The Relative Importance of Institutional Trust in Countering Feelings of Unsafety in Disadvantaged Neighbourhoods

The segregated nature of urban areas reflects an uneven exposure to risk and unsafety. This article analyzes the relevance of place to people’s feelings of unsafety by comparing questionnaire responses from people living in a segregated, disadvantaged neighbourhood to a random sample of people living in the same city. The results suggest that the central factors explaining the individual’s feelings of unsafety differ in this particular neighbourhood compared to the broader population. The article shows that place has a moderating effect on feelings of unsafety. Trust in public institutions is argued to be particularly important in segregated, disadvantaged neighbourhoods because of its potential to prevent feelings of unsafety

Extreme overvalued beliefs: How violent extremist beliefs become “normalized”

Extreme overvalued beliefs (EOB) are rigidly held, non-deusional beliefs that are the motive behind most acts of terrorism and mass shootings. EOBs are differentiated from delusions and obsessions. The concept of an overvalued idea was first described by Wernicke and later applied to terrorism by McHugh. Our group of forensic psychiatrists (Rahman, Resnick, Harry) refined the definition as an aid in the differential diagnosis seen in acts of violence. The form and content of EOBs is discussed as well as group effects, conformity, and obedience to authority. Religious cults such as The People’s Temple, Heaven’s Gate, Aum Shinrikyo, and Islamic State (ISIS) and conspiracy beliefs such as assassinations, moon-hoax, and vaccine-induced autism beliefs are discussed using this construct. Finally, some concluding thoughts on countering violent extremism, including its online presence is discussed utilizing information learned from online eating disorders and consumer experience

Automatic Detection of Online Jihadist Hate Speech

We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.Comment: 31 page

Ethics by design and international soft and hard standards on the nexus gender-artificial intelligence

The contribution is intended to debate over the nexus between gender and artificial intelligence as for programs and systems based on AI which could produce, if the ethics by design is not approached according to a gender perspective, gender biases. The need for overcoming this criticality rests upon the need for improving the presence and participation of the female component in the design, development and implementation of the aforementioned programs and systems, in digital teams as members or leaders, to contribute for the elaboration of technical solutions within a legal framework which is aimed to translate current soft standards in force into hard laws

Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms

Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging gender-related information encoded in WEs. Through the critical lens of construct validity, we validate the proposed measure on synthetic and real collections. Subsequently, we use GSR to compare widely-used Information Retrieval ranking algorithms, including lexical, semantic, and neural models. We check if and how ranking algorithms based on WEs inherit the biases of the underlying embeddings. We also consider the most common debiasing approaches for WEs proposed in the literature and test their impact in terms of GSR and common performance measures. To the best of our knowledge, GSR is the first specifically tailored measure for IR, capable of quantifying representational harms.Comment: To appear in Information Processing & Managemen

Dynamics of Gender Bias in Computing

Gender bias in computing is a hard problem that has resisted decades of research. One obstacle has been the absence of systematic data that might indicate when gender bias emerged in computing and how it has changed. This article presents a new dataset (N=50,000) focusing on formative years of computing as a profession (1950-1980) when U.S. government workforce statistics are thin or non-existent. This longitudinal dataset, based on archival records from six computer user groups (SHARE, USE, and others) and ACM conference attendees and membership rosters, revises commonly held conjectures that gender bias in computing emerged during professionalization of computer science in the 1960s or 1970s and that there was a 'linear' one-time onset of gender bias to the present. Such a linear view also lent support to the "pipeline" model of computing's "losing" women at successive career stages. Instead, this dataset reveals three distinct periods of gender bias in computing and so invites temporally distinct explanations for these changing dynamics. It significantly revises both scholarly assessment and popular understanding about gender bias in computing. It also draws attention to diversity within computing. One consequence of this research for CS reform efforts today is data-driven recognition that legacies of gender bias beginning in the mid-1980s (not in earlier decades) is the problem. A second consequence is correcting the public image of computer science: this research shows that gender bias is a contingent aspect of professional computing, not an intrinsic or permanent one.Comment: 14 pages, 8 figure

