An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. • Introduces the technical and normative foundations of fairness in automated decision-making • Covers the formal and computational methods for characterizing and addressing problems • Provides a critical assessment of their intellectual foundations and practical utility • Features rich pedagogy and extensive instructor resources
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers
An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. • Introduces the technical and normative foundations of fairness in automated decision-making • Covers the formal and computational methods for characterizing and addressing problems • Provides a critical assessment of their intellectual foundations and practical utility • Features rich pedagogy and extensive instructor resources
In recent years, tech companies such as Google and Facebook have rocked the world as they have seemingly revolutionized the culture of work. We've all heard stories of lounges outfitted with ping pong tables, kitchens with kombucha on tap, and other amenities that supposedly foster creative thinking. Nothing could seem further from earlier workplaces associated with a different revolution in capitalism: factories, in which employees are required to perform highly circumscribed tasks as quickly as possible to meet quotas--for next to no pay. However, as Moritz Altenried shows in The Digital Factory, these types of workplaces are not so far from the Googleplex as we might think. While recent accounts of the transformation of labor after the demise of the factory highlight the creative, communicative, immaterial, or artistic features of contemporary labor, Altenried uncovers the factory-like conditions in which many new digital workers perform their jobs. These workers, such as video game testers, social media content moderators, and Amazon fulfillment center workers, perform highly repetitive, unskilled tasks for low and often contingent wages. Based on more than five years of research in different sites using ethnography and interviews combined with an analysis of infrastructural technologies, Altenried's book gives us a first-hand account of many new forms of digital labor that drive contemporary capitalism. He shows that though today's factories might look and feel different than they did 150 years ago, they still follow the same logics and produce the same unequal outcomes"--
Moritz Föllmer traces the history of individuality in Berlin from the late 1920s to the construction of the Berlin Wall in August 1961. The demand to be recognised as an individual was central to metropolitan society, as were the spectres of risk, isolation and loss of agency. This was true under all five regimes of the period, through economic depression, war, occupation and reconstruction. The quest for individuality could put democracy under pressure, as in the Weimar years, and could be satisfied by a dictatorship, as was the case in the Third Reich. It was only in the course of the 1950s, when liberal democracy was able to offer superior opportunities for consumerism, that individuality finally claimed the mantle. Individuality and Modernity in Berlin proposes a fresh perspective on twentieth-century Berlin that will engage readers with an interest in the German metropolis as well as European urban history more broadly.
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
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