Introduction
Summary of the book Atlas of AI by Kate Crawford. Let us start with a brief introduction of the book. Imagine you are holding a mysterious map that guides you through secret tunnels and hidden chambers inside the world’s most celebrated technology—Artificial Intelligence. At first glance, the sleek phones, voice assistants, and recommendation algorithms seem magical, as if conjured from thin air. But as this Atlas of AI reveals, beneath the surface are messy realities and tangled roots. There are deserts yielding critical minerals, factories powered by human sweat, sprawling data sets harvested from our personal lives, and flawed classifications that shape who counts and who doesn’t. This journey uncovers not just the inner workings of AI, but also the power imbalances, environmental costs, and ethical dilemmas woven into its design. By following these chapters, you will learn that AI’s remarkable achievements come with overlooked costs, prompting us to question, who truly benefits, and who might be left behind in the rush toward digital supremacy?
Chapter 1: Uncovering a Strange Tale of a Horse, Hidden Biases, and Illusions in Defining Machine Intelligence.
Picture yourself in a crowded street somewhere in Europe at the turn of the 20th century. People gather excitedly around a well-known performer, but this is no ordinary entertainer. It’s a horse named Clever Hans, and rumor has it that this animal can do math, tell time, and answer questions by tapping its hoof. Onlookers marvel as Hans appears to solve arithmetic problems, identify dates, and even distinguish melodies. It feels as if the boundaries of intelligence have suddenly expanded, causing everyone to question what it truly means to be smart. News spreads fast, and soon Clever Hans is famous. Reporters, scientists, and ordinary people flock to witness the horse’s talents. As the applause and astonishment swell, few dare to ask: Is Hans really thinking like a human, or could something else be guiding those clever taps?
When a careful psychologist named Oskar Pfungst decided to investigate Hans’s abilities, he uncovered a much subtler truth. Hans was not actually reasoning as humans do. Instead, the horse was cleverly picking up on tiny signals—changes in posture, the tilt of a head, the tension in a face—from the people asking him questions. These subtle cues, given unconsciously by the questioners, guided Hans to stop tapping his hoof at just the right moment. The public’s amazement had been built on a misunderstanding: the horse wasn’t a genius; rather, the human observers were giving away answers without even realizing it. This became known as the Clever Hans Effect, a powerful lesson that shows how human expectations, assumptions, and biases can shape what we believe to be intelligence—even in machines and animals that simply reflect back what we feed them.
Fast-forward to today’s world of artificial intelligence, and the lesson of Clever Hans remains deeply relevant. Many people assume that advanced AI systems truly think or understand just like we do. But, much like Hans responding to subtle signals, modern AI relies on patterns in large sets of data, guided by human instructions and hidden biases. These systems perform impressive feats: recognizing faces, translating languages, recommending products, or predicting what we might like to watch. However, these are not signs of a machine’s inner wisdom or human-like comprehension. Instead, they highlight that AI systems are shaped and constrained by the data we choose, the categories we define, and the goals we pursue. Without careful examination, we risk seeing intelligence where none exists, just as those early crowds believed in Hans’s supposed cleverness.
Understanding this deception is crucial as we navigate a world increasingly dominated by algorithms and automated processes. AI’s sophisticated outputs may fool us into thinking these systems grasp cultural nuances, moral values, or emotional contexts. Yet what we call machine learning is more about pattern matching than deep reasoning. The horse named Hans teaches us that intelligence is not just about producing correct answers. It is about the capacity to reason, reflect, understand, and adapt in ways machines cannot truly achieve. By remembering the Clever Hans story, we can become more cautious and critical observers. We can begin asking harder questions of our AI systems: Whose assumptions are shaping these models? How might our hidden beliefs influence the outcomes they produce? It is through such questioning that we start to uncover the deeper truth behind the illusions of machine intelligence.
Chapter 2: Beneath the Shimmering Desert: The Buried Treasures of Lithium and the Real Price of AI’s Core Materials.
Imagine driving through a quiet, sun-scorched desert town called Silver Peak, hidden in a corner of Nevada. At first glance, it seems like an ordinary, dusty settlement. Yet beneath its surface lies something extraordinary—one of the United States’ only domestic sources of lithium. This soft, silvery metal, crucial for modern rechargeable batteries, fuels the devices we use every day. From smartphones and laptops to electric cars and massive data centers, lithium underpins the electronics that power AI’s rise. As our world grows hungrier for faster computing and smarter machines, demand for such materials skyrockets. Extraction sites like Silver Peak become the silent backbones of a technological revolution. But the gleam of innovation often blinds us to the complicated reality: obtaining these resources exacts environmental tolls, disrupts local communities, and leaves landscapes scarred beneath the promise of high-tech progress.
