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From Muscle to Metrics: Artificial Intelligence as the Next Plough Hypothesis

Sun, August 9, 2:00 to 3:30pm, TBA

Abstract

This paper argues that artificial intelligence is a foundational technology, much like the agricultural plough was in the past, and that it has the capacity to entrench inequality in long-lasting ways. The “plough hypothesis” explains how an early tool reshaped work so that men’s greater physical strength was favored, creating gender roles that stuck around long after the original economic reasons had faded. Building on this idea, the paper suggests that AI may play a similar role today by embedding old gendered assumptions into automated systems that ultimately define what counts as competence, productivity, professionalism, and risk.

Building on research on gender data gaps and male-default design, the paper examines how AI may reinforce gender inequality by altering how people are evaluated, sorted, and judged across many areas of life. Inspired by Caroline Criado Perez’s Invisible Women—which shows how everyday systems are often built around unspoken male norms—the paper asks what happens when these assumptions extend beyond individual products or datasets and become embedded in technologies that organize work and decision-making at a large scale.

Instead of focusing solely on bias in individual AI tools, the paper adopts a broader sociological perspective. AI systems depend heavily on historical data and existing institutional ideas about success. As a result, they tend to favor forms of work, behavior, and interaction already associated with dominant groups. Because these systems are presented as technical, objective, or neutral, the gendered assumptions embedded in them can be difficult to discern and even harder to question. Over time, this can create feedback loops in which inequality is reproduced and increasingly regarded as the natural outcome of neutral processes, rather than as something shaped by social choices and power.

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