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Machine Learning Algorithms In Political Research
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Book Synopsis Machine Learning Algorithms in Political Research by : Guy Freedman (Ph. D.)
Download or read book Machine Learning Algorithms in Political Research written by Guy Freedman (Ph. D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, political science has witnessed an explosion of data. Political scientists have begun turning to machine learning methods to provide reliable and scalable measurements of such large datasets. Building on the emerging literature on the use of machine learning in political science, I contribute four major lessons to the students and scholars who wish to make the most of these methods. These lessons include the advantage of treating machine learning as a process, combining text as data with standard data practices, the strength of pooling together supervised and unsupervised learning and the importance of understanding a model’s strengths and limits. Through two rigorous empirical chapters, I trace the process of machine learning in two case studies, with actual outcomes for two widely-used datasets in the discipline. The first centers on a model for identifying agency-creation in historical data of congressional hearings. In the second case study, I tackle a multi-classification problem of predicting one of 20 major policy topics (and over 220 minor topics) in congressional bills. I conclude with a look to the future of machine learning in the discipline as we shift from a first wave of the literature that served as an introduction to machine learning, to a second wave of utilizing machine learning in actual research on political data and the challenges that these data present
Book Synopsis The Democratization of Artificial Intelligence by : Andreas Sudmann
Download or read book The Democratization of Artificial Intelligence written by Andreas Sudmann and published by transcript Verlag. This book was released on 2019-10-31 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?
Book Synopsis Algorithms for the People by : Josh Simons
Download or read book Algorithms for the People written by Josh Simons and published by Princeton University Press. This book was released on 2023-01-10 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to put democracy at the heart of AI governance Artificial intelligence and machine learning are reshaping our world. Police forces use them to decide where to send police officers, judges to decide whom to release on bail, welfare agencies to decide which children are at risk of abuse, and Facebook and Google to rank content and distribute ads. In these spheres, and many others, powerful prediction tools are changing how decisions are made, narrowing opportunities for the exercise of judgment, empathy, and creativity. In Algorithms for the People, Josh Simons flips the narrative about how we govern these technologies. Instead of examining the impact of technology on democracy, he explores how to put democracy at the heart of AI governance. Drawing on his experience as a research fellow at Harvard University, a visiting research scientist on Facebook’s Responsible AI team, and a policy advisor to the UK’s Labour Party, Simons gets under the hood of predictive technologies, offering an accessible account of how they work, why they matter, and how to regulate the institutions that build and use them. He argues that prediction is political: human choices about how to design and use predictive tools shape their effects. Approaching predictive technologies through the lens of political theory casts new light on how democracies should govern political choices made outside the sphere of representative politics. Showing the connection between technology regulation and democratic reform, Simons argues that we must go beyond conventional theorizing of AI ethics to wrestle with fundamental moral and political questions about how the governance of technology can support the flourishing of democracy.
Book Synopsis Machine Learning for Experiments in the Social Sciences by : Jon Green
Download or read book Machine Learning for Experiments in the Social Sciences written by Jon Green and published by Cambridge University Press. This book was released on 2023-04-13 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference and machine learning are typically introduced in the social sciences separately as theoretically distinct methodological traditions. However, applications of machine learning in causal inference are increasingly prevalent. This Element provides theoretical and practical introductions to machine learning for social scientists interested in applying such methods to experimental data. We show how machine learning can be useful for conducting robust causal inference and provide a theoretical foundation researchers can use to understand and apply new methods in this rapidly developing field. We then demonstrate two specific methods – the prediction rule ensemble and the causal random forest – for characterizing treatment effect heterogeneity in survey experiments and testing the extent to which such heterogeneity is robust to out-of-sample prediction. We conclude by discussing limitations and tradeoffs of such methods, while directing readers to additional related methods available on the Comprehensive R Archive Network (CRAN).