To build powerful AI, we need not only clever code but also the raw materials that enable computer chips, batteries, and advanced devices. For every sleek smartphone, there’s a mine laboring away in the background. It might be hidden deep in the mountains of Inner Mongolia, where rare earth elements are pulled from the earth at enormous environmental cost. Or it may be on tin-rich Indonesian islands, where reckless mining operations degrade fragile coastal ecosystems. The hidden story of AI’s material roots is one of global supply chains, each link representing an extraction site, a refinery, a shipping route, and eventually a polished product on a store shelf. While innovation races ahead, few stop to consider what has been dug up, processed, and sacrificed. Layer by layer, the AI industry rests upon a quiet yet staggering effort of resource exploitation.
History repeats itself when it comes to resource extraction. In the 19th century, booming cities were built from the wealth of gold and silver mines carved into lands seized by force. Whole populations were displaced, and fragile environments were torn apart. Today’s AI-driven era might seem distant from that time, but the echoes remain. The lithium pools of Nevada, the rare earth sites in East Asia, and the metal-rich soils of African nations all feed into a high-tech supply chain. And just as in centuries past, the environmental damage and human suffering are often hidden from end consumers. We see only the final gadget in our hands, not the drained lakes, polluted air, or displaced communities that made it possible. This disconnect allows large tech companies to prosper without facing the messy truths beneath their products’ polished surfaces.
As we marvel at the potential of AI—faster computers, smarter assistants, and increasingly capable robots—we must acknowledge the cost of the hardware that makes it all possible. The myth of clean tech suggests these innovations are purely modern miracles, gently lifting society toward a greener future. Yet the reality is more complicated. Mining lithium or rare earth elements still involves harsh chemicals, water-intensive processes, and damage to ecosystems. Local communities often struggle with contaminated water supplies, ruined farmland, or health risks. While giant technology firms and investors celebrate the dawn of a new era, the people living near extraction zones bear the heaviest burden. Recognizing these truths does not mean giving up on progress, but it does mean demanding more responsible and ethically grounded solutions. The shimmering desert pools remind us that true advancement must also respect the planet and its people.
Chapter 3: Shadows of Old Gold Rushes: How Historical Exploitation Echoes in Today’s AI Infrastructure.
Picture the frantic excitement of past centuries when fortune-seekers rushed to goldfields, tearing into landscapes with pickaxes and shovels. Cities like San Francisco surged to greatness on the promise of newly extracted riches. But these riches were not freely given; they were often seized through conflict, forced labor, and displacement. While we may think we live in a more enlightened era, the same patterns of exploitation linger in the modern world of AI. Instead of gold or silver, we now scramble for lithium, cobalt, and other precious materials that drive computing power. The difference is that these extractions are less visible to most of us, concealed behind complex global networks and branded devices that promise smart futures. The old gold rush may seem a distant memory, yet its logic persists, shaping how we source the elemental building blocks of today’s technology.
Throughout history, the pursuit of resources has often involved overlooking moral costs. Entire communities were uprooted, indigenous lands seized, and ecosystems shattered. Now, as we push the boundaries of AI, the extraction imperative continues, just more quietly. Instead of stagecoaches and pickaxes, we have cargo ships and industrial-scale mining operations. Rather than adventurous prospectors, we have corporate supply chain managers carefully tracking commodity prices. The underlying pattern remains: We crave raw materials to support our ambitions, yet rarely do we reflect on whose shoulders those ambitions stand upon. The dusty mining towns of yesterday have become today’s extraction hotspots, quietly fueling data centers, robotic factories, and voice assistants. By recognizing these continuities, we might better understand that the moral questions of today’s AI industry are not new; they stem from a long legacy of turning natural wealth into private gain.
The story of modern technology, including AI, is partly a story of keeping certain truths at arm’s length. Historically, the spoils of resource extraction enriched distant investors and industrialists, while laborers and local residents endured the negative side effects. The boomtowns and scarred landscapes fade from public memory, replaced by polished historical narratives that overlook the human toll. Similarly, today’s digital empires—global tech giants—portray themselves as visionary innovators. Their shining headquarters and massive research budgets stand far from the mines that supply the metal for their servers, the assembly lines that build their devices, and the environmental damage left behind. This distance makes it easier to ignore the suffering or environmental wreckage that quietly underpins technological growth. In essence, modern AI infrastructure inherits not just materials but also the moral dilemmas and entrenched inequalities that have haunted centuries of resource extraction.
