Event-based media monitoring methodology for Human Rights Watch

Executive Summary This report, prepared by a team of researchers from the University of Minnesota for Human Rights Watch (HRW), investigates the use of event-based media monitoring (EMM) to review its application, identify its strengths and weaknesses, and offer suggestions on how HRW can better utilize EMM in its own work. Media monitoring systems include both human-operated (manual) and automated systems, both of which we review throughout the report. The process begins with the selection of news sources, proceeds to the development of a coding manual (for manual searches) or “dictionary” (for automated searches), continues with gathering data, and concludes with the coding of news stories. EMM enables the near real-time tracking of events reported by the media, allowing researchers to get a sense of the scope of and trends in an event, but there are limits to what EMM can accomplish on its own. The media will only cover a portion of a given event, so information will always be missing from EMM data. EMM also introduces research biases of various kinds; mitigating these biases requires careful selection of media sources and clearly defined coding manuals or dictionaries. In manual EMM, coding the gathered data requires human researchers to apply codebook rules in order to collect consistent data from each story they read. In automated EMM, computers apply the dictionary directly to the news stories, automatically picking up the desired information. There are trade-offs in each system. Automated EMM can code stories far more quickly, but the software may incorrectly code stories, requiring manual corrections. Conversely, manual EMM allows for a more nuanced analysis, but the investment of time and effort may diminish the tool’s utility. We believe that both manual and automated EMM, when deployed correctly, can effectively support human rights research and advocacy

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making

CAPTIVE MARKETS": THE IMPACT OF KIDNAPPINGS ON CORPORATE INVESTMENT IN COLOMBIA"

This paper measures the impact of crime on firm investment by exploiting variation in kidnappings in Colombia from 1996 to 2002. Our central result is that firms invest less when kidnappings target firms. We also find that aggregate crime rates-homicides, guerrilla attacks, and general kidnappings-have no significant effect on investment. This finding alleviates concerns that our main result may be driven by unobserved variables that explain both overall criminal activity and investment. Furthermore, kidnappings that target firms reduce not only the investment of firms that sell in local markets, but also the investment of firms that sell in foreign markets. Thus, an unobservable correlation between poor demand conditions and criminal activity is unlikely to explain the negative impact of firm-related kidnappings on investment. Our results are consistent with the hypothesis that managers are reluctant to invest when their freedom and life are at risk; however, we cannot completely discard alternative explanations.Crime, Kidnappings, Investment, Colombia

Measuring Institutions: Indicators of Political and Economic Institutions in Namibia: 1884 - 2008

This paper presents a database on institutional measures for Namibia for the period 1884 to 2008. Using the techniques of principal components and factor analysis in aggregating these indicators, the study does two things. First, it illustrates a methodology for constructing de jure and de facto institutional measures by means of using pieces of legislation and quantitative data, respectively. Secondly, these indicators are used to assess the nature of political and economic institutional transformation from the colonial legacy to the modern outcome using Namibia as a natural experiment. The new indicators while covering a long time period (1884-2008), correlate fairly well with some of the widely used institutional indices produced by the Freedom House and the Heritage foundation.Namibia Institutional Indicators Political Freedom Property Rights Judicial Independence Political Instability

Risk factors and indicators for engagement in violent extremism

Research on terrorism is increasingly empirical and a number of significant advancements have been made. One such evolution is the emergent understanding of risk factors and indicators for engagement in violent extremism. Beyond contributing to academic knowledge, this has important real-world implications. Notably, the development of terrorism risk assessment tools, as well as behavioural threat assessment in counterterrorism. This thesis makes a unique contribution to the literature in two key ways. First, there is a general consensus that no single, stable profile of a terrorist exists. Relying on profiles of static risk factors to inform judgements of risk and/or threat may therefore be problematic, particularly given the observed multi- and equi-finality. One way forward may be to identify configurations of risk factors and tie these to the theorised causal mechanisms they speak to. Second, there has been little attempt to measure the prevalence of potential risk factors for violent extremism in a general population, i.e. base rates. Establishing general population base rates will help develop more scientifically rigorous putative risk factors, increase transparency in the provision of evidence, minimise potential bias in decision-making, improve risk communication, and allow for risk assessments based on Bayesian principles. This thesis consists of four empirical chapters. First, I inductively disaggregate dynamic person-exposure patterns (PEPs) of risk factors in 125 cases of lone-actor terrorism. Further analysis articulates four configurations of individual-level susceptibilities which interact differentially with situational, and exposure factors. The PEP typology ties patterns of risk factors to theorised causal mechanisms specified by a previously designed Risk Analysis Framework (RAF). This may be more stable grounds for risk assessment however than relying on the presence or absence of single factors. However, with no knowledge of base rates, the relevance of seemingly pertinent risk factors remains unclear. However, how to develop base rates is of equal concern. Hence, second, I develop the Base Rate Survey and compare two survey questioning designs, direct questioning and the Unmatched Count Technique (UCT). Under the conditions described, direct questioning yields the most appropriate estimates. Third, I compare the base rates generated via direct questioning to those observed across a sample of lone-actor terrorists. Lone-actor terrorists demonstrated more propensity, situational, and exposure risk factors, suggesting these offenders may differ from the general population in measurable ways. Finally, moving beyond examining the prevalence rates of single factors, I collect a second sample in order to model the relations among these risk factors as a complex, dynamic system. To do so, the Base Rate Survey: UK is distributed to a representative sample of 1,500 participants from the UK. I introduce psychometric network modelling to terrorism studies which visualises the interactions among risk factors as a complex system via network graphs

An inquiry into the theory, causes and consequences of monitoring indicators of health and safety at work

This paper engages in an interdisciplinary survey of the current state of knowledge related to the theory, determinants and consequences of occupational safety and health (OSH). First, it synthesizes the available theoretical frameworks used by economists and psychologists to understand the issues related to the optimal provision of OSH in the labour market. Second, it reviews the academic literature investigating the correlates of a comprehensive set of OSH indicators, which portray the state of OSH infrastructure (social security expenditure, prevention, regulations), inputs (chemical and physical agents, ergonomics, working time, violence) and outcomes (injuries, illnesses, absenteeism, job satisfaction) within workplaces. Third, it explores the implications of the lack of OSH in terms of the economic and social costs that are entailed. Finally, the survey identifies areas of future research interests and suggests priorities for policy initiatives that can improve the health and safety of workers

Socioeconomic, Institutional & Political Determinants of Human Rights Abuse: A Subnational Study of India, 1993-2002

We conduct an econometric analysis of socioeconomic, institutional and political factors determining government respect for human rights within India. Using time series crosssectional data for 28 Indian states for the period 1993 ñ 2002, we find that internal threat poised by number of social violence events, presence of civil war and riot hit disturbed areas are strongly associated with human rights abuses. Amongst socioeconomic factors, ëexclusiveí economic growth, ëunevení development, poor social development spending,youth bulges and differential growth rates between minority religious groups explain the likelihood of human rights violations. Capturing power at the state and central level by Hindu national partiesí viz., Bharatiya Janata Party (BJP) and Shiv Sena, further help understand the incidence of human rights violations within India. We also address the possible endogenity problem between human development and human rights. Using a system of simultaneous equation, we find that improvement in human development have positive impact on government respect for human rights within India.http://deepblue.lib.umich.edu/bitstream/2027.42/64388/1/wp926.pd