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Is Artificial Intelligence Racist? by Arshin Adib-Moghaddam Pdf
How did racism creep into the algorithms that govern our daily lives, from banking and shopping, to job applications? Connecting the legacy of enlightenment racism to forms of discrimination in modern day algorithms and Artificial Intelligence, this volume examines what data feeds into AI technology - and how this data will shape the future of humanity. Delving into the narratives enveloping the development of AI systems, with a particular emphasis on "tech-giants" and the ideas of Mark Zuckerberg, Elon Musk and Bill Gates, Arshin Adib-Moghaddam explains how and why technology aids and abets various forms of extremism, entrenches social hierarchies and discriminatory boundaries and how this will impact international security and human rights in the future.
Algorithms of Oppression by Safiya Umoja Noble Pdf
Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author
Hidden in White Sight by Calvin Lawrence (Computer scientist) Pdf
"Artificial Intelligence was meant to be the great social equalizer to help promote fairness by removing human bias. Technology has no emotions or feelings, so it should be void of prejudice by definition alone. From healthcare services to social scoring in exams, to applying for and getting loans, AI outcomes often restrict, restrain, and deny those most in need of those services. In this book, Calvin Lawrence, an engineer specializing in AI, illuminates how unintended models and algorithms can wreak havoc on the unsuspected. The book explores how subtle AI biases seep into the innermost realms of our everyday lives. The book also includes solutions to address these issues, including a proposed AI Bill of Rights"--
Artificial Intelligence was meant to be the great social equalizer that helps promote fairness by removing human bias from the equation, but is this true? Given that the policing and judicial systems can display human bias, this book explores how the technology they use can also reflect these prejudices. From healthcare services to social scoring in exams, to applying for and getting loans, AI outcomes often restrict those most in need of these services. Through personal stories from an esteemed Black Data Scientist and AI expert, this book attempts to demystify the algorithmic black box. AI pervades all aspects of modern society and affects everyone within it, yet its internal biases are rarely confronted. This book advises readers on what they can do to fight against it, including the introduction of a proposed AI Bill of Rights, whilst also providing specific recommendations for AI developers and technologists. https://hiddeninwhitesight.com/
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 here.
Is Artificial Intelligence Racist? by Arshin Adib-Moghaddam Pdf
How did racism creep into the algorithms that govern our daily lives, from banking and shopping, to job applications? Connecting the legacy of enlightenment racism to forms of discrimination in modern day algorithms and Artificial Intelligence, this volume examines what data feeds into AI technology - and how this data will shape the future of humanity. Delving into the narratives enveloping the development of AI systems, with a particular emphasis on "tech-giants" and the ideas of Mark Zuckerberg, Elon Musk and Bill Gates, Arshin Adib-Moghaddam explains how and why technology aids and abets various forms of extremism, entrenches social hierarchies and discriminatory boundaries and how this will impact international security and human rights in the future.
Algorithms of Oppression by Safiya Umoja Noble Pdf
As seen in Wired and Time A revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for “black girls”—what will you find? “Big Booty” and other sexually explicit terms are likely to come up as top search terms. But, if you type in “white girls,” the results are radically different. The suggested porn sites and un-moderated discussions about “why black women are so sassy” or “why black women are so angry” presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color. Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance—operating as a source for email, a major vehicle for primary and secondary school learning, and beyond—understanding and reversing these disquieting trends and discriminatory practices is of utmost importance. An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century. Safiya Noble discusses search engine bias in an interview with USC Annenberg School for Communication and Journalism
"A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,"--NoveList.
Pattern Discrimination by Clemens Apprich,Wendy Hui Kyong Chun,Florian Cramer,Hito Steyerl Pdf
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?
