  The emergence and growth of research on issues of ethics in AI, and in
particular algorithmic fairness, has roots in an essential observation that
structural inequalities in society are reflected in the data used to train
predictive models and in the design of objective functions. While research
aiming to mitigate these issues is inherently interdisciplinary, the design of
unbiased algorithms and fair socio-technical systems are key desired outcomes
which depend on practitioners from the fields of data science and computing.
However, these computing fields broadly also suffer from the same
under-representation issues that are found in the datasets we analyze. This
disconnect affects the design of both the desired outcomes and metrics by which
we measure success. If the ethical AI research community accepts this, we
tacitly endorse the status quo and contradict the goals of non-discrimination
and equity which work on algorithmic fairness, accountability, and transparency
seeks to address. Therefore, we advocate in this work for diversifying
computing as a core priority of the field and our efforts to achieve ethical AI
practices. We draw connections between the lack of diversity within academic
and professional computing fields and the type and breadth of the biases
encountered in datasets, machine learning models, problem formulations, and
interpretation of results. Examining the current fairness/ethics in AI
literature, we highlight cases where this lack of diverse perspectives has been
foundational to the inequity in treatment of underrepresented and protected
group data. We also look to other professional communities, such as in law and
health, where disparities have been reduced both in the educational diversity
of trainees and among professional practices. We use these lessons to develop
recommendations that provide concrete steps for the computing community to
increase diversity.
