Atlas of AI is a book that looks far beyond the definitions of artificial intelligence we usually encounter. Kate Crawford examines AI not only as a technology but as a wide ecosystem involving Earth’s resources, human labor, government policies, and data cultures. The book reveals the hidden structures behind AI through striking examples.
Crawford’s main argument is this: Artificial intelligence is neither truly “artificial” nor entirely “intelligent.” AI relies on many tangible components—mineral extraction, energy consumption, data centers, and low‑paid digital labor. This perspective encourages readers to see AI not merely as the applications on our screens but as a system built through real people and real material resources.
One of the author’s most notable approaches is her framing of AI as an atlas. As she explains, “an atlas does not offer a single view; it is made of many maps together.” In this way, AI is a layered structure that cannot be understood from only one angle. Crawford suggests that to understand AI, we must look at mining, labor, data, classification, and state policies together.
Another striking point is Crawford’s discussion of data. She writes, “Datasets do not only contain information; they also reflect specific worldviews.” This highlights how data collection and classification are influenced by cultural and political forces. Readers are encouraged to question whether data can ever be fully “neutral.”
Crawford also questions the idea that AI is a neutral tool. She argues: “If we want to understand a system, we must first ask who designed it and for what purpose.” This encourages readers to adopt a more critical and informed perspective on technology.
Main Chapters and Key Ideas
Chapter 1 – The Earth
This chapter shows how AI technologies rely on a vast physical infrastructure. Lithium mining, energy consumption, and environmental impact form the core focus of this section.
Crawford describes this chapter in the book as follows: “we begin in the lithium mines of Nevada, one of the many sites of mineral extraction needed to power contemporary computation. Mining is where we see the extractive politics of AI at their most literal. The tech sector’s demand for rare earth minerals, oil, and coal is vast, but the true costs of this extraction is never borne by the industry itself. On the software side, building models for natural language processing and computer vision is enormously energy hungry, and the competition to produce faster and more efficient models has driven computationally greedy methods that expand AI’s carbon footprint. From the last trees in Malaysia that were harvested to produce latex for the first transatlantic undersea cables to the giant artificial lake of toxic residues in Inner Mongolia, we trace the environmental and human birthplaces of planetary computation networks and see how they continue to terraform the planet.”
Chapter 2 – Labor
This chapter explains the invisible labor behind systems that seem “automated.” It discusses how data annotators, warehouse employees, and other workers support the development of AI.
Crawford explains this chapter in the book as follows: “Chapter 2 shows how artificial intelligence is made of human labor. We look at the digital pieceworkers paid pennies on the dollar clicking on microtasks so that data systems can seem more intelligent than they are. Our journey will take us inside the Amazon warehouses where employees must keep in time with the algorithmic cadences of a vast logistical empire, and we will visit the Chicago meat laborers on the disassembly lines where animal carcasses are vivisected and prepared for consumption. And we’ll hear from the workers who are protesting against the way that AI systems are increasing surveillance and control for their bosses. Labor is also a story about time. Coordinating the actions of humans with the repetitive motions of robots and line machinery has always involved a controlling of bodies in space and time. From the invention of the stopwatch to Google’s TrueTime, the process of time coordination is at the heart of workplace management. AI technologies both require and create the conditions for ever more granular and precise mechanisms of temporal management. Coordinating time demands increasingly detailed information about what people are doing and how and when they do it…..”
Chapter 3 – Data
This chapter examines how massive datasets are created and the ethical challenges involved. Crawford explains why data is not neutral and how personal information becomes a resource for AI systems.
Crawford explains this chapter in the book as follows: “Chapter 3 focuses on the role of data. All publicly accessible digital material—including data that is personal or potentially damaging—is open to being harvested for training datasets that are used to produce AI models. There are gigantic datasets full of people’s selfies, of hand gestures, of people driving cars, of babies crying, of newsgroup conversations from the 1990s, all to improve algorithms that perform such functions as facial recognition, language prediction, and objectmdetection. When these collections of data are no longer seen as people’s personal material but merely as infrastructure, the specific meaning or context of an image or a video is assumed to be irrelevant. Beyond the serious issues of privacy and ongoing surveillance capitalism, the current practices of working with data in AI raise profound ethical, methodological, and epistemological concerns….”
Chapter 4 – Classification
This chapter analyzes how AI categorizes people and how these systems may reproduce social biases and deepen inequalities.
Crawford explains this section of the book as follows: “In chapter 4, we look at the practices of classification in artificial intelligence systems, what sociologist Karin Knorr Cetina calls the “epistemic machinery.” We see how contemporary systems use labels to predict human identity, commonly using binary gender, essentialized racial categories, and problematic assessments of character and credit worthiness. A sign will stand in for a system, a proxy will stand for the real, and a toy model will be asked to substitute for the infinite complexity of human subjectivity. By looking at how classifications are made, we see how technical schemas enforce hierarchies and magnify inequity. Machine learning presents us with a regime of normative reasoning that, when in the ascendant, takes shape as a powerful governing rationality. From here, we travel to the hill towns of Papua New Guinea to explore the history of affect recognition, the idea that facial expressions hold the key to revealing a person’s inner emotional state…..”