If we care about the future of AI and the type of society we are constructing, we must explore the shadows cast by our historic tendencies. Rewriting the narrative involves questioning the idea that progress inevitably justifies exploitation. It involves asking: Are the promises of AI worth the cost if they rely on hidden harm? While we enjoy the convenience of powerful digital tools, we must acknowledge the people and places that pay the price. This is not an invitation to reject AI entirely, but rather to demand more equitable and sustainable foundations. By facing these historical echoes head-on, we can decide to move forward differently. We can push for fair labor practices, environmentally responsible extraction methods, and policies that respect indigenous rights and local communities. Only by understanding the past can we shape a more just technological future.
Chapter 4: Working in the Digital Mines: Unseen Human Toil Powering the AI Industry’s Global Rise.
When we think of mining, we often picture rugged individuals scraping gold nuggets out of a riverbed or extracting coal deep underground. But in the age of AI, a new type of mining thrives quietly: digital mining, where human labor is hidden behind computer screens. Picture large rooms of low-paid workers clicking through thousands of images, tagging objects, labeling facial expressions, and identifying patterns. These human labelers provide the essential training data that AI systems need to learn. Without their tireless efforts, the algorithms that power facial recognition, language translation, and product recommendations would struggle to function. Yet these workers often remain invisible—unseen by users and overshadowed by the glamour of cutting-edge innovation. Their labor is essential, yet poorly compensated and frequently overlooked, resembling the quiet cogs of a colossal machine that benefits those sitting at the top.
Far from the spotlight of prestigious tech campuses, these human workers operate under challenging conditions. Some might spend countless hours looking at disturbing images to teach AI how to recognize harmful content. Others click tirelessly to identify every object in a photograph, training computer vision systems. The pay is often minimal, the job security weak, and the psychological impact can be severe. Individuals performing these tasks may come from economically vulnerable backgrounds, sometimes living in regions where employment options are scarce. Tech companies seeking cheap labor rely on this global army of annotators but seldom acknowledge their contributions publicly. This hidden human input shows that AI is not as autonomous as it may seem. Instead, it is carefully shaped and guided by countless hours of human effort—an uncomfortable reality rarely discussed in glowing product announcements.
Consider the hardware side: workers assemble smartphones, laptops, and data center components under tight deadlines in large factories. They handle complex circuits, delicate screens, and miniature components that will soon carry our digital world. Reports of poor working conditions, long hours, and limited labor rights are common. While AI conjures images of smooth-running robots and automated precision, the truth is that human hands still do much of the intricate work. From the mines that extract raw materials to the factories that turn them into advanced electronics, human labor is central at every stage. Yet, because this labor is often outsourced and geographically distant from end consumers, it remains easy for the public to forget. In this sense, the AI industry’s human toil resembles the foundation of a tall building—essential but buried out of sight.
If we value ethical technology, we must acknowledge the conditions of these workers. We should ask who benefits from AI and who shoulders its burdens. By demanding fair wages, safer working environments, and stronger labor protections, we can ensure that the people who make AI possible are treated with dignity. If we ignore this, we risk repeating the same patterns of exploitation that have defined previous industrial revolutions. AI’s promise is not just about achieving incredible computational feats, but also about reimagining how technology can serve humanity. Recognizing the human toll brings us closer to building systems that do not merely benefit a wealthy few, but also respect and uplift the many individuals whose labor, skills, and efforts sustain the digital ecosystem. Ultimately, making these invisible workers visible is a crucial step toward more just and humane innovation.
Chapter 5: The Endless Data Hunt: Scraping, Storing, and Shaping Our Lives into AI Training Fuel.
Imagine that every click you make, every post you share, and every image you upload becomes a tiny grain in a gigantic data quarry. AI developers dig deep into this repository, feeding their models with the scraps of our daily digital lives. They collect your social media posts, online reviews, search histories, and even facial expressions captured in videos. All these fragments of personal information serve as raw material to train intelligent systems. Yet how often do we pause to wonder: Who gave permission for this massive data harvest? Who decided that our online behavior should become fuel for machine learning? The truth is, much of this data is acquired without explicit consent. It slips through legal gaps, fine-print agreements, or public data sets scraped from the web. Users rarely realize how their everyday actions are turned into fodder for AI algorithms.
