Discriminating Data
Download Discriminating Data full books in PDF, epub, and Kindle. Read online free Discriminating Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Wendy Hui Kyong Chun |
| Publisher | : MIT Press |
| Total Pages | : 341 |
| Release | : 2021-11-02 |
| Genre | : Technology & Engineering |
| ISBN | : 0262046229 |
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.
| Author | : Clemens Apprich |
| Publisher | : U of Minnesota Press |
| Total Pages | : 155 |
| Release | : 2018-11-13 |
| Genre | : Social Science |
| ISBN | : 1452959277 |
How do “human” prejudices reemerge in algorithmic cultures allegedly devised to be blind to them? How do “human” prejudices reemerge in algorithmic cultures allegedly devised to be blind to them? To answer this question, this book investigates a fundamental axiom in computer science: pattern discrimination. By imposing identity on input data, in order to filter—that is, to discriminate—signals from noise, patterns become a highly political issue. Algorithmic identity politics reinstate old forms of social segregation, such as class, race, and gender, through defaults and paradigmatic assumptions about the homophilic nature of connection. Instead of providing a more “objective” basis of decision making, machine-learning algorithms deepen bias and further inscribe inequality into media. Yet pattern discrimination is an essential part of human—and nonhuman—cognition. Bringing together media thinkers and artists from the United States and Germany, this volume asks the urgent questions: How can we discriminate without being discriminatory? How can we filter information out of data without reinserting racist, sexist, and classist beliefs? How can we queer homophilic tendencies within digital cultures?
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 335 |
| Release | : 2004-07-24 |
| Genre | : Social Science |
| ISBN | : 0309091268 |
Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€"pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination.
| Author | : Guy Stuart |
| Publisher | : Cornell University Press |
| Total Pages | : 284 |
| Release | : 2003 |
| Genre | : Business & Economics |
| ISBN | : 9780801440663 |
Mortgage lenders in the US, Stuart contends, are embedded in and shape a social context that can best be understood in terms of rules, networks and the production of space. This history of lenders' risk criteria reveals that they were synthesized from rules of thumb and untested theories.
| Author | : Pieter Kubben |
| Publisher | : Springer |
| Total Pages | : 219 |
| Release | : 2018-12-21 |
| Genre | : Medical |
| ISBN | : 3319997130 |
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
| Author | : Bart Custers |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 370 |
| Release | : 2012-08-11 |
| Genre | : Technology & Engineering |
| ISBN | : 3642304877 |
Vast amounts of data are nowadays collected, stored and processed, in an effort to assist in making a variety of administrative and governmental decisions. These innovative steps considerably improve the speed, effectiveness and quality of decisions. Analyses are increasingly performed by data mining and profiling technologies that statistically and automatically determine patterns and trends. However, when such practices lead to unwanted or unjustified selections, they may result in unacceptable forms of discrimination. Processing vast amounts of data may lead to situations in which data controllers know many of the characteristics, behaviors and whereabouts of people. In some cases, analysts might know more about individuals than these individuals know about themselves. Judging people by their digital identities sheds a different light on our views of privacy and data protection. This book discusses discrimination and privacy issues related to data mining and profiling practices. It provides technological and regulatory solutions, to problems which arise in these innovative contexts. The book explains that common measures for mitigating privacy and discrimination, such as access controls and anonymity, fail to properly resolve privacy and discrimination concerns. Therefore, new solutions, focusing on technology design, transparency and accountability are called for and set forth.
| Author | : Milkyway Media |
| Publisher | : Milkyway Media |
| Total Pages | : 29 |
| Release | : 2021-11-08 |
| Genre | : Study Aids |
| ISBN | : |
Get the summary from Wendy Hui Kyong Chun's Discriminating Data #1 The Cambridge Analytica scandal showed how social media can be abused and manipulate elections. #2 Psychographics superseded demographics, geographics, and economics in terms of impact. It was determined that people’s personalities could be changed with rational, yet fear-based messages. #3 The claims made by Cambridge Analytica, and many other companies that use psychographic targeting, need to be taken with several grains of salt. Their efficacy has not yet been proven.
| Author | : Ruha Benjamin |
| Publisher | : John Wiley & Sons |
| Total Pages | : 172 |
| Release | : 2019-07-09 |
| Genre | : Social Science |
| ISBN | : 1509526439 |
From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity. Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life. This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture. Visit the book's free Discussion Guide: www.dropbox.com
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 171 |
| Release | : 2016-09-03 |
| Genre | : Social Science |
| ISBN | : 0309439124 |
Estimates indicate that as many as 1 in 4 Americans will experience a mental health problem or will misuse alcohol or drugs in their lifetimes. These disorders are among the most highly stigmatized health conditions in the United States, and they remain barriers to full participation in society in areas as basic as education, housing, and employment. Improving the lives of people with mental health and substance abuse disorders has been a priority in the United States for more than 50 years. The Community Mental Health Act of 1963 is considered a major turning point in America's efforts to improve behavioral healthcare. It ushered in an era of optimism and hope and laid the groundwork for the consumer movement and new models of recovery. The consumer movement gave voice to people with mental and substance use disorders and brought their perspectives and experience into national discussions about mental health. However over the same 50-year period, positive change in American public attitudes and beliefs about mental and substance use disorders has lagged behind these advances. Stigma is a complex social phenomenon based on a relationship between an attribute and a stereotype that assigns undesirable labels, qualities, and behaviors to a person with that attribute. Labeled individuals are then socially devalued, which leads to inequality and discrimination. This report contributes to national efforts to understand and change attitudes, beliefs and behaviors that can lead to stigma and discrimination. Changing stigma in a lasting way will require coordinated efforts, which are based on the best possible evidence, supported at the national level with multiyear funding, and planned and implemented by an effective coalition of representative stakeholders. Ending Discrimination Against People with Mental and Substance Use Disorders: The Evidence for Stigma Change explores stigma and discrimination faced by individuals with mental or substance use disorders and recommends effective strategies for reducing stigma and encouraging people to seek treatment and other supportive services. It offers a set of conclusions and recommendations about successful stigma change strategies and the research needed to inform and evaluate these efforts in the United States.
| Author | : Gary S. Becker |
| Publisher | : University of Chicago Press |
| Total Pages | : 178 |
| Release | : 2010-08-15 |
| Genre | : Business & Economics |
| ISBN | : 0226041042 |
This second edition of Gary S. Becker's The Economics of Discrimination has been expanded to include three further discussions of the problem and an entirely new introduction which considers the contributions made by others in recent years and some of the more important problems remaining. Mr. Becker's work confronts the economic effects of discrimination in the market place because of race, religion, sex, color, social class, personality, or other non-pecuniary considerations. He demonstrates that discrimination in the market place by any group reduces their own real incomes as well as those of the minority. The original edition of The Economics of Discrimination was warmly received by economists, sociologists, and psychologists alike for focusing the discerning eye of economic analysis upon a vital social problem—discrimination in the market place. "This is an unusual book; not only is it filled with ingenious theorizing but the implications of the theory are boldly confronted with facts. . . . The intimate relation of the theory and observation has resulted in a book of great vitality on a subject whose interest and importance are obvious."—M.W. Reder, American Economic Review "The author's solution to the problem of measuring the motive behind actual discrimination is something of a tour de force. . . . Sociologists in the field of race relations will wish to read this book."—Karl Schuessler, American Sociological Review
