Deep Learning for Coders with fastai and PyTorch

Chapter 3. Data Ethics

Acknowledgment: Dr. Rachel Thomas

This chapter was coauthored by Dr. Rachel Thomas, the cofounder of fast.ai and founding director of the Center for Applied Data Ethics at the University of San Francisco. It largely follows a subset of the syllabus she developed for the Introduction to Data Ethics course.

As we discussed in Chapters 1 and 2, sometimes machine learning models can go wrong. They can have bugs. They can be presented with data that they haven’t seen before and behave in ways we don’t expect. Or they could work exactly as designed, but be used for something that we would much prefer they were never, ever used for.

Because deep learning is such a powerful tool and can be used for so many things, it becomes particularly important that we consider the consequences of our choices. The philosophical study of ethics is the study of right and wrong, including how we can define those terms, recognize right and wrong actions, and understand the connection between actions and consequences. The field of data ethics has been around for a long time, and many academics are focused on this field. It is being used to help define policy in many jurisdictions; it is being used in companies big and small to consider how best to ensure good societal outcomes from product development; and it is being used by researchers who want to make sure that the work they are doing is used for good, and not for bad.

As a deep learning practitioner, therefore, you will likely ...