Data science is an exciting field, and it’s easy to get caught up in thinking about what you can find in the data and coming up with helpful predictions or an easy way to label something that used to be done by hand. But anything that can have a positive impact can also have unintended consequences, which may be negative, so it’s important to anticipate those and make sure the work should still be done and, if so, how. Imagining and understanding unintended negative consequences can be difficult, however. It’s important to have a systematic way of identifying potential impacts and either mitigating them or abandoning a problematic project.