Because Technology Discriminates
Description
Engineers designing technologies and systems produce problems when they do not account for existing biases in society. Designers have a mandate to make technologies efficiently, economically, and ethically. This textbook is written for both students and practicing designers, engineers, researchers, or artists who want to create more ethical designs; it aims to help readers understand how race is implicated in technology design. Learning from historical and contemporary case studies of engineering and architecture projects will help readers see clearly the power of design decisions to either perpetuate or contest racism. Chapter exercises will change engineers' mental models to see the bias inherent to existing technological design. By incorporating the knowledge and insights of community-based experts into design projects, readers will begin to practice anti-racist leadership and counter-expertise.
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Logan D. A. Williams is a science and technology studies scholar who investigates knowledge from the margins. In addition to her science and technology studies PhD, she has a BS and MS in Mechanical Engineering. For her MS thesis she designed an ultrasound medical imaging technology. Her first book was a multi-sited global ethnography (India, Nepal, Mexico, Kenya, US). It highlighted the innovations created by ophthalmologists and engineers in South Asia and circulated to eradicate avoidable blindness among the world's poor. More generally, her work explores technology users, technology design and technology governance typically in health and information technologies. She has previously been a tenure track professor in Lyman Briggs College and Sociology at Michigan State University and a lecturer in the A. James Clark School of Engineering at University of Maryland, College Park.
Content
Introduction.- Race, Gender and the Myth of Technological Incompetence.- Your diverse design team is very competent: how to lead relationally.- The race audit: mitigating environmental racism in civil infrastructure.- Data racism, machine learning and the importance of context in design.- Designing sustainable organizations to create ethical designs.