Reducing bias and ensuring fairness in data science

In our work at Civis, we build a lot of models. Most of the time we're modeling people and their behavior because that's what we're particularly good at, but we're hardly the only ones doing this -- as we enter the age of "big data" more and more industries are applying machine learning techniques to drive person-level decision-making. This comes with exciting opportunities, but it also introduces an ethical dilemma: when machine learning models make decisions that affect people's lives, how can you be sure those decisions are fair? A central challenge in trying to build fair models is quantifying some notion of'fairness'. In the US there is a legal precedent which establishes one particular definition. However, this is also an area of active research.