The roots of this data rush run deep. Early attempts at speech recognition and facial recognition research required enormous amounts of information. Over time, as the internet expanded, researchers found they no longer needed to obtain data with clear consent. Instead, they could scrape the web, assembling enormous datasets of images, text, and videos that represent humanity in all its complexity. But this complexity includes biases, stereotypes, and harmful content. When these messy datasets train AI models, the models absorb distortions and negative assumptions. Without careful curation, the output becomes flawed, sometimes even dangerous. Consider a facial recognition system built on biased data—it could misidentify people of certain racial or ethnic backgrounds or reinforce unfair judgments. The industry’s hunger for data can lead to reckless collection practices and superficial labeling, ensuring that unethical or misguided AI tools flourish unchecked.
As AI becomes woven into everyday applications—predicting what we watch, guiding hiring decisions, influencing medical diagnoses—data quality matters more than ever. Yet currently, many machine learning pipelines treat data as a cheap, abundant commodity. Workers label images at lightning speed, often with little context or care, turning complex human realities into neat categories. Privacy regulations lag behind technological advancements, and companies press forward, believing that more data always leads to better AI. This relentless approach mirrors old frontiersmen chasing gold: quantity matters more than thoughtfulness. But as AI’s influence grows, it’s no longer just about profit margins or convenience. It’s about shaping how we understand ourselves and each other. If the raw material is flawed, incomplete, or extracted without respect for human dignity, the resulting systems can harm, discriminate, or invade personal boundaries.
The time has come to rethink our relationship with data. Rather than treating human-generated information as a limitless resource, we might consider frameworks that prioritize informed consent, transparency, and accountability. What if data collection were grounded in respect for human rights, cultural differences, and individual privacy? Without such principles, AI’s rapid expansion risks becoming an uncontrollable force that exploits people’s personal expressions and patterns. As public awareness grows, we must push for regulations, ethical guidelines, and industry standards that value the quality of data over sheer quantity. For example, stronger data protection laws, more robust ethical review boards, and user-friendly consent mechanisms can help ensure that data is not just taken but responsibly sourced. Only by addressing these issues at their root—our uncritical appetite for data—can we build AI systems that truly serve society without trampling over its values.
Chapter 6: Understanding AI’s Taxonomies: How Complex Classification Systems Reinforce Prejudices and Misdirected Judgments.
Classification systems shape how we understand the world. They allow us to sort things into categories, from plants and animals to musical genres and personality types. But when these systems are embedded in AI, they can take on a troubling power, labeling people’s faces, voices, and written words according to rigid, pre-defined groupings. Consider a large image dataset that attempts to organize millions of pictures by concepts it inherits from old dictionaries or wordlists. Some categories might seem harmless: fruit, building, or car. But then there are categories for people—terms that reflect outdated, biased, or offensive viewpoints. Suddenly, a tool designed to help machines see reveals its creators’ unconscious assumptions, showing how seemingly neutral technical structures can contain deep societal prejudices. The act of naming and grouping isn’t just technical; it’s political. It shapes how AI understands humans and influences how humans are treated.
Historically, attempts to classify human intelligence or worth have had devastating consequences. Think of 19th-century scientists who measured skulls to prove racial hierarchies. Their methods lent fake scientific credibility to racist ideologies. Today’s AI classification systems may not measure skulls, but they still categorize people according to flawed mentalities. Databases used to train facial recognition might lump people of color into crude clusters or assign demeaning labels to women. These choices aren’t random errors; they stem from the underlying data and the goals set by developers. When AI models learn from tainted sources, they can produce harmful outputs—misidentifying people, reinforcing stereotypes, or marginalizing vulnerable communities. In this way, the logic of classification can become a dangerous tool, not only reflecting society’s ills but also making them more efficient and harder to detect.
Attempts to fix biased systems by simply expanding training data or including more diverse examples can feel like treating symptoms rather than the root cause. True fairness in AI demands asking why we are classifying people in the first place. Are we oversimplifying human identity into rigid boxes that don’t capture complexity? Race, gender, morality, and personality are not like objects that can be neatly labeled. They are dynamic, context-dependent concepts with rich cultural meanings. Simply adding more categories or including more images doesn’t solve the core issue: AI systems are not neutral observers. They inherit and amplify the worldview of their architects and the content they absorb. As a result, we must consider whether certain classifications belong in AI at all. Perhaps some tasks are better left un-automated, requiring human empathy and understanding rather than machine-based sorting.
