“BBQ Becky” and “Karen” memes reference real-world incidents in which Black individuals were harassed by White women in public spaces. In what I term the BBQ Becky meme genre, Black meme creators use humor, satire, and strategic positioning to perform a set of interrelated social commentaries on the behavior of White women. By conducting a visual Critical Technocultural Discourse Analysis (CTDA) of BBQ Becky memes, I argue that Becky and Karen memes are a cultural critique of White surveillance and White racial dominance. I find that memes in the BBQ Becky meme genre call attention to, and reject, White women’s surveillance and regulation of Black bodies in public spaces—making an important connection between racialized surveillance of the past and contemporary acts of “casual” racism. This meme genre also disrupts White supremacist logics and performative racial ignorance by framing Karens and Beckys as racist—not just disgruntled or entitled. Finally, in a subversion and reversal of power dynamics, Karen and BBQ Becky memes police White supremacy and explicitly call for consequences, providing Black communities with a form of agency. Hence, I conclude that Black memes matter in the struggle for racial equity.

Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize (American English sp) in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems.