  Modern software relies heavily on data and machine learning, and affects
decisions that shape our world. Unfortunately, recent studies have shown that
because of biases in data, software systems frequently inject bias into their
decisions, from producing better closed caption transcriptions of men's voices
than of women's voices to overcharging people of color for financial loans. To
address bias in machine learning, data scientists need tools that help them
understand the trade-offs between model quality and fairness in their specific
data domains. Toward that end, we present fairkit-learn, a toolkit for helping
data scientists reason about and understand fairness. Fairkit-learn works with
state-of-the-art machine learning tools and uses the same interfaces to ease
adoption. It can evaluate thousands of models produced by multiple machine
learning algorithms, hyperparameters, and data permutations, and compute and
visualize a small Pareto-optimal set of models that describe the optimal
trade-offs between fairness and quality. We evaluate fairkit-learn via a user
study with 54 students, showing that students using fairkit-learn produce
models that provide a better balance between fairness and quality than students
using scikit-learn and IBM AI Fairness 360 toolkits. With fairkit-learn, users
can select models that are up to 67% more fair and 10% more accurate than the
models they are likely to train with scikit-learn.
