Chapter 4 Data Ethics

This section draws on material from “Deep learning for Coders with fastai & PyTorch” by Jeremey Howard and Sylvain Gugger (Jeremy Howard 2020). The chapter “Data Ethics” is co-authored by Dr. Rachel Thomas, founding director of the Center of Applied Data Ethics at the University of San Francisco.

First lets look at a definition of ethics from Markkula Center for Applied Ethics Ethics is complicated and context-dependent. The following definition has therefore to be abstract since the answer to what is right and wrong are vary between cultures, over time and context.

Definition of ethics:

  • Well-founded standards of right and wrong that prescribe what humans should do
  • The study and development of one’s ethical standards

As a company there is the need to develop an ethical standard for the company. In times of demand for fair trade, green investment and anti-racism the topic of corporate ethics gains more and more significance to maintain a better connection with stakeholders of the company. Many major corporations promote their commitment to non-economic values in the frame of their ethics codes.


Jeremy Howard, Sylvain Gugger. 2020. Deep Learning for Coders with Fastai and PyTorch. O’Reilly Media, Inc.