This book shows how a machine political, local Democratic organization in Brooklyn failed to make the transition necessary to survive in modern urban political life. Political organizations do not live in a sociological vacuum. They must struggle for survival in a highly competitive human environment. The story of the Madison Club tells how the ethnic, religious, racial and generational transitions affect decisions, group cohesion and the success of political organizations at all levels.
Paul Frymer argues provocatively that two-party competition in the United States leads to the marginalization of African Americans and the subversion of democracy. Scholars have long claimed that the need to win elections makes candidates, parties, and government responsive to any and all voters. Frymer shows, however, that party competition is centered around racially conservative white voters, and that this focus on white voters has dire consequences for African Americans. As both parties try to attract white swing voters by distancing themselves from blacks, black voters are often ignored and left with unappealing alternatives. African Americans are thus the leading example of a "captured minority." Frymer argues that our two-party system bears much of the blame for this state of affairs. Often overlooked in current discussions of racial politics, the party system represents a genuine form of institutional racism. Frymer shows that this is no accident, for the party system was set up in part to keep African American concerns off the political agenda. Today, the party system continues to restrict the political opportunities of African American voters, as was shown most recently when Bill Clinton took pains to distance himself from African Americans in order to capture conservative votes and win the presidency. Frymer compares the position of black voters with other social groups--gays and lesbians and the Christian right, for example--who have recently found themselves similarly "captured." Rigorously argued and researched, Uneasy Alliances is a powerful challenge to how we think about the relationship between black voters, political parties, and American democracy.
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
Why do some ethnic parties succeed in attracting the support of their target ethnic group while others fail? In a world in which ethnic parties flourish in both established and emerging democracies alike, understanding the conditions under which such parties rise and fall is of critical importance to both political scientists and policy makers. Drawing on a study of variation in the performance of ethnic parties in India, this book builds a theory of ethnic party performance in 'patronage democracies'. Chandra shows why individual voters and political entrepreneurs in such democracies condition their strategies not on party ideologies or policy platforms, but on a headcount of co-ethnics and others across party personnel and among the electorate.
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.
Two trends are dramatically altering the American political landscape: growing immigration and the rising prominence of independent and nonpartisan voters. Examining partisan attachments across the four primary racial groups in the United States, this book offers the first sustained and systematic account of how race and immigration today influence the relationship that Americans have--or fail to have--with the Democratic and Republican parties. Zoltan Hajnal and Taeku Lee contend that partisanship is shaped by three factors--identity, ideology, and information--and they show that African Americans, Asian Americans, Latinos, and whites respond to these factors in distinct ways. The book explores why so many Americans--in particular, Latinos and Asians--fail to develop ties to either major party, why African Americans feel locked into a particular party, and why some white Americans are shut out by ideologically polarized party competition. Through extensive analysis, the authors demonstrate that when the Democratic and Republican parties fail to raise political awareness, to engage deeply held political convictions, or to affirm primary group attachments, nonpartisanship becomes a rationally adaptive response. By developing a model of partisanship that explicitly considers America's new racial diversity and evolving nonpartisanship, this book provides the Democratic and Republican parties and other political stakeholders with the means and motivation to more fully engage the diverse range of Americans who remain outside the partisan fray.
What really happened when citizens were asked to participate in their community’s poverty programs? In this revealing new book, the authors provide an answer to this question through a systematic empirical analysis of a single public policy issue—citizen participation in the Community Action Program of the Johnson Administration’s “War on Poverty.” Beginning with a brief case study description and analysis of the politics of community action in each of America’s five largest cities—New York, Chicago, Los Angeles, Detroit, and Philadelphia—the authors move on to a fascinating examination of race and authority structures in our urban life. In a series of lively chapters, Professors Greenstone and Peterson show how the coalitions that formed around the community action question developed not out of electoral or organizational interests alone, but were strongly influenced by our conceptions of the nature of authority in America. They discuss the factors that affected the development of the action program and they note that democratic elections of low-income representatives, however much preferred by democratic reformers, were an ineffective way of representing the interests of the poor. The book stresses the way in which both machine and reform structures affected the ability of minority groups to organize effectively and to form alliances in urban politics. It considers the wide-ranging critiques made of the Community Action Program by conservative, liberal, and radical analysts and finds that all of them fail to appreciate the significance and intensity of the racial cleavage in American politics.
This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."