Book Synopsis Computational Frameworks for Political and Social Research with Python by : Josh Cutler
Download or read book Computational Frameworks for Political and Social Research with Python written by Josh Cutler and published by Springer Nature. This book was released on 2020-04-22 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
Download or read book If...Then written by Taina Bucher and published by Oxford University Press. This book was released on 2018-06-05 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live in a world in which Google's search algorithms determine how we access information, Facebook's News Feed algorithms shape how we socialize, and Netflix collaborative filtering algorithms choose the media products we consume. As such, we live algorithmic lives. Life, however, is not blindly controlled or determined by algorithms. Nor are we simply victims of an ever-expanding artificial intelligence. Rather than looking at how technologies shape or are shaped by political institutions, this book is concerned with the ways in which informational infrastructure may be considered political in its capacity to shape social and cultural life. It looks specifically at the conditions of algorithmic life -- how algorithms work, both materially and discursively, to create the conditions for sociality and connectivity. The book argues that the most important aspect of algorithms is not what they are in terms of their specific technical details but rather how they become part of social practices and how different people enlist them as powerful brokers of information, communication and society. If we truly want to engage with the promises of automation and predictive analytics entailed by the promises of "big data", we also need to understand the contours of algorithmic life that condition such practices. Setting out to explore both the specific uses of algorithms and the cultural forms they generate, this book offers a novel understanding of the power and politics of algorithmic life as grounded in case studies that explore the material-discursive dimensions of software.
Book Synopsis Quantitative Social Science by : Kosuke Imai
Download or read book Quantitative Social Science written by Kosuke Imai and published by Princeton University Press. This book was released on 2021-03-16 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--
Download or read book Algorithms written by Tobias Matzner and published by Taylor & Francis. This book was released on 2023-10-02 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms. It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies. The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also “data doubles”. The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology. The examples include state-of-the-art applications of machine learning, such as self-driving cars, and large language models such as GPT. The book will be of interest for everyone engaging critically with algorithms, particularly in the social sciences, media studies, STS, political theory, or philosophy. With its broad scope it can serve as a high-level introduction that picks up and builds on more than two decades of critical research on algorithms.
Book Synopsis Computational Social Science by : R. Michael Alvarez
Download or read book Computational Social Science written by R. Michael Alvarez and published by Cambridge University Press. This book was released on 2016-03-10 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of cutting-edge approaches to computational social science.
Book Synopsis Algorithmic Reason by : Claudia Aradau
Download or read book Algorithmic Reason written by Claudia Aradau and published by Oxford University Press. This book was released on 2022-05-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism through which these transformations can be explored. Claudia Aradau and Tobias Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. They explore the emergence of algorithmic reason through rationalities, materializations, and interventions, and trace how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book provides a global trandisciplinary perspective on algorithmic operations, drawing on qualitative and digital methods to investigate controversies ranging from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany.
Book Synopsis Algorithmic Institutionalism by : Ricardo Fabrino Mendonca
Download or read book Algorithmic Institutionalism written by Ricardo Fabrino Mendonca and published by Oxford University Press. This book was released on 2023-11-14 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Institutionalism is the first book to conceive algorithms as institutions in contemporary societies, focusing on different dimensions of how they structure decision-making and enact power relations. In many situations in contemporary societies, algorithms structure social interactions, resulting in patterns of action and human behavior in collective contexts. Almeida, Filgueiras, and Mendonca discuss how algorithms are gradually occupying an institutional space in societies, deciding on different aspects of social life and shaping collective and individual human behaviors. As institutions, algorithms work as decision systems that define what is allowed, hindered, facilitated, or made impossible as well as positions within society's organizational structures. Algorithmic institutionalism uses the perspective of institutional theories to explain the functioning of these decision systems and how they establish patterns and norms that affect human behavior and lead to deep changes in contemporary society. The book points to the challenges of political orders that are gradually institutionalized with algorithms, comprising new dynamics of interaction between humans and machines. These disruptive dynamics of interaction between humans and machines create new challenges related to the democratization of algorithms and the impasses that emerge with technological advancement through digital technologies. Providing an analytical framework for an adequate comprehension of the social and political implications of algorithmic systems, Algorithmic institutionalism applies this framework to make sense of recommendation systems, the platformization of governments, and the deployment of algorithms in security. It then addresses the challenge of developing approaches to democratize the new political order influenced by the global expansion of algorithmic decision-making, pointing to key democratic values that are relevant once we consider the construction of legitimate decisions in contemporary societies.
