Book SummaryAtlas of AI, by Kate Crawford

1-Page Summary1-Page Book Summary of Atlas of AI

The impact that AI systems have on societal frameworks and the sustainability of the environment, including their physical makeup and governance.

In this section, Crawford explores the concept that artificial intelligence is comprised of an extensive system involving the procurement of materials, workforce involvement, and the distribution of resources, all of which have a profound effect on both the environment and social frameworks. Artificial intelligence has a profound connection with actual global conditions that greatly affect the planet and shift the power dynamics in favor of specific nations and business entities.

The foundational elements of computational infrastructure are deeply rooted in significant environmental effects and the acquisition of resources.

Crawford advocates for a deeper investigation into AI through an examination of its underlying physical frameworks. She emphasizes the significant ecological consequences associated with developing and maintaining these digital infrastructures, dispelling the myth that this technology is environmentally benign.

Data centers and cloud computing contribute significantly to carbon emissions due to their substantial energy requirements.

Crawford emphasizes the substantial power consumption of vast artificial intelligence networks, primarily sustained by finite resources. The carbon footprint of the foundational systems that drive cloud computing is equivalent to the total emissions generated by the aviation sector. Crawford cites research indicating that by 2040, the tech sector could account for up to 14% of global greenhouse gas emissions, with the power demands of data centers potentially increasing fifteenfold by 2030.

Although certain prominent technology firms assert their carbon neutrality, Crawford reveals how they exacerbate the problem by supplying artificial intelligence technology, expertise, and essential assistance to entities within the oil and gas industry. She underscores the critical demand for computational resources necessary to advance technologies in machine learning, highlighting a tendency towards intensive computational tasks to improve functionality, which significantly impacts the ecological system.

Other Perspectives

• The shift towards hyperscale data centers is resulting in more energy-efficient operations compared to smaller, less efficient data centers.
• The energy used by AI networks often comes from a mix of sources, including renewable energy, which can mitigate the carbon footprint associated with their operation.
• The carbon footprint of cloud computing is subject to rapid improvements in energy efficiency, whereas gains in aviation fuel efficiency are slower, meaning the equivalence may not hold over time.
• The 14% figure assumes a static scenario where other sectors do not also increase their emissions, potentially skewing the tech sector's relative contribution to global emissions.
• The prediction might not take into account the possibility of regulatory changes that could enforce stricter energy consumption standards for data centers.
• Technology firms may argue that their partnerships with the oil and gas industry are aimed at improving efficiency and reducing emissions within those sectors through the application of advanced technologies.
• There is a growing trend towards developing more energy-efficient machine learning models, which suggests that the reliance on computational resources could decrease as these models become more prevalent.
• The ecological impact is also being addressed by the adoption of edge computing, which can process data closer to the source and reduce the energy required for data transmission and centralized processing.

AI technologies depend on acquiring and using a variety of natural resources, including scarce minerals from the earth as well as fossil fuels, to function effectively.

Crawford explores the pivotal role that minerals play in building systems for artificial intelligence. The author highlights the process of obtaining scarce minerals such as lithium, which are essential for the production of batteries used in electric cars and numerous electronic devices. She leads us through an examination of the lithium mines in Nevada, depicting a pattern of resource exploitation that echoes the transformative gold rush which catalyzed San Francisco's growth.

Crawford delves into the complex worldwide systems that procure minerals like tin, highlighting how the industry offloads the true costs onto the local community and the environment. The author argues that the electric car, touted as a solution, is far from perfect due to the environmental impacts of its battery production. She argues that the way in which intelligent systems manage information gives rise to substantial ethical and methodological concerns, along with questions related to the theory of knowledge.

Practical Tips

• Engage with local schools by proposing a curriculum module on minerals in technology. Collaborate with science teachers to develop a program that educates students about the role of minerals in modern devices, including AI systems. This initiative can raise awareness among the younger generation about the significance of minerals and encourage responsible usage and recycling of electronic devices.
• You can diversify your investment portfolio by including stocks from companies that are involved in the mining or recycling of scarce minerals like lithium. By doing so, you're not only potentially benefiting from the growth of AI technology but also supporting the supply chain that underpins it. For example, look for mutual funds or ETFs that focus on raw materials or specific industries like battery production and recycling.
• Consider purchasing electric vehicles (EVs) or devices that use lithium-ion batteries to directly contribute to the demand for lithium. This personal choice supports the market for lithium...

Atlas of AI Summary The development and deployment of AI are tainted by the unjust and biased treatment of human laborers.

The industry of artificial intelligence relies significantly on a vast array of human labor, which is frequently hidden and taken advantage of, to maintain the appearance of automated efficiency.

The development and maintenance of AI technologies frequently rely on laborers who receive insufficient compensation and face unstable employment conditions.

Crawford reveals how the true costs of AI systems are systematically underestimated and obscured, revealing a pattern of reliance on low-wage workers across the globe. This labor, she explains, is seen as “ghost work” or “human-driven automation” and is widely used by the AI ​​field to complete tasks that machines cannot do faster and cheaper than hiring students. From annotating images for computer vision systems to testing whether algorithms produce the right results, these jobs support the functionality of AI systems, but workers rarely receive the recognition and fair compensation they deserve.

The author examines the swift growth of online microtask platforms, highlighting the considerable power disparity that benefits the organizations assigning these tasks compared to the laborers. She depicts workers as cogs in a vast...

Atlas of AI Summary The ramifications of collecting data, its organization, and the built-in biases present in artificial intelligence algorithms.

The book delves into the intrinsic influence that training data has on AI systems, highlighting the inherently political aspects of gathering data, its classification, and the difficulties involved in addressing biases.

The progression of AI models has been driven by the extensive collection and assimilation of personal data, frequently without the individuals' permission.

Crawford argues that the norm within the technology sector involves amassing extensive personal data without consent, justifying this practice with the belief that more data is always better. She reveals the disconcerting methods of collecting vast amounts of data for training purposes, often derived from individuals' social media accounts and accessible public databases, with scant regard for the protection of personal information or the ethical handling of such data.

The author exposes the erroneous logic in considering data simply as an asset to be exploited, without recognizing that it encapsulates a collection of individual expressions, important life events, and confidential information. Crawford emphasizes the need to critically evaluate the drive to gather data and recognizes the potential harm...

Atlas of AI Summary The integration of artificial intelligence by the government into its systems of administration, regulation, and monitoring.

Crawford delves into the complex relationship between artificial intelligence and state power, scrutinizing how AI tools are employed not just for monitoring and controlling citizens, but also for reshaping the very nature of governance and supervision.

Intelligence and law enforcement agencies are progressively utilizing AI tools to enhance their surveillance capabilities and to implement proactive policing strategies.

Crawford explores how artificial intelligence expands and intensifies conventional methods of surveillance by governments across the globe. She delves into the secretive domain where intelligence agencies employ sophisticated algorithms to amass vast amounts of information, assess potential threats, and carry out targeted operations.

Crawford highlights the strategy employed by the NSA in creating initiatives like TREASUREMAP and FOXACID, which involves collecting extensive data sets to detect anomalies via analytical methods. She reveals how these approaches, once exclusive to intelligence agencies, have now filtered down to local police departments through partnerships with companies like Palantir, leading to increased surveillance of vulnerable...