WRIT 1155: Writing and Culture (Critical AI)

Artificial Intelligence and Learning Futures explores the implications of artificial intelligence's adoption in higher education and the challenges to building sustainable instead of dystopic schooling. As AI becomes integral to both pedagogy and profitability in today's colleges and universities, a critical discourse on these systems and algorithms is urgently needed to push back against their potential to enable surveillance, control, and oppression. This book examines the development, risks, and opportunities inherent to AI in education and curriculum design, the problematic ideological assumptions of intelligence and technology, and the evidence base and ethical imagination required to responsibly implement these learning technologies in a way that ensures quality and sustainability. Leaders, administrators, and faculty as well as technologists and designers will find these provocative and accessible ideas profoundly applicable to their research, decision-making, and concerns.

NATIONAL BESTSELLER * "The conscience of the AI revolution" (Fortune) explains how we've arrived at an era of AI harms and oppression, and what we can do to avoid its pitfalls. "AI is not coming, it's here. If we answer the beautiful call inside these pages, we can decide who we are going to be and how we're going to use technology in service of what it means to be fully human."--Brené Brown, #1 New York Times bestselling author of Dare to Lead A LOS ANGELES TIMES BEST BOOK OF THE YEAR * Shortlisted for the Inc. Non-Obvious Book Award To most of us, it seems like recent developments in artificial intelligence emerged out of nowhere to pose unprecedented threats to humankind. But to Dr. Joy Buolamwini, who has been at the forefront of AI research, this moment has been a long time in the making. After tinkering with robotics as a high school student in Memphis and then developing mobile apps in Zambia as a Fulbright fellow, Buolamwini followed her lifelong passion for computer science, engineering, and art to MIT in 2015. As a graduate student at the "Future Factory," she did groundbreaking research that exposed widespread racial and gender bias in AI services from tech giants across the world. Unmasking AI goes beyond the headlines about existential risks produced by Big Tech. It is the remarkable story of how Buolamwini uncovered what she calls "the coded gaze"--the evidence of encoded discrimination and exclusion in tech products--and how she galvanized the movement to prevent AI harms by founding the Algorithmic Justice League. Applying an intersectional lens to both the tech industry and the research sector, she shows how racism, sexism, colorism, and ableism can overlap and render broad swaths of humanity "excoded" and therefore vulnerable in a world rapidly adopting AI tools. Computers, she reminds us, are reflections of both the aspirations and the limitations of the people who create them. Encouraging experts and non-experts alike to join this fight, Buolamwini writes, "The rising frontier for civil rights will require algorithmic justice. AI should be for the people and by the people, not just the privileged few."

From two of TIME's 100 Most Influential People in AI, what you need to know about AI--and how to defend yourself against bogus AI claims and products Confused about AI and worried about what it means for your future and the future of the world? You're not alone. AI is everywhere--and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn't, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil--products that don't work, and probably never will. While acknowledging the potential of some AI, such as ChatGPT, AI Snake Oil uncovers rampant misleading claims about the capabilities of AI and describes the serious harms AI is already causing in how it's being built, marketed, and used in areas such as education, medicine, hiring, banking, insurance, and criminal justice. The book explains the crucial differences between types of AI, why organizations are falling for AI snake oil, why AI can't fix social media, why AI isn't an existential risk, and why we should be far more worried about what people will do with AI than about anything AI will do on its own. The book also warns of the dangers of a world where AI continues to be controlled by largely unaccountable big tech companies. By revealing AI's limits and real risks, AI Snake Oil will help you make better decisions about whether and how to use AI at work and home.

Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day. The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years. Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.

This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today's AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI.   What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them?   Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning

An indispensable look at the next frontier of technological advancement and its impact on our world Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do. In this book, Marr sheds light on the most innovative real-world GenAI applications through practical examples, describing how they are moulding industries like retail, healthcare, education, finance, and beyond. You'll enjoy a captivating discussion of innovations in media and entertainment, seismic shifts in advertising, and the future trajectory of GenAI. You will: Navigate the complex landscapes of risks and challenges posed by Generative AI Delve into the revolutionary transformation of the job market in the age of GenAI Understand AI's transformative impact on education, healthcare, and retail Explore the boundless potentials in media, design, banking, coding, and even the legal arena Ideal for professionals, technophiles, and anyone eager to understand the next big thing in technology, Generative AI In Practice will equip readers with insights on how to implement GenAI, how GenAI is different to traditional AI, and a comprehensive list of generative AI tools available today.

Artifi cial Intelligence (AI) has been an exciting fi eld of study and research in educational institutions and research labs across the globe. Technology giants and IT organizations invest heavily on AI technologies and tools with the aim of preciselyautomating a variety of simple as well as complicated business operations acrossindustry verticals. This book covers the latest trends and transitions happening in thefuturistic AI domain. The book also focuses on machine and deep learning (ML/DL)algorithms, which are, undoubtedly, the mainstream implementation technologies ofstate-of-the-art AI systems and services. Also, there are chapters on computer vision(CV) and natural language processing (NLP), the primary use cases and applicationsof AI. The book has well-written chapters for demystifying AI model engineeringmethods. Further on, our esteemed readers can fi nd details on AI model evaluation,optimization, deployment and observability. Finally, the book deals and describesgenerative AI, the latest buzzword in the IT industry. The book presents the recent ground-breaking changes taking place in the aspects of AI model building, hosting, running and maintaining in cloud environments, articulates and accentuates the most recent developments taking place in the domain of Artifi cial Intelligence, covers the noteworthy innovations and disruptions towards Generative Artifi cial Intelligence (Generative AI), explains the breakthrough innovations and disruptions towards Artifi cial General Intelligence (AGI) and delineates an engaging discussion of Natural Language Processing, Neuromorphic Systems and Biometrics.

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.

Chapters 5, 12, and 18 of this work are available under the terms of a CC BY-NC-ND 4.0 International open access licence. These parts of the work are free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Feminist AI: Critical Perspectives on Algorithms, Data and Intelligent Machines is the first volume to bring together leading feminist thinkers from across the disciplines to explore the impact of artificial intelligence (AI) and related data-driven technologies on human society. Recent years have seen both an explosion in AI systems and a corresponding rise in important critical analyses of these technologies. Central to these analyses has been feminist scholarship, which calls upon the AI sector to be accountable for designing and deploying AI in ways that further, rather than undermine, the pursuit of social justice. This book aims to be a touchstone text for AI researchers concerned with the social impact of their systems, as well as theorists, students and educators in the field of gender and technology. It demonstrates the importance of an intersectional understanding of the risks and benefits of AI, approaching feminism as a political project that aims to challenge various interlocking forms of injustice, social inequality and structural relations of power. Feminist AI showcases the vital contributions of feminist scholarship to thinking about AI, data, and intelligent machines as well as laying the groundwork for future feminist scholarship on AI. It brings together scholars from a variety of disciplinary backgrounds, from computer science, software engineering, and medical sciences to political theory, anthropology, and literature. It provides an entry point for scholars of AI, science and technology into the diversity of feminist approaches to AI, and creates a rich dialogue between scholars and practitioners of AI to examine the powerful congruences and generative tensions between different feminist approaches to new and emerging technologies. It features original and essential works specially selected to span multiple generations of practitioners and scholars. These contributors are also attuned to conversations at industry-level around the risks and possibilities that frame the drive to adopt AI. This collection reflects the increasingly blurred divide between the academy, industry and corporate research groups and brings interdisciplinary feminist insights together with postcolonial studies, disability theory, and critical race studies to confront ageism, racism, sexism, ableism, and class-based oppressions in AI.