"The role of algorithms in propelling conspiracy theories and radicalisation has been brought into sharp focus by the interlocking crises of the past 12 months. Social media platforms have sought to tamp down on algorithmic recommendation of conspiracy theories and extremist content, for example by
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preventing conspiracy-linked hashtags from trending or stopping certain groups and pages from being recommended to other users." (Introduction)
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"YouTube is the second-most visited website in the world, and its algorithm drives 70% of watch time on the platform—an estimated 700 million hours every single day. For years, that recommendation algorithm has helped spread health misinformation, political disinformation, hateful diatribes, and o
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ther regrettable content to people around the globe. YouTube’s enormous influence means these films reach a huge audience, having a deep impact on countless lives, from radicalization to polarization [...] 37,380 YouTube users stepped up as YouTube watch dogs, volunteering data about the regrettable experiences they have on YouTube for Mozilla researchers to carefully analyze. As a result, Mozilla gained insight into a pool of YouTube's tightly-held data in the largest-ever crowdsourced investigation into YouTube's algorithm. Collectively, these volunteers flagged 3,362 regrettable videos, coming from 91 countries, between July 2020 and May 2021. This report highlights what we learned from our RegretsReporter research. Specifically, we uncovered three main findings: 1. YouTube Regrets are disparate and disturbing. Our volunteers reported everything from Covid fear-mongering to political misinformation to wildly inappropriate "children's" cartoons. The most frequent Regret categories are misinformation, violent or graphic content, hate speech, and spam/scams. 2. The algorithm is the problem. 71% of all Regret reports came from videos recommended to our volunteers by YouTube's automatic recommendation system. Further, recommended videos were 40% more likely to be reported by our volunteers than videos that they searched for. And in several cases, YouTube recommended videos that actually violate their own Community Guidelines and/or were unrelated to previous videos watched. 3. Non-English speakers are hit the hardest. The rate of YouTube Regrets is 60% higher in countries that do not have English as a primary language (with Brazil, Germany and France being particularly high), and pandemic-related Regrets were especially prevalent in non-English languages." (Executive summary)
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"Beim so genannten „Scoring“ wird einer Person mithilfe algorithmischer Verfahren ein Zahlenwert zugeordnet, um ihr Verhalten zu bewerten und zu beeinflussen. „Super-Scoring“-Praktiken gehen noch weiter und führen Punktesysteme und Skalen aus unterschiedlichen Lebensbereichen zusammen, wie
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etwa Bonität, Gesundheitsverhalten oder Lernleistungen. Diese Ver-fahren könnten sich zu einem neuen und übergreifenden Governance-Prinzip in der digitalen Gesellschaft entwickeln. Ein besonders prominentes Beispiel ist das Social Credit System in China. Aber auch in westlichen Gesellschaften gewinnen Scoring-Praktiken und digitale Soziometrien an Bedeutung. Dieser Open Access Band stellt aktuelle Beispiele von datengetriebenen sozialen Steuerungs-prozessen aus verschiedenen Ländern vor, diskutiert ihre normativen Grundlagen und gesell-schaftspolitischen Auswirkungen und gibt erste bildungspolitische Empfehlungen. Wie ist der aktuelle Stand einschlägiger Praktiken in China und in westlichen Gesellschaften? Wie sind die individuellen und sozialen Folgen zu bewerten? Wie wandelt sich das Bild vom Menschen und wie sollte bereits heute die politische und aufklärerische Bildung darauf reagieren?" (Buchrückseite)
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"Invisible Women shows us how, in a world largely built for and by men, we are systematically ignoring half the population. It exposes the gender data gap - a gap in our knowledge that is at the root of perpetual, systemic discrimination against women, and that has created a pervasive but invisible
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bias with a profound effect on women's lives. Award-winning campaigner and writer Caroline Criado Perez brings together for the first time an impressive range of case studies, stories and new research from across the world that illustrate the hidden ways in which women are forgotten, and the impact this has on their health and well-being. From government policy and medical research, to technology, workplaces, urban planning and the media, Invisible Women reveals the biased data that excludes women. In making the case for change, this powerful and provocative book will make you see the world anew." (Back cover)
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"El objetivo de esta investigación es el desarrollo de una guía de recolección de datos de migración con el fin entender mejor las características de la población migrante. Las recomendaciones de este trabajo surgen a partir de tres casos de estudio en ciudades/regiones fronterizas: Monterrey