Book Synopsis Discriminating Data by : Wendy Hui Kyong Chun
Download or read book Discriminating Data written by Wendy Hui Kyong Chun and published by MIT Press. This book was released on 2021-11-02 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.
Book Synopsis Discriminating Data by : Wendy Hui Kyong Chun
Download or read book Discriminating Data written by Wendy Hui Kyong Chun and published by MIT Press. This book was released on 2021-11-02 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.
Book Synopsis Handbook on the Politics and Governance of Big Data and Artificial Intelligence by : Andrej Zwitter
Download or read book Handbook on the Politics and Governance of Big Data and Artificial Intelligence written by Andrej Zwitter and published by Edward Elgar Publishing. This book was released on 2023-06-01 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance. This title contains one or more Open Access chapters.
Book Synopsis Towards an International Political Economy of Artificial Intelligence by : Tugrul Keskin
Download or read book Towards an International Political Economy of Artificial Intelligence written by Tugrul Keskin and published by Springer Nature. This book was released on 2021-07-01 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume seeks to leverage academic interdisciplinarity to develop insight into how Artificial intelligence (AI), the latest GPT to emerge, may influence or radically change socio-political norms, practices, and institutions. AI may best be understood as a predictive technology. “Prediction is the process of filling in missing information. Prediction takes information you have, often called ‘data’, and uses it to generate information you don’t have” (Agrawal, Gans, and Goldfarb 2018, 13; also see Mayer-Schonberger and Ramge 2018). AI makes prediction cheap because the cost of information is now close to zero. Cheap prediction through AI technologies are radically altering how we govern ourselves, interact with each other, and sustain society. Contributors to this volume represent the academic disciplines of Sociology and Political Science working within a diverse set of intra-disciplinary fields that when combined, yield novel insights into the following questions guiding this volume: How might AI transform people? How might AI transform socio-political practices? How might AI transform socio-political institutions?
Book Synopsis Algorithmic Regulation by : Karen Yeung
Download or read book Algorithmic Regulation written by Karen Yeung and published by Oxford University Press. This book was released on 2019-09-05 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the power and sophistication of of 'big data' and predictive analytics has continued to expand, so too has policy and public concern about the use of algorithms in contemporary life. This is hardly surprising given our increasing reliance on algorithms in daily life, touching policy sectors from healthcare, transport, finance, consumer retail, manufacturing education, and employment through to public service provision and the operation of the criminal justice system. This has prompted concerns about the need and importance of holding algorithmic power to account, yet it is far from clear that existing legal and other oversight mechanisms are up to the task. This collection of essays, edited by two leading regulatory governance scholars, offers a critical exploration of 'algorithmic regulation', understood both as a means for co-ordinating and regulating social action and decision-making, as well as the need for institutional mechanisms through which the power of algorithms and algorithmic systems might themselves be regulated. It offers a unique perspective that is likely to become a significant reference point for the ever-growing debates about the power of algorithms in daily life in the worlds of research, policy and practice. The range of contributors are drawn from a broad range of disciplinary perspectives including law, public administration, applied philosophy, data science and artificial intelligence. Taken together, they highlight the rise of algorithmic power, the potential benefits and risks associated with this power, the way in which Sheila Jasanoff's long-standing claim that 'technology is politics' has been thrown into sharp relief by the speed and scale at which algorithmic systems are proliferating, and the urgent need for wider public debate and engagement of their underlying values and value trade-offs, the way in which they affect individual and collective decision-making and action, and effective and legitimate mechanisms by and through which algorithmic power is held to account.
Book Synopsis Unsupervised Machine Learning for Clustering in Political and Social Research by : Philip D. Waggoner
Download or read book Unsupervised Machine Learning for Clustering in Political and Social Research written by Philip D. Waggoner and published by Cambridge University Press. This book was released on 2021-01-28 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.
