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The emergence and growth of research on issues of ethics in AI, and inparticular algorithmic fairness, has roots in an essential observation thatstructural inequalities in society are reflected in the data used to trainpredictive models and in the design of objective functions. While researchaiming to mitigate these issues is inherently interdisciplinary, the design ofunbiased algorithms and fair socio-technical systems are key desired outcomeswhich depend on practitioners from the fields of data science and computing.However, these computing fields broadly also suffer from the sameunder-representation issues that are found in the datasets we analyze. Thisdisconnect affects the design of both the desired outcomes and metrics by whichwe measure success. If the ethical AI research community accepts this, wetacitly endorse the status quo and contradict the goals of non-discriminationand equity which work on algorithmic fairness, accountability, and transparencyseeks to address. Therefore, we advocate in this work for diversifyingcomputing as a core priority of the field and our efforts to achieve ethical AIpractices. We draw connections between the lack of diversity within academicand professional computing fields and the type and breadth of the biasesencountered in datasets, machine learning models, problem formulations, andinterpretation of results. Examining the current fairness/ethics in AIliterature, we highlight cases where this lack of diverse perspectives has beenfoundational to the inequity in treatment of underrepresented and protectedgroup data. We also look to other professional communities, such as in law andhealth, where disparities have been reduced both in the educational diversityof trainees and among professional practices. We use these lessons to developrecommendations that provide concrete steps for the computing community toincrease diversity.

No computation without representation: Avoiding data and algorithm biases through diversity