With the advent of powerful computational tools like Machine Learning and their growing applications in real-life, the ethical concerns underlying their use have become an important and urgent topic of study. In this talk, we will discuss the importance of a fundamental ethical view-point in the mathematical modeling process that underlies the application of mathematics in real-life that goes beyond the usually discussed topics like algorithmic bias, data bias, etc. We will discuss some historical examples related to the precautionary principle as well as a recent paper (with Adam Rumpf) on designing a public transit system with equitable access to social services.