Emily Wall – författare

Poet Emily Wall began collecting birth stories after the birth of her third child, Lucy. She realized that women were always quietly sharing their stories—in living rooms with a mug of tea, or whispered at the preschool playground. She saw the intensity with which women listened to each other’s stories. They were shared, remembered, retold, but not collected, not treated as the art form they are. Wall began asking for, and collecting birth stories: women sent her emails, handed her their journals, and recorded their own voices. She collected stories from a lesbian couple, a story from an indigenous father who is fighting for his language, and a story from a grandmother. Some of the stories are about difficult and painful births: a woman who had a miscarriage, a woman unable to get pregnant. And some of the stories are beautiful: a birth in water that happened exactly as the mother dreamed it would. Wall has taken these stories and shaped them into poems, and then into this collection, offering the reader a look into the story that women, for centuries, have been quietly sharing with each other.

This open access book demonstrates how human biases affect the process of visual data analysis, a subject which has typically been left to researchers in cognitive and perceptual psychology and the social sciences. Human biases affect the way that people interpret and experience the world and how they operate within it and make decisions. These can include cognitive biases such as confirmation or anchoring bias, perceptual biases including visual or auditory illusions, and implicit biases such as racial or gender bias that are often borne of harmful cultural norms and stereotypes. In the context of visual data analysis, this book explores (1) what these biases are, (2) how to characterize them, and (3) how to mitigate them through designing digital interventions. This book synthesizes years of work on detecting and mitigating biases in visual data analysis and project directions for the next decade of research and practice. It represents an accessible entry point to understanding the prevalence of biases in computing before taking readers on a deeper dive into empirical studies on the efficacy of various bias mitigation interventions. It will synthesize years of research into a digestible portal to technical work on visual data analysis. Data scientists and citizens alike can benefit from this book by reflecting on their own unique privileges and susceptibility to biases and scrutinizing how digital interventions, sometimes as simple as adding one extra step to verify the decision by checking “yes,” might be integrated or enacted in their own personal and professional decision making settings.