If we want AI that supports human well-being rather than distorting it, we must reevaluate our approach to classification. One step is to involve a broader range of voices in the creation of these systems—social scientists, ethicists, community representatives, and users from diverse backgrounds. We can also demand that companies and researchers be transparent about how they label their training data and what categories they choose to include. This scrutiny can uncover hidden assumptions and allow for more informed discussions about what AI should and should not do. Such measures go beyond technical tweaks. They require a shift in mindset, accepting that classification is not a neutral technical step but a moral and political act. By recognizing the stakes of labeling people and their behaviors, we can move towards AI that respects human dignity and complexity.
Chapter 7: Polluted Currents of Progress: Environmental Costs, Energy Hunger, and AI’s Unseen Climate Footprint.
As AI systems become more capable, they devour enormous amounts of computational power. Data centers—vast warehouses of servers—run day and night to process information, train models, and handle trillions of user requests. These centers need steady electricity, cooling systems, and backup infrastructures. In many cases, that power still comes from burning fossil fuels, linking digital innovation directly to environmental harm. It’s like a hidden smokestack attached to every digital action. We rarely picture AI as a polluter, but every time we ask a virtual assistant a question or rely on an automated recommendation, we’re tapping into a global network of energy-hungry machines. The promise of AI often comes with a heavy environmental price tag, and if we don’t address it, we risk turning technological progress into a driver of climate instability and resource depletion.
Consider the difference between a small machine learning model that runs on your laptop and a massive model trained on huge datasets using hundreds or thousands of powerful processors. The latter can consume as much energy as several households, just to adjust its parameters for slightly better accuracy. Multiply that by the countless experiments, refinements, and retraining cycles performed daily in research labs and tech companies, and the total energy footprint soars. While some data centers run on renewable energy, many still rely on traditional power grids. This means that innovation in AI can indirectly contribute to greenhouse gas emissions, water scarcity (from cooling systems), and the destruction of ecosystems needed to build and maintain infrastructure. As the industry chases ever-larger, more complex models, we must ask: At what environmental cost do we pursue incremental accuracy gains?
The narrative of a green, sustainable tech future can be misleading if we ignore these realities. While some companies promise carbon-neutral operations, the overall system of mass extraction, production, and disposal remains ecologically damaging. Mining rare earth metals for server components, building new data centers in sensitive areas, and discarding old electronics all contribute to a cycle of waste and pollution. Our growing dependence on AI can accelerate this cycle if not carefully managed. Instead of treating efficiency and environmental responsibility as secondary concerns, we must bring them to the forefront. Engineers can design more energy-efficient chips, governments can regulate data center emissions, and investors can reward companies that minimize their environmental footprint. By doing so, we ensure that AI’s powerful capabilities do not come at the planet’s expense.
Beyond the energy itself, we must also consider the human communities affected by these environmental costs. For example, data centers placed in resource-strained regions might compete with local populations for clean water and stable electrical supply. The impact of climate change—worsening droughts, floods, and extreme weather—can further complicate these tensions. The environmental burdens of AI may not appear on glossy product marketing, but they show up in rising energy bills, damaged ecosystems, and struggling communities. Recognizing this invisible link between AI and environmental harm is the first step towards change. With public pressure, responsible policy-making, and ethical corporate leadership, we can reduce AI’s ecological footprint. Instead of rushing blindly into the future, we can pause and ensure that technological progress aligns with the health of our planet and the well-being of present and future generations.
Chapter 8: Reimagining the Road Ahead: Ethical Imperatives, Accountable Design, and a More Compassionate AI Future.
We stand at a crossroads where AI’s extraordinary abilities tempt us with convenience, efficiency, and profits. But as we have seen, this technology’s hidden costs—environmental damage, exploitative labor practices, biased classifications, and relentless data harvesting—are not problems to dismiss. They must be confronted if we want AI to genuinely serve humanity. The way forward requires rethinking the principles guiding AI development. Instead of viewing technology as a neutral tool, we can acknowledge its embedded values and power structures. By doing so, we can begin to design systems that aim to benefit all people, not just privileged groups. This means re-examining every step: how we source materials, how we treat workers, how we respect privacy, and how we classify and understand others. A more compassionate AI future lies in challenging old assumptions and demanding accountability from those who shape these tools.
