{"results":[{"id":"ss_e89dfa306723e8ef031765e9c44e5f6f94fd8fda","title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","authors":[{"name":"Tim Miller"}],"abstract":"There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Computer Science"],"doi":"10.1016/J.ARTINT.2018.07.007","url":"https://www.semanticscholar.org/paper/e89dfa306723e8ef031765e9c44e5f6f94fd8fda","pdf_url":"https://doi.org/10.1016/j.artint.2018.07.007","is_open_access":true,"citations":5059,"published_at":"","score":91},{"id":"ss_76b698c03ef770ce0c63c2d89e8f76917843bf82","title":"The Reviewer’s Guide to Quantitative Methods in the Social Sciences","authors":[{"name":"G. Hancock"},{"name":"R. Mueller"},{"name":"Laura M. Stapleton"}],"abstract":"The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its 31 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include:","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Psychology"],"doi":"10.4324/9780203861554","url":"https://www.semanticscholar.org/paper/76b698c03ef770ce0c63c2d89e8f76917843bf82","is_open_access":true,"citations":868,"published_at":"","score":88.03999999999999},{"id":"ss_48fa66a9066a1188da5385100585256974104a9b","title":"Network Analysis in the Social Sciences","authors":[{"name":"Stephen P. Borgatti"},{"name":"Ajay Mehra"},{"name":"Daniel J. Brass"},{"name":"G. Labianca"}],"abstract":"","source":"Semantic Scholar","year":2009,"language":"en","subjects":["Medicine","Sociology"],"doi":"10.1126/science.1165821","url":"https://www.semanticscholar.org/paper/48fa66a9066a1188da5385100585256974104a9b","pdf_url":"http://www.ajaymehra.net/Documents/SNA_Review_for_Science.pdf","is_open_access":true,"citations":4046,"published_at":"","score":83},{"id":"ss_89ff89180764b44bc8c4f474777e5cbcb840ac85","title":"Complexity Theory and the Social Sciences","authors":[{"name":"D. Byrne"}],"abstract":"This expanded and updated edition of Complexity Theory and the Social Sciences: The State of the Art revisits the use of complexity theory across the social sciences and demonstrates how complexity informs approaches to various contemporary issues in the context of the COVID-19 pandemic, widening social inequality, and impending social and ecological catastrophe wrought by global warming. The book reviews complexity theory in the practice of the social sciences and at their interface with ecological science. It outlines how social theory can be reconciled with complexity thinking and presents a review of the way research can be done using complexity theory. The book suggests how complexity theory can be used to understand and evaluate governance processes, particularly with regard to social inequality and the climate crisis. The impact of the COVID-19 pandemic is also examined through a complexity lens, reviewing how complexity thinking has been employed in relation to the pandemic and how implementing a complexity framework can transform health and social care. The book concludes with a call to action and the use of complexity theory to inform critical thinking in the education system. This textbook will be immensely useful to students and researchers interested in social research methods, social theory, business and organization studies, health, education, urban studies, and development studies. © 2023 David Byrne and Gillian Callaghan.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Geography"],"doi":"10.4324/9781003213574","url":"https://www.semanticscholar.org/paper/89ff89180764b44bc8c4f474777e5cbcb840ac85","is_open_access":true,"citations":563,"published_at":"","score":82.89},{"id":"ss_5f3dde21c6c90fc7b50842cb77bee6f8d4fc21a7","title":"Qualitative Research Methods for the Social Sciences","authors":[{"name":"B. 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Lamont"},{"name":"Virág Molnár"}],"abstract":"","source":"Semantic Scholar","year":2002,"language":"en","subjects":["Sociology"],"doi":"10.1146/ANNUREV.SOC.28.110601.141107","url":"https://www.semanticscholar.org/paper/6f6039d27c655f7059c973de3444222d4a984f38","is_open_access":true,"citations":4328,"published_at":"","score":80},{"id":"ss_fe74b7354416689d4f28fd8e2f6d9113e3d452c9","title":"Research methods in the social sciences","authors":[{"name":"B. Somekh"},{"name":"C. Lewin"}],"abstract":"","source":"Semantic Scholar","year":2005,"language":"en","subjects":["Sociology"],"doi":"10.1037/e548842006-001","url":"https://www.semanticscholar.org/paper/fe74b7354416689d4f28fd8e2f6d9113e3d452c9","is_open_access":true,"citations":3252,"published_at":"","score":80},{"id":"ss_60428de0b244bdf0242628a531071704017463a4","title":"Interviews in the social sciences","authors":[{"name":"Eleanor Knott"},{"name":"Aliya Hamid Rao"},{"name":"K. Summers"},{"name":"Chana Teeger"}],"abstract":"","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"doi":"10.1038/s43586-022-00150-6","url":"https://www.semanticscholar.org/paper/60428de0b244bdf0242628a531071704017463a4","pdf_url":"http://eprints.lse.ac.uk/115656/1/nature_primer_interviews_preproof.pdf","is_open_access":true,"citations":401,"published_at":"","score":78.03},{"id":"ss_569fc5bf38b0811f0d0635a75831721e4362a401","title":"Game Theory for the Social Sciences: Conflict, Bargaining, Cooperation and Power","authors":[{"name":"Pierre Dehez"}],"abstract":"","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.1007/978-3-031-58241-7","url":"https://www.semanticscholar.org/paper/569fc5bf38b0811f0d0635a75831721e4362a401","is_open_access":true,"citations":163,"published_at":"","score":72.89},{"id":"ss_d26caa4015f8c37ef699206013db5a0f40cf3cb4","title":"Text as Data: A New Framework for Machine Learning and the Social Sciences","authors":[{"name":"K. Freeman"}],"abstract":"At its most fundamental, ‘‘social science is the process of creating generalizable knowledge that explains or predicts societal patterns’’ (p. 264). Text as Data: A New Framework