Chapter 5 – Affect
This chapter discusses emotion‑recognition technologies, their scientific debates, and their rapid adoption in fields such as hiring, education, and policing.
Crawford explains this chapter in the book as follows: “Chapter 5 considers the claim of the psychologist Paul Ekman that there are a small set of universal emotional states which can be read directly from the face. Tech companies are now deploying this idea in affect recognition systems, as part of an industry predicted to be worth more than seventeen billion dollars. But there is considerable scientific controversy around emotion detection, which is at best incomplete and at worst misleading. Despite the unstable premise, these tools are being rapidly implemented into hiring, education, and policing systems…..”
Chapter 6 – The State
This chapter explores how AI is used in governance, security, and public services. It discusses military influences, surveillance practices, and how these systems shape relationships between states and citizens.
Crawford explains this chapter in the book as follows: “In chapter 6 we look at the ways in which AI systems are used as a tool of state power. The military past and present of artificial intelligence have shaped the practices of surveillance, data extraction, and risk assessment we see today. The deep interconnections between the tech sector and the military are now being reined in to fit a strong nationalist agenda. Meanwhile, extralegal tools used by the intelligence community have now dispersed, moving from the military world into the commercial technology sector, to be used in classrooms, police stations, workplaces, and unemployment offices. The military logics that have shaped AI systems are now part of the workings of municipal government, and they are further skewing the relation between states and subjects…..”
Why AItoHope Recommends This Book
AItoHope recommends this book because it helps young readers explore different perspectives on technology.
- It offers a broader perspective on AI: It does more than explain how algorithms work; it also explores AI’s social, environmental, and economic impacts. This helps readers understand AI not just as a technical tool, but as a multi-layered system that shapes communities, resources, and human experiences.
- It encourages critical thinking:
It encourages young people to evaluate artificial intelligence not only as a technical system but through its social, ethical, environmental, and political dimensions. This helps readers analyze the technologies shaping both the present and the future from multiple perspectives, question how and why these systems are designed, and think creatively about possible solutions. - It explains social impacts clearly: It illustrates how AI is used in areas such as education, justice, public services, and the workplace through concrete, real-world examples. This helps readers understand the consequences of these systems for individuals and society, allowing them to examine issues like inequality, transparency, accountability, and ethical risks in a meaningful context.
- It strengthens AI literacy: It helps young people engage with artificial intelligence not just as users, but as informed individuals who understand how these systems work, what assumptions they rely on, and how they shape society. It encourages a critical, reflective, and conscious interaction with technology. This empowers readers to make better decisions in a rapidly evolving digital world, evaluate risks and opportunities more effectively, and build a solid foundation for understanding the technologies that will shape the future.
AItoHope’s mission is to provide young people with a reliable and inspiring space where they can explore diverse ideas and research about artificial intelligence.
About the Author
Kate Crawford is a leading scholar of artificial intelligence and its material impacts. She is a Research Professor at the University of Southern California, a Senior Principal Researcher at Microsoft Research New York, and the inaugural visiting chair of AI and Justice at the École Normale Supérieure in Paris.
Her latest book, Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence (Yale University Press) was named a best book of the year by The Financial Times, won three international prizes, and has been translated into twelve languages.
Crawford leads the interdisciplinary lab Knowing Machines Project, a transatlantic research collaboration of scientists, artists, and legal scholars that investigates how AI systems are trained. She has also co-founded multiple research institutes, including FATE (Fairness, Accountability, Transparency and Ethics in AI, based at MSR) and the AI Now Institute (NYU). Her research has appeared in many venues including Nature, AI & Society, and Science, Technology & Human Values. She has advised policymakers in the United Nations, the White House, the European Parliament, and is currently on the AI Council of President Sanchez of Spain. TIME100 named Crawford as one of the world’s most influential people in AI.
In addition to her scholarly work, Crawford is an award-winning artist. Her latest project Calculating Empires: A Genealogy of Technology and Power Since 1500* *(with Vladan Joler) won the Silver Lion at the Venice Architecture Biennale 2025, the Grand Prize of the European Commission for art and technology, and the Boghossian Art Prize 2024. It premiered at Fondazione Prada in Milan in 2023, and has been shown around the world including the Mori Museum in Tokyo, the Jeu De Paume in Paris, the Design Museum in Barcelona, the Rijksmuseum Twenthe, and the KW Institute in Berlin. Crawford and Joler’s prior project, Anatomy of an AI System, has been shown in more than 100 exhibitions worldwide and is in major museum collections.
Note: Author information has been taken from Kate Crawford’s official website (https://katecrawford.net).
