When the Past Learns to Code

2026-07-12 · Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Social Sciences & Behavioral Studies · Depth: Fundamental Awareness, short

Summary

Laura Bates' book, "The New Age of Sexism — How the AI Revolution is Reinventing Misogyny," argues that artificial intelligence, far from being a purely futuristic and objective technology, inherently stems from the past by learning from biased and incomplete human data. The book demonstrates how historical inequalities, particularly gender-based ones, become deeply embedded in algorithms influencing critical areas like recruitment, healthcare, and policing. This embedding reveals that technology is never built on neutral ground; every dataset carries history, every model inherits culture, and every prediction reflects past human choices and blind spots. The author extends this realization to workplace culture, noting that organizations, like AI, largely inherit their operational norms. The central question posed is whether AI, as a vast repository of human knowledge, will also perpetuate unexamined societal assumptions, highlighting humanity's profound responsibility in shaping what these intelligent systems are taught for future generations.

Key takeaway

For AI Ethicists and policy makers designing or regulating intelligent systems, recognize that AI inherently reflects historical human biases embedded in its training data. You must proactively audit datasets for inherited inequalities, especially gender-based ones, to prevent perpetuating damaging societal cycles in critical applications like healthcare and recruitment. Your responsibility extends to ensuring these powerful systems are taught a more equitable future, not merely a reflection of an unexamined past.

Key insights

AI inherits human biases from historical data, embedding past inequalities into future systems.

Principles

• AI systems inherit human bias, not invent it.
• Technology is never built on empty ground.
• Every dataset carries history and culture.

Topics

• AI Bias
• Data Bias
• Algorithmic Ethics
• Gender Inequality
• AI Governance
• Historical Data

Best for: AI Scientist, Research Scientist, AI Product Manager, AI Ethicist, Policy Maker, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.