for Machine Learning and the Social Sciences seeks to provide readers with a model to do just this, but with a relatively untapped form of data, at least for the social sciences. Using text as data happens frequently in the computer science world, and Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart, the authors of this text, seek to extend known computer science methodology to align with social science methodological principles. The authors bridge this gap by applying our methodological models (some of them, at least) to this novel, timerelevant, and expanding form of data. This is an ambitious text that, at different stages, provides critical insight for undergraduates, graduate students across the social sciences, and practitioners. Text as Data systematically walks readers through the research process, from selection and representation to discovery to measurement and, finally, to inference and prediction. In the first section of the text, they concisely detail this model of research and the justifications behind it for the more novice scholars. The text then introduces each stage of this research process, laying out the assumptions and best practices informing this specific approach with text as data. Common to all of these introductory chapters is the emphasis on the crucial role of the human researcher. The authors do not shy away from a common fear in analyses with ‘‘big data,’’ that human work is becoming obsolete and theory is disappearing. Instead, they make a compelling case that although the analytic processes necessitated by ‘‘big data’’ may seem (and sometimes even be named) as if computers are operating independently of theory and of humans, the social science project will only succeed with the continued and constant engagement of the human-generated ideas behind the projects. Following each of these introductory chapters that adeptly frame the overall endeavor and lay out the novel application of research methods to text data, the authors present a thorough overview of the many ways in which practitioners can pursue research with text data. Here, the authors present work that has already been done in the social sciences (e.g., authorship of the Federalist papers, identifying a model of Congressional ideology from press releases, authorship and tone of tweets from former President Trump) and also work through one or more basic algorithms to link the reader to the algebraic and mathematical progressions that provide the foundation for machine learning (or other similarly opaque procedures). Concluding these detailed presentations of possible steps through the research process, the text progresses to the next step in the research process (i.e., from measurement to inference), clearly linking and overlapping these processes where appropriate. Often methodological training in the social sciences bends in the direction of either inductive or deductive research. Researchers seek, often going to extreme measures, to justify their conceptualization, operationalization, modeling, and interpretation choices prior to embarking on analytical procedures in order to avoid questions of over-fitting, p-hacking, and the like. Alternatively, researchers embark on scholarly pursuits to build theory emerging from their research sites and informants, often utilizing only qualitative techniques to do so. Especially in elementary methodological training, these two tracks are distinct and, sometimes, juxtaposed as opposites. Not so in this text, where the authors use the emergent and exciting field of text data to emphasize the importance of iterative and sequential scholarship. The authors showcase across these four stages of the research process the opportunities for building a comprehensive research agenda that celebrates multiple approaches and Reviews 347","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.1177/00943061231181317p","url":"https://www.semanticscholar.org/paper/d26caa4015f8c37ef699206013db5a0f40cf3cb4","is_open_access":true,"citations":162,"published_at":"","score":71.86},{"id":"ss_91e507ec9f591b5b74883bed2cf987c90a2d3261","title":"Likert Scale in Social Sciences Research: Problems and Difficulties","authors":null,"abstract":"The Likert scale is one of the essential rating scales used as a measurement tool in social sciences research, especially in the qualitative approach. Unfortunately, this scale has a great deal of controversy surrounding how data is obtained from Likert questionnaires and the appropriate statistical analysis of these data. A systematic review was performed to address this issue. Research publications from various recognized national and international articles served as research objects. This paper provides a comprehensive study of the two-perspective of the rating scales based on measurement experts, statisticians, education researchers, and other practitioners. The experts’ opinions, analyses, suggestions, and solutions are obtained from journal articles, proceedings, theses, and books. After reading this article, the reader should be able to know that the accurate Likert scale produces data intervals for social sciences research. However, some requirements must be considered, specifically the composite score, midpoint, and the number of points. If these conditions are implemented, statistical methods, parametric and non- parametric, can be used to analyze the data depending on the research purpose","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"doi":"10.51709/19951272/winter2022/7","url":"https://www.semanticscholar.org/paper/91e507ec9f591b5b74883bed2cf987c90a2d3261","pdf_url":"https://doi.org/10.51709/19951272/winter2022/7","is_open_access":true,"citations":179,"published_at":"","score":71.37}],"total":19880257,"page":1,"page_size":20,"sources":["CrossRef","DOAJ","Semantic Scholar"],"query":"Social Sciences"}