This book has scope and relevance well beyond feminism (although that’s plenty reason for reading it). Even more critically, in reaches into the realms of data provenance and general bias. I learned several new, useful terms when reading this.
Privilege hazard : When data teams are primarily composed of people from dominant groups, those perspectives come to exert outsized influence on the decisions being made—to the exclusion of other identities and perspectives.
Jim Code : When we bake societally-inherent bias into algorithms
If these sound alarmist to you, consider how much we typically take for granted that data speaks for itself, without considering its provenance. When you consider the how much etymology changes, even in the past twenty years, think about how fully incorporating change into historical data would affect the it’s analyzed and interpreted. Now consider how marketable a commodity ‘open data’ is, and think about how difficult it will be to correct error that’s been propagated and amplified for generations.
Read this book. If it does nothing more than increase your level of healthy skepticism of our latter-day data journalism it will have been worthwhile. Even better if it increases you concern as a data citizen on the content our AI models are ingesting at a rapid rate, that’s even better.