Ethical AI is not a simple checklist, but an ongoing effort that requires dialogue and collaboration. Policymakers must craft laws that guard against abuses, protect personal data, and limit environmental harm. Engineers and data scientists must learn to listen to ethicists, sociologists, and historians to understand the broader implications of their code. Company leaders must prioritize long-term societal benefit over short-term gains, knowing that public trust is fragile. Meanwhile, ordinary citizens have the right to know how these technologies work, to contest their unfair outcomes, and to influence their development. In this complex ecosystem, everyone has a role. Building AI with kindness and responsibility involves looking beyond shiny marketing messages and empty promises. It means acknowledging that genuine progress is measured not only by computational power but also by fairness, inclusivity, and sustainability.
Imagine a world where technology advances hand in hand with principles of justice and care. In this scenario, the once invisible workers behind data labeling are paid fairly and treated with respect. Resource extraction is minimized, recycled materials are prioritized, and manufacturing processes are cleaned up. Data is collected transparently, with meaningful consent, and the individuals providing that data have a say in how it is used. Classification systems are carefully audited to avoid spreading harmful stereotypes. AI models are optimized not just for performance but for ethical standards, environmental responsibility, and social good. This might sound idealistic, but it’s not impossible. It’s about setting higher expectations and being willing to enforce them. With collective determination, we can steer AI’s development toward solutions that uplift human dignity and preserve our shared home.
The road ahead is uncertain, but it need not be dark. We’ve seen that AI can be incredibly powerful—able to assist medical diagnoses, improve transportation safety, or help people communicate across languages. These accomplishments need not vanish. Instead, they can be grounded in ethical frameworks that ensure the technology’s benefits reach everyone fairly and sustainably. The key is to accept that AI is neither magically intelligent nor morally neutral. It’s a system shaped by human choices and priorities. By making different choices—respecting labor, valuing privacy, fighting biases, and protecting the environment—we can guide AI toward a brighter, more humane horizon. The journey may be challenging, but it’s one worth undertaking. After all, technology should reflect the best of humanity, not our darkest impulses. With courage, reflection, and commitment, we can create an AI future that truly supports life on Earth.
All about the Book
Explore the profound implications of artificial intelligence in Kate Crawford’s ‘Atlas of AI’. Uncover the societal, ethical, and environmental impacts of AI technologies, enriching your understanding of our future in the AI-driven world.
Kate Crawford is a leading scholar and researcher in AI ethics, renowned for her work on the social implications of artificial intelligence and the intersections of technology and society.
Data Scientists, AI Ethicists, Policy Makers, Environmental Scientists, Journalists
Tech Philosophy, Science Fiction, Social Advocacy, Data Visualization, Ethical Debates
Ethical implications of AI, Environmental impact of technology, Data privacy concerns, Societal inequality exacerbated by tech
AI is not just a tool; it shapes the very fabric of our society, and we must question its consequences.
Elon Musk, Michelle Obama, Timnit Gebru
Best Non-Fiction Book of the Year, AI Ethics Award, Environmental Literature Award
1. How does AI influence our daily decision-making processes? #2. In what ways can AI biases affect society? #3. What role does data play in shaping AI systems? #4. How is AI connected to environmental sustainability issues? #5. What ethical dilemmas arise from AI technologies? #6. How do corporations influence AI development and regulation? #7. What are the social implications of AI surveillance? #8. How can AI reinforce existing power dynamics? #9. In what ways is labor impacted by AI advancements? #10. How does AI contribute to the spread of misinformation? #11. What responsibilities do developers have in AI deployment? #12. How can we ensure transparency in AI algorithms? #13. What is the relationship between AI and human rights? #14. How does AI affect marginalized communities differently? #15. What can be done to ensure equitable AI systems? #16. How are cultural values reflected in AI technologies? #17. What strategies promote accountability in AI practices? #18. How does AI impact our understanding of knowledge? #19. What are the challenges of regulating AI technologies? #20. How can we foster critical thinking about AI?
Atlas of AI, Kate Crawford, artificial intelligence, AI ethics, impact of AI on society, data bias, technology and society, AI governance, machine learning, AI and human rights, global data mining, critical AI studies
https://www.amazon.com/Atlas-AI-Kate-Crawford/dp/0300239189
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