No Computation without Representation: Avoiding Data and Algorithm Biases through Diversity

e emergence and growth of research on issues of ethics in Articial Intelligence, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in our society are re ected 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 elds of data science and computing. However, these computing elds broadly also suffer from the same under-representation issues that are found in the datasets we analyze. is disconnect a ects 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. erefore, we advocate in this work for diversifying computing as a core priority of the eld and our efforts to achieve ethical AI practices. We draw connections between the lack of diversity within academic and professional computing elds and the type and breadth of the biases encountered in datasets, machine learning models, problem formulations, and the 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 the treatments of underrepresented and protected group data. We also look to other professional communities, such as in the law and health domains, where disparities have been reduced both in the educational diversity of trainees and among their professional practices. We use these lessons to develop a set of recommendations that provide concrete steps for the computing community to increased diversity.