“This is an essential text for anyone grappling with issues of AI bias, fairness, and justice.

Whatever else they are, AI systems are systems of classification. In brief, they ‘learn’ what they know from data, and they use what they learn to classify what they ‘see.’ For example, an AI system for hiring might be taught what a ‘promising job candidate’ looks like by [inputting] videos of ‘successful workers.’ Show this AI system a candidate video, and it compares the video to its ‘successful worker’ composite, classifying the candidate as either promising, or not. Such systems are already in use, and the stakes are high: if, for instance, black women weren’t represented among the ‘successful workers’ training videos, then it’s unlikely the system would classify them as ‘promising’ and unlikely that a black woman would ever get hired.

Sorting Things Out engages with the politics and consequences of such classification practices, treating classification not as a reflection of ‘natural categories,’ but as a product of history, culture, and power in which ‘each category valorizes some point of view, and silences another.’ The book examines classification systems ranging from Apartheid South Africa’s racial passbook, which struggled to apply rigid racial categories to diverse human bodies, to the World Health Organization’s International Classification of Diseases, which requires a vast bureaucracy in its attempt to normalize cultural differences in the understanding of illness and health. By attending to these histories, the authors expose the contingency of categories we often take for granted, providing a foundational resource for understanding, critiquing, and contesting the AI systems that are currently automating classification across core social domains.”

★ THE MASTER ALGORITHM, BY PEDRO DOMINGOS