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Fordham Law Review



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We live in a datafied society in which our personal data is being constantly harvested, analyzed, and sold by public and private entities, and yet we have little control over our data and little voice in how it is used. In light of the impacts of algorithmic decision-making systems—including those that run on machine learning and artificial intelligence—there are increasing calls to integrate public participation into the adoption, design, and oversight of these tech tools. Stakeholder input is particularly crucial for members of marginalized groups, who bear the disproportionate harms of data-centric technologies. Yet, recent calls for public participation have been mostly hortatory and without specific strategies or realistic recommendations. As this Article explains, policy makers need not operate from a blank slate. For decades, a variety of American statutory regimes have mandated public participation, such as in the areas of environmental law, land use law, and anti-poverty programs. Such mandates have had outsized effects on communities suffering from economic disadvantage and racial and ethnic discrimination. This Article contends that we should examine these regulatory mandates in thinking about how to include the perspectives of marginalized stakeholders in the datafied society. The core takeaway is that meaningful public participation is extremely challenging and does not happen without intentional and inclusive design. At its best, public input can improve outputs and empower stakeholders. At its worst, it operates as a form of “window dressing,” in which marginalized communities have no real power to effect outcomes, thus generating distrust and alienation. Case studies show that meaningful public participation is most likely to result when there are hard-law requirements for public participation and when decision-makers operate transparently and recognize the value of the public’s expertise. In addition, impacted communities must be provided with capacity-building tools and resources to support their engagement. As legislative proposals to enhance tech accountability—through algorithmic impact assessments, audits, and other tools—gain steam, we must heed these lessons.

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