A recent study highlighted the persistent bias in open source AI tools used for resume vetting, favoring male candidates over equally qualified females. