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Case study: Racism and sexism in AI-generated images

A research study exposes race and gender biases in AI-generated images. This is one of the best case studies I saw and it helps correct misconceptions. Highlights & reflections.

➤ The study compared GenAI images of professionals to information from the US census.

See highlights in the attached pdf.

➤ It found that GenAI exacerbates gender and race misrepresentation:

For example:

💔Most AI-generated housekeeper images portrayed non-white people. 

But, in reality, most US housekeepers identify as white.

💔All AI-generated flight attendant images portrayed women. 

But, in reality, only about 65% of US flight attendants identify as women.

➤ Reflections

This study helps correct misconceptions about AI fairness problems

🔥Some people think that AI biases “just” represent the biases in reality. 

🔥Not the case!

🔥First, often, AI exacerbates the bias.

🔥Second, GenAI images impact reality. A flood of imbalanced generated images can worsen real-life stereotypes.

➤ The paper’s authors:

Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza , Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 

➤ The illustration in the pdf from an article by Anaya

➤ What do other people think? Join the conversation in the LinkedIn thread!

GenAI stereotypes
Download PDF • 708KB



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