Runaway Feedback Loops in Predictive Policing

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.Comment: Extended version accepted to the 1st Conference on Fairness, Accountability and Transparency, 2018. Adds further treatment of reported as well as discovered incident

An Assessment of How Mobile Telecommunications Competition Effect on Mobile Call Prices

世界范围内，各个行业都存在激烈的竞争。文献中，前人已经从各个角度分析了竞争带给经济的影响。本文通过分析来自49个国家，2010年到2014年5年的面板数据，分析通讯行业内的竞争对通讯费的影响。本文的目标是判断行业竞争是否是影响通讯价格最主要的因素，以及竞争在发展中国家和发达国家的不同影响。本文中，通讯费的数据来自国际通讯报告(以占国内生产总值的比例计算而来)，其他变量来自世界银行和维基百科。固定国家效应和时间效应被纳入模型中，本文结果显示：竞争对于通讯费有重要的正面影响；此外，HHI因素对于通讯费的影响在发展中国家更为显著。Competition is a wide spread phenomenon present in almost all the industries in the world. In the academic research field competition has been examined from different angles. This thesis assesses the effect of mobile telecommunications competition on mobile call prices on a panel of 49 countries for a period of five years (from 2010 to 2014). The objective of this paper is to find out if competiti...学位：经济学硕士院系专业：王亚南经济研究院_金融学学号：2772014115463

Auditing Black-box Models for Indirect Influence

Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting the behavior of complex models, or asserting that certain problematic attributes (like race or gender) are not unduly influencing decisions. In this paper, we present a technique for auditing black-box models, which lets us study the extent to which existing models take advantage of particular features in the dataset, without knowing how the models work. Our work focuses on the problem of indirect influence: how some features might indirectly influence outcomes via other, related features. As a result, we can find attribute influences even in cases where, upon further direct examination of the model, the attribute is not referred to by the model at all. Our approach does not require the black-box model to be retrained. This is important if (for example) the model is only accessible via an API, and contrasts our work with other methods that investigate feature influence like feature selection. We present experimental evidence for the effectiveness of our procedure using a variety of publicly available datasets and models. We also validate our procedure using techniques from interpretable learning and feature selection, as well as against other black-box auditing procedures.Comment: Final version of paper that appears in the IEEE International Conference on Data Mining (ICDM), 201

MS 226 Guide to the Ruth SoRelle Papers (1950s-2019)

The collection includes photos, plaques, framed objects, clipping books, clipping files, reference and topic files, childhood writings, science and medical articles, reporter notebooks, and clippings from SoRelle\u27s budding career as a journalist at the University of Texas. One notable area of interest are the articles related to the HIV/AIDS epidemic in Houston. See more at MS 226

A meta-analysis of behavioral and event predictors within 24 hours of suicide attempt

Despite much time, research, and resources dedicated to preventing suicide, the rates of suicide and suicide attempts in the United States are increasing. Progress in understanding and preventing suicide has been challenging because it is an extremely low base rate phenomenon. The vast majority of people who exhibit long-term risk factors and even short-term warning signs of suicide do not attempt or die by suicide. While existing studies on this topic have been synthesized through meta-analyses, their focus has largely been on identifying trait-like characteristics that may predict lifetime suicide attempt or death. Given the poor predictive ability of such long-term risk factors, coupled with the increasing rates of suicide and its societal impact, identification of shorter-term events and behaviors that warn of imminent suicide (i.e., over the next hours, days, weeks) is a public health imperative. The current study is a meta-analysis on the existent literature of behavioral and event warning signs that occurred within 24 hours of suicide attempt or death. The current search yielded 14 studies with 4992 total participants that met inclusion criteria from 19,545 references. The meta-analysis of included studies found that (1) the use of alcohol (OR = 4.16; 95 percent CI: 2.87, 6.04; p [less than] 0.001) and (2) the experience of a negative interpersonal life event (NLE; OR = 3.81; 95 percent CI: 2.83, 5.12; p [less than] 0.001) were each significantly associated with a suicide attempt in the following 24 hours. When these two broad categories were examined more closely, the following predictors are significantly associated with a suicide attempt in the subsequent 24 hours: heavy alcohol use ((OR = 11.79; 95 percent CI: 2.02, 68.95; p = 0.006), CNS depressant use (OR = 3.29; 95 percent CI: 1.41, 7.66; p = .006), a family NLE (OR = 3.70; 95 percent CI: 1.41, 9.69; p = 0.008), a marriage/love NLE (OR = 4.21; 95 percent CI: 3.09, 5.75; p [less than] 0.001), and a friend NLE (OR = 23; 95 percent CI: 3.11, 170.20; p = .002). These novel findings may help inform suicide prevention efforts by highlighting warning signs that an individual may be imminent risk of an attempt within the next day, thereby supporting the allocation of limited resources (e.g., increased monitoring and supervision, hospitalization, suicide prevention interventions) to those who are experiencing acute risk. Future research is needed to examine additional warning signs, to determine whether prediction can be further improved by combining both long-term risk factors and short-term warning signs, and to identify the most effective and efficient interventions for highest risk individuals.Includes bibliographical references

Experiences of LGBTQ+ graduate students in research-focused doctoral programs: a scoping review

Students of sexual and gender minority (SGM) identities have long been underserved in higher education, and the limited research thus far has focused on undergraduates. There is a large gap in understanding the outcomes and experiences of LGBTQ+ graduate students, particularly in STEM. We undertook the first scoping review to examine the available literature on LGBTQ+ student experiences in research-focused doctoral programs. A scoping review methodology was utilized to compile a broad set of publications for a narrative review of emergent themes. A comprehensive search of 5 bibliographic databases yielded 1,971 unique studies, which were screened by two independent reviewers for data on LGBTQ+ doctoral students in non-clinical fields. Eighty-two publications were included in the analysis, over half of which were published in the past 5 years. Thirteen themes emerged from analyzing the included publications. LGBTQ+ ientities can continue evolving during graduate school, and some students incorporated SGM identities in their research (“mesearch”). Though students expected academia to be welcoming, many encountered repeated anti-LGBTQ+ bias that impacted their perceived safety for coming out. Nearly half of the studies mentioned intersectionality with other marginalized identities, including race/ethnicity, religion, disability, and others. Based on the information presented, we outline recommendations for practitioners to improve doctoral education, such as preparing teaching assistants to manage discriminatory classroom conduct

Fair Meta-Learning: Learning How to Learn Fairly

Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-learning approaches in such situations. For models that can be trained through gradient descent, we demonstrate that there are some parameter configurations that allow models to be optimized from a few number of gradient steps and with minimal data which are both fair and accurate. To learn such weight sets, we adapt the popular MAML algorithm to Fair-MAML by the inclusion of a fairness regularization term. In practice, Fair-MAML allows practitioners to train fair machine learning models from only a few examples when data from related tasks is available. We empirically exhibit the value of this technique by comparing to relevant baselines.Comment: arXiv admin note: substantial text overlap with arXiv:1908.0909