Erratum: Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

[This corrects the article DOI: 10.1016/j.patter.

Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of machine learning (ML) models amplifying societal biases. In this paper, we propose adapting income inequality metrics from economics to complement existing model-level fairness metrics, which focus on intergroup differences of model performance. In particular, we evaluate their ability to measure disparities between exposures that individuals receive in a production recommendation system, the Twitter algorithmic timeline.

Removing the influence of group variables in high-dimensional predictive modelling.

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias.

Carceral Amplification of COVID-19: Impacts for Community, Corrections Officer, and Incarcerated Population Risks.

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g.

Characterizing patterns in police stops by race in Minneapolis from 2016 to 2021.

The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze individual, geographic, and temporal patterns in more than 170,000 police stops since 2016. We evaluate person and vehicle searches at the individual level by race using generalized estimating equations with neighborhood clustering, directly addressing neighborhood differences in police activity.

The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates.

We build an agent-based model of incarceration based on the susceptible-infected-suspectible (SIS) model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be explained as the result of differential sentencing between the two demographic groups. We demonstrate that if incarceration can be spread through a social influence network, then even relatively small differences in sentencing can result in large disparities in incarceration rates.

A comparison of marginal and conditional models for capture-recapture data with application to human rights violations data.

Human rights data presents challenges for capture-recapture methodology. Lists of violent acts provided by many different groups create large, sparse tables of data for which saturated models are difficult to fit and for which simple models may be misspecified. We analyze data on killings and disappearances in Casanare, Colombia during years 1998 to 2007.

Estimating Human Cases of Avian Influenza A(H7N9) from Poultry Exposure.

In March 2013 an outbreak of avian influenza A(H7N9) was first recognized in China. To date there have been 130 cases in human, 47% of which are in men over the age of 55.The influenza strain is a novel subtype not seen before in humans; little is known about zoonotic transmission of the virus, but it is hypothesized that contact with poultry in live bird markets may be a source of exposure.

PLANNING AND RESPONSE IN THE AFTERMATH OF A LARGE CRISIS: AN AGENT-BASED INFORMATICS FRAMEWORK.

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure.

Remission of type 2 diabetes after Roux-en-Y gastric bypass is associated with greater weight loss.

Background: Physiologic studies in rodents and preliminary human studies have suggested that Roux-en-Y gastric bypass (RYGB) improves type 2 diabetes mellitus (T2DM) by way of metabolic changes, long before the bariatric or weight loss effects occur, leading to the concept of "metabolic surgery." To test this hypothesis, we studied patients with insulin-dependent T2DM who underwent RYGB to determine whether T2DM remission in this treatment-resistant subgroup occurred independent of weight loss.

Methods: Of all the patients undergoing RYGB from 2000 to 2006 (n = 1546) with >/=12 months of follow-up, 318 had T2DM (21%), and 75 (24%) of these were insulin dependent.

Spontaneous resolution of a 13-mm Chiari malformation Type I in relation to differential growth of the posterior fossa volume.

The case of a 3-year-old patient with tuberous sclerosis and a 13-mm Chiari malformation Type I that spontaneously disappeared over the course of 4 years is presented. Using morphometric measurements of the posterior fossa and cerebellum in this patient, the authors show that the volume of the posterior fossa at the time of initial evaluation was consistent with that reported as normal in the literature (180.24 cm3; normal volume 132-198 cm3).