Barabas’s article sets forth three propositions: (1) that attempts to render AI- branded tools more accurate by addressing narrow notions of “bias” miss the deeper methodological and epistemological issues regarding the fairness of these tools, (2) that the key questions to address these issues are whether predictive tools reflect and reinforce punitive practices that drive disparate outcomes and how data regimes interact with the penal ideology to naturalize these practices, and (3) that an abolitionist understanding of the role and function of the carceral state provides us with the First Principles Framework we need in order to fundamentally reformulate the questions we ask, the way we characterize existing data, and how we identify and fill gaps in existing data regimes of the carceral state.

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