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A review by phire
Automating Inequality by Virginia Eubanks
4.0
This was more of a 3.5 or maybe a 3.75.
Things this book was good at: tracing the history of data discrimination from scientific charity to modern algorithms, painting a vivid picture of the human costs of poorly implemented automated systems, and contextualizing the three case studies in their respective political environments.
Things this book struggled with: occasionally getting caught up in folksy flourishes, lacking in-depth exploration of intersectional concerns, and lacking in-depth analysis of the "so what" component of the issue. The last chapter provides an overview of the examples discussed and attempts to make a case for why both liberals and conservatives should care that data-driven social services hurt the most marginalized. I found this the weakest chapter, especially the section tying data justice to so-called national values, and while reading it I couldn't help thinking of that "I Don't Know How To Explain To You That You Should Care About Other People" article. I think framing this conversation around how it fits into existing US partisan paradigms cedes more ground than is necessary, as it precludes considering an entirely new paradigm around this transnational issue. The conclusion is much sharper and more incisive in highlighting our collective responsibility to undo a problem that was deliberately caused by conscious choices.
You should read it if: you are fascinated (if repulsed) by detailed explorations of how politicians screw with the lives of ordinary people. I especially appreciated the discussion of LA's Skid Row and the housing matching system.
Things this book was good at: tracing the history of data discrimination from scientific charity to modern algorithms, painting a vivid picture of the human costs of poorly implemented automated systems, and contextualizing the three case studies in their respective political environments.
Things this book struggled with: occasionally getting caught up in folksy flourishes, lacking in-depth exploration of intersectional concerns, and lacking in-depth analysis of the "so what" component of the issue. The last chapter provides an overview of the examples discussed and attempts to make a case for why both liberals and conservatives should care that data-driven social services hurt the most marginalized. I found this the weakest chapter, especially the section tying data justice to so-called national values, and while reading it I couldn't help thinking of that "I Don't Know How To Explain To You That You Should Care About Other People" article. I think framing this conversation around how it fits into existing US partisan paradigms cedes more ground than is necessary, as it precludes considering an entirely new paradigm around this transnational issue. The conclusion is much sharper and more incisive in highlighting our collective responsibility to undo a problem that was deliberately caused by conscious choices.
You should read it if: you are fascinated (if repulsed) by detailed explorations of how politicians screw with the lives of ordinary people. I especially appreciated the discussion of LA's Skid Row and the housing matching system.