COVID-19 challenges the daily function of nearly every institution of society. It is the duty of any society to be responsive to such challenges by relying on the best tools and logic available to analyze the costs and benefits of any mitigative action. We here provide a mathematical model to explore the epidemiological consequences of allowing standard intake and unaltered within-jail operational dynamics to be maintained during the ongoing COVID-19 pandemic, and contrast this with proposed interventions to reduce the burden of negative health outcomes. In this way, we provide estimates of the infection risks, and likely loss of life, that arise from current incarceration practices. We provide estimates for in-custody deaths and show how the within-jail dynamics lead to spill-over risks, not only affecting the incarcerated people, but increasing the exposure, infection, and death rates for both corrections officers with whom they interact within the jail system, and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business as usual way will result in significant and rapid loss of life. Large scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, staff and the community at large.As the COVID-19 pandemic sweeps the globe, one of the critical functions of 2 epidemiology is to consider how society can transform current practice to increase the 3 health and safety of the public. The widespread risk of infection and the high case 4 fatality rates, especially in older or medically compromised populations, mean that we, 5 as a society, must be willing to consider structural reforms to our institutions to 6 promote an overall greater good. To these ends, we have already seen systemic shifts in 7 institutional practices that would be unthinkable under normal conditions: 8 , 2020

Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on "ethical" or value-aligned deployments, often skipping over the prior question of whether a given system functions, or provides any benefits at all. To describe the harms of various types of functionality failures, we analyze a set of case studies to create a taxonomy of known AI functionality issues. We then point to policy and organizational responses that are often overlooked and become more readily available once functionality is drawn into focus. We argue that functionality is a meaningful AI policy challenge, operating as a necessary first step towards protecting affected communities from algorithmic harm.

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing algorithms on datasets used in prior work. In many cases, this data is used as a benchmark to demonstrate good performance without accounting for the complexities of criminal justice (CJ) processes. We show that pretrial RAI datasets contain numerous measurement biases and errors inherent to CJ pretrial evidence and due to disparities in discretion and deployment, are limited in making claims about real-world outcomes, making the datasets a poor fit for benchmarking under assumptions of ground truth and real-world impact. Conventional practices of simply replicating previous data experiments may implicitly inherit or edify normative positions without explicitly interrogating assumptions. With context of how interdisciplinary fields have engaged in CJ research, algorithmic fairness practices are misaligned for meaningful contribution in the context of CJ, and would benefit from transparent engagement with normative considerations and values related to fairness, justice, and equality. These factors prompt questions about whether benchmarks for intrinsically socio-technical systems like the CJ system can exist in a beneficial and ethical way.

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the epidemiologic impact of such interventions in jails and contrast them with the consequences of maintaining unaltered practices. We consider infection risk and likely in-custody deaths, and estimate how within-jail dynamics lead to spill-over risks, not only affecting incarcerated people but increasing exposure, infection, and death rates for both corrections officers and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business-as-usual way results in substantial, rapid, and ongoing loss of life. Our results are consistent with the hypothesis that large-scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, jail staff, and the wider community.

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