97 Things About Ethics Everyone in Data Science Should Know

Chapter 10. Data Science Ethics: What Is the Foundational Standard?

Mario Vela

Principal Data Scientist, US Cellular Corp

To address the question of ethics in any arena, including data science, we first need to ask ourselves what standard should be used to define what is “good” and what is “bad.” The importance of knowing such a standard is fundamental, since choosing the wrong standard can generate false definitions of what is “good” and “bad,” with a variety of consequences in society and, in this case, in the practice and use of data science. Hence, the standard must be absolute, because if it changes, then the meaning of “good” and “bad” is lost, and we fall into moral relativism.

Peter Kreeft suggests that, to talk about ethics, we must ask ourselves: what is the moral standard that we use in our daily lives?¹ If we cannot readily answer such a question, we should embark on the search for the answer using logic and reason. Kreeft argues that, to answer this type of question, we have two options: either our core moral values are objective, or they are subjective; they are discovered as scientists discover the laws of physics, or they are created as the rules of a game or a piece of art. He also notes that premodern cultures believed that core moral values are objective, and it is only in recent times that society started to believe that those core moral values are subjective ...