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Caroline Criado Perez

Invisible Women

Reveal the hidden gender gap in data—and how closing it can unlock smarter decisions, better products, and a fairer world.
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What Will You Learn?

Data gaps designed around men create dangerous blind spots for women – Invisible Women by Caroline Criado Perez exposes how gender data bias shapes our world.

This comprehensive gender data gap summary and main ideas explain:

  • Default Male - Everything from car safety to medical doses assumes male bodies as standard
  • Unpaid Work - GDP calculations ignore $10 trillion in women's unpaid labor annually
  • Snow Clearing - Prioritizing roads over sidewalks costs more due to women's pedestrian injuries
  • Workplace Design - Office temperatures set for male metabolic rates reduce female productivity 40%



The hidden gem: Perez's "Yentl Syndrome" reveals women die from heart attacks misdiagnosed as anxiety.

Your Click.Apply.Grow™ blueprint: Question "one-size-fits-all" solutions tomorrow. Collect gender-disaggregated data. Design inclusively. Organizations closing data gaps report 35% better outcomes for all users.

Fix the data, fix the world.

invisible-women original-book-image

About the Author

Caroline Criado Perez is the data detective exposing how a world designed for men literally kills women, winning the 2019 Royal Society Science Book Prize.

British journalist and feminist campaigner who successfully lobbied Bank of England to feature Jane Austen on £10 notes. Invisible Women sold 500,000+ copies, translated into 30+ languages, revealing deadly gender data gaps from car crashes to heart attacks. Winner Financial Times Book of the Year 2019. Regular Guardian contributor with 200,000+ newsletter subscribers. OBE recipient for services to equality.

2025 relevance: As AI trains on biased data, Perez's framework becomes critical for preventing algorithmic discrimination at scale.

Her research influences policy at WHO, UN, and governments globally. Organizations implementing her recommendations report 50% reduction in gender bias incidents.

The woman making the invisible visible through irrefutable data.

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