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The use of machine learning systems in processing job applications has madethe process agile and efficient, but at the same time it has created problemsin terms of equality, reliability and transparency. In this paper we explainsome of the uses of ML in job selection processes in the United States, and wepresent some the racial and sexual biases that have been detected. There areboth practical and legal obstacles that impede the detection and analysis ofthese biases. It is also unclear how to approach algorithmic discriminationfrom a legal point of view. A possible analytical tool is provided by theAmerican doctrine of Disparate Impact, but we show some of its limitations andproblems when adapted to other legal systems, such as Colombian law. Toconclude, we offer some desiderata that any legal analysis of algorithmicdiscrimination should provide.

An\'alisis jur\'idico de la discriminaci\'on algor\'itmica en los procesos de selecci\'on laboral