Michele E. Gilman (University of Baltimore – School of Law) has posted Code and Consequences: How Fraud Detection Algorithms Criminalize Welfare on SSRN. Here is the abstract:
This article investigates the growing use of automated fraud detection systems in public benefits programs, focusing on how these technologies have intensified the surveillance and criminalization of low-income individuals. Across the globe, government agencies deploying fraud detection algorithms have wrongfully accused thousands of people of committing fraud, with devastating consequences, including bankruptcy, job loss, and psychological trauma. These algorithmic systems operate as opaque “black boxes,” fueled by historical biases against the poor and largely unaccountable to the individuals they affect. Meanwhile, the private vendors that develop the algorithms reap massive profits from unfulfilled promises of efficiency and cost savings. The article situates these developments within the broader frameworks of surveillance capitalism and the datafied state, arguing that the convergence of corporate data commodification and government automation has created a new digital dystopia in welfare administration. While “real” fraud—committed by organized criminal syndicates—has increased due to the proliferation of unregulated personal data flows, the state’s response has disproportionately targeted vulnerable populations. The article contends that existing legal protections, including due process, privacy law, and anti-discrimination laws, are inadequate to address the systemic harms posed by these algorithms. These doctrines were shaped to restrain human actors; they are not sufficient on their own to govern the scope, scale, and opacity of the datafied state. The article thus calls for a fundamental rethinking of algorithmic governance, urging a shift from the prevailing fraud-first presumption.
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Lawrence Solum