Kristian Lum
Dados Biográficos
| ID | 919002 |
|---|---|
| NOME | Kristian Lum |
| PRENOMES | Kristian |
| SOBRENOME | Lum |
| ASSINATURA | LUM K |
| AFILIAÇÕES | Analysis Group (United States) |
| ORCID | 0000-0003-2637-5612 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 6 |
| TOTAL DE CITAÇÕES | 5 |
| TOTAL COMO AUTOR | 6 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2010 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2022 |
| ÍNDICE H | 1 |
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 diff…
Algorithmic Fairness
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-…
Layers of Bias
Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the ri…
Addressing the Race Gap in Incarceration Rates
Using an agent-based model of incarceration, the authors conduct a series of simulation experiments testing the efficacy of policy interventions designed to address racial disparities in incarceration rates. The first experiment eliminates race-based sentencing disparities, and additional experiments eliminate race-based sentencing disparities and disrupt the clustering of incarcerated individuals in their social networks. Findings suggest that e…
Limitations of mitigating judicial bias with machine learning
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation
Collecting data for the analysis of past human rights violations is fraught with challenges. For example, individuals from or about whom data should be collected may be displaced, missing, or dead. Some reports of acts may be easier to find than others, and as a result, datasets will be biased toward those cases. These challenges must be overcome in order to create effective official policy for violence mitigation and prevention. This relies on s…
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation
Collecting data for the analysis of past human rights violations is fraught with challenges. For example, individuals from or about whom data should be collected may be displaced, missing, or dead. Some reports of acts may be easier to find than others, and as a result, datasets will be biased toward those cases. These challenges must be overcome in order to create effective official policy for violence mitigation and prevention. This relies on s…
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation
Collecting data for the analysis of past human rights violations is fraught with challenges. For example, individuals from or about whom data should be collected may be displaced, missing, or dead. Some reports of acts may be easier to find than others, and as a result, datasets will be biased toward those cases. These challenges must be overcome in order to create effective official policy for violence mitigation and prevention. This relies on s…
Addressing the Race Gap in Incarceration Rates
Using an agent-based model of incarceration, the authors conduct a series of simulation experiments testing the efficacy of policy interventions designed to address racial disparities in incarceration rates. The first experiment eliminates race-based sentencing disparities, and additional experiments eliminate race-based sentencing disparities and disrupt the clustering of incarcerated individuals in their social networks. Findings suggest that e…
Limitations of mitigating judicial bias with machine learning
Layers of Bias
Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the ri…
Algorithmic Fairness
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-…
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 diff…
Computer Science (6 obras) · Artificial Intelligence (3 obras) · Crime Patterns and Interventions (3 obras) · Criminology (3 obras) · Psychology (3 obras) · Sociology (3 obras) · Criminal Justice and Corrections Analysis (2 obras) · Data science (2 obras) · Econometrics (2 obras) · Economics (2 obras)