Anyone can edit, not everyone does: Wikipedia and the gender gap

Feminist STS has long established that science’s provenance as a male domain continues to define what counts as knowledge and expertise. Wikipedia, arguably one of the most powerful sources of information today, was initially lauded as providing the opportunity to rebuild knowledge institutions by providing greater representation of multiple groups. However, less than ten percent of Wikipedia editors are women. At one level, this imbalance in contributions and therefore content is yet another case of the masculine culture of technoscience. This is an important argument and, in this article, we examine the empirical research that highlights these issues. Our main objective, however, is to extend current accounts by demonstrating that Wikipedia’s infrastructure introduces new and less visible sources of gender disparity. In sum, our aim here is to present a consolidated analysis of the gendering of Wikipedia

IT education as a factor to influence gender imbalances in computing: Comparing Russian and American experience

Introduction. The problem of the relatively small number of women professionally employed in computing (computer science and information technology) is relevant throughout the world. Despite the fact that IT professionals are widely in demand, women in many countries, including theUSA andRussia, make up no more than a quarter of their total number, which requires explanation. One of the major reasons for this phenomenon, according to the authors, lies in the education system. The aim of this article was to analyse the factors affecting gender imbalance in IT professions, by comparing two countries in which information technology has historically played an important role, and which are very different from each other in many ways – economic, political, educational system and others. Research methodology. The present research is based on the comparison of data on IT education in schools and universities, and the degree of involvement of girls and women in computing in theUSA andRussia. Results. Both in theUSA and inRussia, gender imbalances in IT professions are formed largely in the field of education. Cultural stereotypes about computing as a male-dominated profession are produced by the media. Such stereotypes can discourage some girls and young women from studying computer science and also result in imbalance formation. The education system needs to increase the confidence of girls and young women in the possibilities of realising their abilities in the field of computer science and information technologies. Educational institutions should help to eliminate the negative attitude towards girls’ choice of IT professions. Scientific novelty. For the first time, general factors in the field of education were identified that affect gender imbalances among IT professionals inRussia and theUSA – the countries with significantly different traditions and educational systems. Practical significance of the present work is to justify the conditions for improving school and university education to solve the problem of gender inequality in IT industry.Введение. Проблема относительно малой доли женщин, профессионально занятых в компьютинге (информатике и информационных технологиях), актуальна во всем мире. Несмотря на то, что ITспециалисты широко востребованы, женщины во многих странах, включая США и Россию, составляют не более четверти от их общего количества. Одна из причин указанного явления, по мнению авторов чрезвычайно весомая, кроется в системе образования. Цель данного исследования – анализ факторов, приводящих к гендерной диспропорции в IT-профессиях, с помощью сравнения опыта двух стран, в которых информационные технологии исторически играют важную роль и которые сильно отличаются друг от друга по многим параметрам – экономическим, политическим, системам образования и иным. Методология исследования – сопоставление данных о школьном и университетском IT-образовании в США и России и степени вовлеченности женщин в этих странах в область компьютерных технологий. Результаты работы. Показано, что и в США, и в России гендерные диспропорции в IT-отрасли формируются в значительной мере в образовательной среде. Их появлению способствуют также транслируемые масс-медиа социокультурные стереотипы о программировании как исключительно мужской профессии. Системе образования необходимо повышать уверенность девушек и молодых женщин в возможности реализации их способностей в сфере информатики и информационных технологий. Образовательные учреждения должны содействовать ликвидации негативного отношения к выбору девушками IT-профессий. Научная новизна. Впервые выявлены общие факторы в сфере образования, влияющие на гендерные диспропорции среди IT-специалистов в США и России – странах с существенно разными традициями и системами образования. Практическая значимость работы состоит в обосновании условий совершенствования школьного и университетского образования для решения проблемы гендерного неравенства в IT-отрасли

Empowering NGOs in Countering Online Hate Messages

Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategies to fight hate speech, in particular through the creation of counter-messages. While existing manual scrutiny and intervention strategies are time-consuming and not scalable, advances in natural language processing have the potential to provide a systematic approach to hatred management. In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyze social media data, along with a counter-narrative suggestion tool. Our platform aims at increasing the efficiency and effectiveness of operators' activities against islamophobia. We test the platform with more than one hundred NGO operators in three countries through qualitative and quantitative evaluation. Results show that NGOs favor the platform solution with the suggestion tool, and that the time required to produce counter-narratives significantly decreases.Comment: Preprint of the paper published in Online Social Networks and Media Journal (OSNEM