Marina Krakovsky on Muck Rack

Marina Krakovsky

San Francisco
As seen in: Medium, Forbes, Psychology Today, Stanford University, Communications of the ACM, Fast Company, Harvard Business Review, Inc., Scientific American, Discover Magazine and
Author of THE MIDDLEMAN ECONOMY: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit (amzn.to/2dNdFaf). Business ghostwriter.
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Marina Krakovsky’s Biography

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A Silicon Valley-based social science and business journalist with a degree from Stanford University, Marina Krakovsky is the author of The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit (Palgrave Macmillan). She is also
co-author, with economist Kay-Yut Chen, of Secrets of the Moneylab: How Behavioral Economics Can Improve Your Business (Portfolio/Penguin).

As a speaker, Marina frequently presents to professional groups, associations, and comp…

Articles

Mentira y racionalización

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By Opinión Caribe, Marina Krakovsky
| opinioncaribe.com
Por: Edgar Romero Vasquez, MScEE (Master of Sciences in Electrical Engineering), Ph.D La mentira es parte de la vida en la tierra, está en la naturaleza, de muchas formas y para sobrevivir. Un caso es el de la gacela que salta como medida contra sus depredadores, por ejemplo intenta mostrar a su persecutor que tiene extraordinaria energía y habilidad y que es inútil que la persigan.
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Mandy O'Neill: Why Capable Women Don't Always Realize Their Potential

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By Marina Krakovsky
| Stanford University
When Olivia “Mandy” O’Neill entered the PhD Program at Stanford in 2000, the question she most wanted to answer was one that had been puzzling researchers and employers alike: Why were promising professional women failing to realize their potential in the workforce? Previous research into this question, which tried to disentangle people’s biological sex from their gender identity, left everyone scratching their heads.
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Formalizing Fairness

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By Marina Krakovsky
| Communications of the ACM Verified
By Marina Krakovsky Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 11-13 10.1145/3542815 Comments View as: Print Mobile App ACM Digital Library Full Text (PDF) In the Digital Edition Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook As machine learning has made its way into more and more areas of our lives, concerns about algorithmic bias have escalated.
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