Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This article discusses several fairness criteria that have recently been applied to assess the fairness of RPIs. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.

A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.

Summary. We consider a multiple hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block, H1, . . . , H k , of hypotheses. A rejection rule in this setting amounts to a procedure for choosing the stopping point k. This setting is inspired by the sequential nature of many model selection problems, where choosing a stopping point or a model is equivalent to rejecting all hypotheses up to that point and none thereafter. We propose two new testing procedures, and prove that they control the false discovery rate in the ordered testing setting. We also show how the methods can be applied to model selection using recent results on p-values in sequential model selection settings.

Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases, the purpose of the risk assessment tool is to inform actions, such as medical treatments or release conditions, often with the aim of reducing the likelihood of an adverse event such as hospital readmission or recidivism. Problematically, most tools are trained and evaluated on historical data in which the outcomes observed depend on the historical decision-making policy. These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform. Even when tools are constructed to predict risk under a specific decision, they are often improperly evaluated as predictors of the target outcome. Focusing on the evaluation task, in this paper we define counterfactual analogues of common predictive performance and algorithmic fairness metrics that we argue are better suited for the decision-making context. We introduce a new method for estimating the proposed metrics using doubly robust estimation. We provide theoretical results that show that only under strong conditions can fairness according to the standard metric and the counterfactual metric simultaneously hold. Consequently, fairness-promoting methods that target parity in a standard fairness metric may-and as we show empirically, do-induce greater imbalance in the counterfactual analogue. We provide empirical comparisons on both synthetic data and a real world child welfare dataset to demonstrate how the proposed method improves upon standard practice.

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Background Human cardiac progenitor cells have demonstrated great potential for myocardial repair in small and large animals, but robust methods for longitudinal assessment of their engraftment in humans is not yet readily available. In this study, we sought to optimize and evaluate the use of positron emission tomography (PET) reporter gene imaging for monitoring human cardiac progenitor cell (hCPC) transplantation in a mouse model of myocardial infarction. Methods & Results hCPCs were isolated and expanded from human myocardial samples and stably transduced with variations of the thymidine kinase (TK) PET reporter gene. TK-expressing hCPCs were characterized in vitro and transplanted into murine myocardial infarction models (n=60). Cardiac echocardiographic, magnetic resonance imaging (MRI), and pressure-volume (PV) loop analyses revealed improvement in left ventricular contractile function two weeks after transplant (hCPC vs. PBS, P<0.03). Noninvasive PET imaging was used to track hCPC fate over a four week time period, demonstrating a substantial decline in surviving cells. Importantly, early cell engraftment as assessed by PET was found to predict subsequent functional improvement, implying a “dose-effect” relationship. We isolated the transplanted cells from recipient myocardium by laser capture microdissection for in vivo transcriptome analysis. Our results provide direct evidence that hCPCs augment cardiac function after their transplantation into ischemic myocardium through paracrine secretion of growth factors. Conclusions PET reporter gene imaging can provide important diagnostic and prognostic information regarding the ultimate success of human cardiac progenitor cell treatment for myocardial infarction.

Algorithmic decision-making systems are increasingly being adopted by government public service agencies. Researchers, policy experts, and civil rights groups have all voiced concerns that such systems are being deployed without adequate consideration of potential harms, disparate impacts, and public accountability practices. Yet little is known about the concerns of those most likely to be affected by these systems. We report on workshops conducted to learn about the concerns of affected communities in the context of child welfare services. The workshops involved 83 study participants including families involved in the child welfare system, employees of child welfare agencies, and service providers. Our findings indicate that general distrust in the existing system contributes significantly to low comfort in algorithmic decision-making. We identify strategies for improving comfort through greater transparency and improved communication strategies. We discuss the implications of our study for accountable algorithm design for child welfare applications.