This book examines the intimate relationship between race and technologies and how digital platforms reabsorb racism as an internal arrangement within its modes of technical and affective architecture. Premising the idea that technologies supplant and mirror the ‘logic’ of racialization as mimetic instruments of social control and violence, the book interrogates the present arrangement of platform capital, and its modes of re-abstraction of race into its fibres and terrains of re-territorialization of the human spheres of social, economic and political life. If capitalism reframed and consolidated racialization through its re-territorialization and primitive accumulation producing continuities from colonization and imperialism, platformization and digital capital redrafts and redistributes its racial logic in new modes of reassembling social and economic life through data, machine learning, algorithms, software designs and in tandem its automaticity. In learning, refining, and accelerating its enterprise through the mimetic violence of producing difference, racism in the digital age calibrates intimately with power, Western rationality and the ubiquity of technologies within the everyday. If the non-hominization of alterity relied on discoveries of science and its conflations with truth and White supremacy, the sustained production and oppression of the ‘inferior other’ co-opted automaticity and technologies, reiterating our fascination with and our understanding of human progress as pegged to machines, as entities working in excess of human cognition and comprehension, connecting and responding to its ambient intelligence despite its material absence. The book underpins the configuration of power and White supremacy through its co-enterprise with technologies seeks to provide an alternative and decolonial approach to technology studies particularly new media and digital technological advancements, leveraging on the notion of the digital age as an era of acceleration of difference, experimentation and the production of alterity through overt and covert modes of surveillance, image recognition software, and algorithms which work in complicity with racial capital.
Since the early days of the internet, there have been questions about how emerging technologies might one day liberate or further harm communities of color that already face structural inequalities of racism. As reliance on computing technologies increases, it is also important to address questions about racial bias in the design of digital platforms, labor inequalities in tech industries, and digital surveillance on Black and Brown communities. This textbook provides a comprehensive introduction to the theory and research on race and digital media. Focusing on the experiences of people of color in the United States, it explores the various ways that racism and white supremacy have shaped aspects of our digital world ‒ from the infrastructures and policies that support technological development, to algorithms and the collection of data, to the interfaces that shape engagement. Yet it also reveals how communities of color have deployed digital media in ways that expand the public sphere, contest the status quo, and give voice to creativity and joy. Race and Digital Media provides an essential resource for students of communication, media, technology, and society. It shows how to make sense of our ever-changing digital media landscape in a way that centers the continued impact of institutionalized racism and the potential for anti-racist futures.
An Intelligence in Our Image by Osonde A. Osoba,William Welser IV Pdf
Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches for combating these problems.
Intelligence, Genes, and Success by Bernie Devlin,Stephen E. Fienberg,Daniel P. Resnick,Kathryn Roeder Pdf
A scientific response to the best-selling The Bell Curve which set off a hailstorm of controversy upon its publication in 1994. Much of the public reaction to the book was polemic and failed to analyse the details of the science and validity of the statistical arguments underlying the books conclusion. Here, at last, social scientists and statisticians reply to The Bell Curve and its conclusions about IQ, genetics and social outcomes.
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.
Dramatic statements about the promise and peril of artificial intelligence for humanity abound, as an industry of experts claims that AI is poised to reshape nearly every sphere of life. Who profits from the idea that the age of AI has arrived? Why do ideas of AI’s transformative potential keep reappearing in social and political discourse, and how are they linked to broader political agendas? Yarden Katz reveals the ideology embedded in the concept of artificial intelligence, contending that it both serves and mimics the logic of white supremacy. He demonstrates that understandings of AI, as a field and a technology, have shifted dramatically over time based on the needs of its funders and the professional class that formed around it. From its origins in the Cold War military-industrial complex through its present-day Silicon Valley proselytizers and eager policy analysts, AI has never been simply a technical project enabled by larger data and better computing. Drawing on intimate familiarity with the field and its practices, Katz instead asks us to see how AI reinforces models of knowledge that assume white male superiority and an imperialist worldview. Only by seeing the connection between artificial intelligence and whiteness can we prioritize alternatives to the conception of AI as an all-encompassing technological force. Bringing together theories of whiteness and race in the humanities and social sciences with a deep understanding of the history and practice of science and computing, Artificial Whiteness is an incisive, urgent critique of the uses of AI as a political tool to uphold social hierarchies.
