  As technology continues to advance, there is increasing concern about
individuals being left behind. Many businesses are striving to adopt
responsible design practices and avoid any unintended consequences of their
products and services, ranging from privacy vulnerabilities to algorithmic
bias. We propose a novel approach to fairness and inclusiveness based on
experimentation. We use experimentation because we want to assess not only the
intrinsic properties of products and algorithms but also their impact on
people. We do this by introducing an inequality approach to A/B testing,
leveraging the Atkinson index from the economics literature. We show how to
perform causal inference over this inequality measure. We also introduce the
concept of site-wide inequality impact, which captures the inclusiveness impact
of targeting specific subpopulations for experiments, and show how to conduct
statistical inference on this impact. We provide real examples from LinkedIn,
as well as an open-source, highly scalable implementation of the computation of
the Atkinson index and its variance in Spark/Scala. We also provide over a
year's worth of learnings -- gathered by deploying our method at scale and
analyzing thousands of experiments -- on which areas and which kinds of product
innovations seem to inherently foster fairness through inclusiveness.