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(México); Cúcuta (Colombia) y la Región Huetar Norte de Costa Rica. Este trabajo contribuye a identificar qué datos se producen sobre personas migrantes, quién los produce y cómo se manejan. A través de la investigación, se puede observar que los datos migratorios que se producen son principalmente de entradas/salidas, y hay una falta de recopilación y/o publicación de datos sobre las poblaciones migrantes en cada país. Debido a esto se realiza una serie de recomendaciones y una guía para la recopilación y manejo de datos de personas migrantes, para señalar los datos que pueden ser útiles para la creación de políticas públicas de integración y asentamiento." (Resumen ejecutivo)
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"Many of the significant developments of our era have resulted from advances in technology, including the design of large-scale systems; advances in medicine, manufacturing, and artificial intelligence; the role of social media in influencing behaviour and toppling governments; and the surge of onli
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ne transactions that are replacing human face-to-face interactions. These advances have given rise to new kinds of ethical concerns around the uses (and misuses) of technology. This collection of essays by prominent academics and technology leaders covers important ethical questions arising in modern industry, offering guidance on how to approach these dilemmas. Chapters discuss what we can learn from the ethical lapses of #MeToo, Volkswagen, and Cambridge Analytica, and highlight the common need across all applications for sound decision-making and understanding the implications for stakeholders. Technologists and general readers with no formal ethics training and specialists exploring technological applications to the field of ethics will benefit from this overview." (Publisher description)
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"Paul Nemitz und Matthias Pfeffer zeigen eindrücklich, wie die derzeitigen Versuche ethischer Regulierung von Künstlicher Intelligenz zu kurz greifen. Nemitz ist Mitglied der Datenethikkommission der Bundesregierung und war massgeblich verantwortlich für die Einführung der EU-Datenschutzgrundver
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ordnung. Pfeffer beschäftigt sich als freier TV-Journalist und Produzent mit dem Thema Künstliche Intelligenz. Die Autoren bieten eine genaue Analyse und legen dabei den Schwerpunkt auf die Rolle der Öffentlichkeit und die Gefährdung des Journalismus in digitalen Zeiten. Sie fordern die strikte Regulierung Künstlicher Intelligenz und eine Neubesinnung auf das Prinzip Mensch, das gegen das Prinzip Maschine verteidigt werden muss. Ihr Buch schließt mit klaren Handlungsempfehlungen an Politik, Zivilgesellschaft und insbesondere an die technische Intelligenz." (Verlagsbeschreibung)
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"China’s Integrated Joint Operations Platform (IJOP) operates in Xinjiang by collecting Big Data and alerting authorities to those it deems potentially harmful to the CCP regime. It does so through two major devices: the mobile phone, and the camera. These act as tools of disablement constraining
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Uighur mobility and settlement. Uighurs are now obligated to carry smartphones, on which police mandate “nanny apps” to monitor Uighurs through their devices. The Jingwang (“cleansing the web”) app not only tracks Uighurs’ movement, but also records and extracts all messages, internet use, contacts, photographs, and files. These are then amalgamated by IJOP which uses keyword searches to compare the data to its list of potential crimes, which include prayer, visiting banned websites, and other petty accusations. IJOP then decides who is considered a threat and will thus be arrested, and who will simply continue to be monitored." (Page 6)
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"This guide has explored some of the key considerations that should inform the conceptualization and implementation of Machine Learning (ML) and artifical intelligence (AI) components within a development project. New, automated decision systems can offer considerable and rapid efficiency gains, but
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we must always remember that they embed numerous and ongoing human decisions. These may be intentional or unintentional, benevolent or malicious, general or highly context specific. As with physical infrastructure such as roads and bridges, digital infrastructure can all too easily encode unexamined bias – sometimes in ways that can undermine development gains. As outlined in this guide, a wide variety of decisions need to be made at different stages of the project lifecycle: from which stakeholders should be involved and how, to measuring model accuracy and success, to determining overall whether ML is an appropriate tool to use for your development context. There is no one-size-fits all answer to these questions. But whatever the specific ML/AI technologies and applications you consider, broad guidance is offered in the four thematic areas woven throughout this guide: Responsible, equitable, and inclusive design; Strategic partnerships and human capital; Adaptive management; Enabling environment for ML/AI. These focal points should help you and your project team make the best possible choices at each stage of the project life cycle." (Conclusion)
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"Understanding and improving the science behind the algorithms that run our lives is quickly becoming one of the most pressing issues of this century. Traditional solutions, such as laws, regulations and watchdog groups, have proven woefully inadequate, at best. Derived from the cutting-edge of scie
