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Secondary Analysis of Electronic Health Records
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable r...
Secondary Analysis of Electronic Health Records
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable r...
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data e...
Uncertain Archives
Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability. This pathbreaking work offers an interdisciplinary perspective on big data, interrogating key terms. Scholars from a range of disciplines interrogate concepts relevant to critical studies of big data--arranged glossary style, from from abuse and aggregate to visualization and vulnerability--both challenging conventional usage of such often-used terms as prediction and objectivity and introducing such unfamiliar ones as overfitting and copynorm. The contributors include both leading researchers, including N. Katherine Hayles, Johanna Drucker and Lisa Gitelman, and such emerging agenda-setting scholars as Safiya Noble, Sarah T. Roberts and Nicole Starosielski.
All Data Are Local
How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States—Harvard's Arnold Arboretum, the Digi...
Machine Learners
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esote...
Transmissions
Researchers rethink tactics for inventing and disseminating research, examining the use of such unconventional forms as poetry, performance, catalogs, interactive machines, costume, and digital platforms. Transmission is the research moment when invention meets dissemination—the tactical combination of making (how theory, methods, and data shape research) and communicating (how research is shown and shared). In this book, researchers from a range of disciplines examine tactics for the transmission of research, exploring such unconventional forms as poetry, performance, catalogs, interactive machines, costume, and digital platforms. Focusing on transmissions draws attention to a critical pa...
Child Data Citizen
An examination of the datafication of family life--in particular, the construction of our children into data subjects. Our families are being turned into data, as the digital traces we leave are shared, sold, and commodified. Children are datafied even before birth, with pregnancy apps and social media postings, and then tracked through babyhood with learning apps, smart home devices, and medical records. If we want to understand the emergence of the datafied citizen, Veronica Barassi argues, we should look at the first generation of datafied natives: our children. In Child Data Citizen, she examines the construction of children into data subjects, describing how their personal information is collected, archived, sold, and aggregated into unique profiles that can follow them across a lifetime.
Discriminating Data
How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine lear...
Giving Bodies Back to Data
An examination of the bodily, situated aspects of data-visualization work, looking at visualization practices around the development of MRI technology. Our bodies are scanned, probed, imaged, sampled, and transformed into data by clinicians and technologists. In this book, Silvia Casini reveals the affective relations and materiality that turn data into image--and in so doing, gives bodies back to data. Opening the black box of MRI technology, Casini examines the bodily, situated aspects of visualization practices around the development of this technology. Reframing existing narratives of biomedical innovation, she emphasizes the important but often overlooked roles played by aesthetics, aff...
