Most of the high-profile cases of real or perceived unethical activity in data science aren’t matters of bad intent. Rather, they occur because the ethics simply aren’t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult.
In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices.
In the chapter Triage and Artificial Intelligence, Peter Bruce discusses the role of AI in scenarios where it is inappropriate for an algorithm to make final decisions, and its role is analogous to that of the triage system making rapid intermediate decisions in a medical setting. In his chapter Random Selection at Harvard, he posits random selection as a statistical solution to the problem of human bias in college selection. In the chapter, The Ethical Dilemma of Model Interpretability, Grant Fleming discusses the ethical dimensions of the loss of transparency in black-box models, and how model interpretability is important.