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ntific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Weaving together the science behind algorithm design with stories of citizens, lawyers, scientists, and activists experiencing the trial-and-error of research in real-time, Michael Kearns and Aaron Roth present a strikingly original way forward, showing how we can begin to work together to protect people from the unintended impacts of algorithms—and, sometimes, protect the science that could save us from ourselves." (Publisher description)
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"Was wollen wir mit den Thesen erreichen? Sie sind eine Momentaufnahme auf die Digitalität. Sie müssen ständig fortgeschrieben werden, weil die Entwicklungen ungeheuer agil sind. Sie beinhalten auch keine umfassende theologische Deutung der Digitalität. Die vorliegenden Thesen sind aber ein Disk
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ussionsbeitrag, der die Schnittmenge zwischen Digitalität und KI einerseits und dem christlichen Menschenbild andererseits, das den Menschen in seiner Transzendenz versteht, skizziert. Als Kirche stecken wir also quasi einen Claim in Sachen Digitalität ab. Zusammenfassend stelle ich fest: Digitalität und KI müssen nicht in einer Dystopie enden. Sie können für das Wohl der Menschen und unserer Gesellschaft eine große Wirkung entfalten. Unter dem Anspruch menschendienlicher und sachgerechter Technologie-Gestaltung muss sich keiner vor dieser technischen Umwälzung fürchten. Denn Digitalität und Künstliche Intelligenz stehen im Dienst des Geist-begabten und Selbst-bewussten Menschen. Kommunikations- und Medienkompetenz helfen bei deren Entfaltung." (Bischof Gebhard Fürst, Seite 2)
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"Spatially and temporally relevant ‘big data’ that does not require data collection in the field has the potential to provide insights into people’s economic, social, behavioural and political lives, and hence could be used in measuring key development outcomes. Big data consists of humangener
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ated data including online searches, social media, citizen reporting or crowdsourced data, process-mediated data such as mobile phone call record details (CRD), commercial transactions data and machine-generated data from satellites, sensors or drones. The primary value of big data is that it is possible to measure outcomes that could not previously be measured using household surveys at the required temporal and spatial scale. The potential of big data to answer causal attribution, however, is still not widely understood, especially in low- and middle-income countries (L&MICs). The report is based on a map of the studies using big data and its objective is to discuss methodological, ethical and practical constraints relating to the use of big data. The systematic map includes impact evaluations (IEs) that use big data to evaluate development outcomes, systematic reviews (SRs) of big data IEs and other measurement studies that innovatively use big data to measure and validate any development outcomes. This study also explores the sectoral and geographical spread of big data's use in international development. This map includes studies written in English and published between 2005 and 2019, regardless of the target country's income level or population's status. We provide detailed breakdowns on the map for different country income classifications, fragile contexts and population characteristics. From the initial list of 17,393 studies we arrived at a final list of 437 studies, which included 48 IEs, 381 measurement studies and 8 SRs." (Executive summary)
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"Gender equality and the empowerment of women and girls, one of the Sustainable Development Goals, is a highly complex and challenging undertaking. We must address multiple issues—discrimination, violence, education, employment, economic resources, and technology—and work across economic sectors
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, from agriculture to financial services. Achieving gender equality will require significant amounts of accurate data about the situations and struggles of women and girls. Globally, however, there is a major gap in data that is disaggregated by sex, and this gap often renders women’s societal, cultural, and economic contributions and obstacles practically invisible. It can also exacerbate existing gender divides, feeding and reinforcing biases in social programs, access to financial and other services, economic opportunities, and even development programs designed to address gender inequality. Part of the solution may be in the form of big data, which, if used effectively, can provide the volume of data needed to portray women and their situations accurately, which in turn can inform the creation of evidence-based solutions." (Page 1)
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"[Der Autor] spürt den paradoxen und tragischen Momenten nach, zu denen die menschlichen Bemühungen einer technischen Beherrschung der Welt geführt haben. So zeigt er auf, wie Navigationssysteme Touristen in den Ozean leiten, Autopiloten Flugzeugabschüsse provozieren, algorithmisierter Hochfrequ
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enzhandel Börsencrashs auslöst und eine zu große Datenfülle wissenschaftlichen Fortschritt hemmt. Unsere Abhängigkeit von technologischen Systemen sei, so Bridle, jedoch nicht einfach rückgängig zu machen. Vielmehr plädiert er dafür, diese überhaupt erst ins Bewusstsein zu bringen. Dies gelinge nur durch ein Denken, das sich von der Vorstellung einer perfekten Berechenbarkeit der Welt verabschiedet und die komplexen Interaktionen zwischen natürlicher Umwelt, Technik und unserer sozialen Lebenswelt in den Blick nimmt." (Verlagsbeschreibung)
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