No Computation without Representation

The emergence and growth of research on issues of ethics in Artificial Intelligence, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in our 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.