take has both intended and unintended consequences. The purpose may be as simple as making money, but the consequences for the world may reach far beyond that original purpose. We, the engineers building these systems, have a responsibility to carefully consider those consequences and to consciously decide what kind of world we want ...