Structural race-based inequalities in Mexico cannot be denied. Anthropologists and social scientists have thoroughly documented racism at both personal and systemic levels. Following I.M. Young’s framework, this paper identifies two possible pathways for the anti-racist movement in Mexico: the liability and the social connection models. The former uses guilt to assign responsibility —it requires an agent to be voluntarily and causally connected to injustice; the latter does not isolate perpetrators but assigns responsibility to all agents who contribute (voluntarily or not) by their actions to the structural processes that produce injustice. After examining the trajectory of the Mexican anti-racist movement, this paper demonstrates that activists are relying too heavily on the liability model. Furthermore, drawing from ethnographic material from Brazil and the United States, the paper suggests that this model is not only unnecessarily confrontational and ineffectual, but potentially counterproductive for the anti-racist movement, as it is prone to provoke a defensive response. In turn, this paper suggests focusing on the structural nature of racism in Mexico and developing ways to communicate this effectively, in order to foster the positive prospects of successful anti-racist activism.

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Objective: A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased. Objective: Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo). Methods: We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color Results: We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and kmeans clustering algorithms to determine the skin tone category of portraits. Conclusion: After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.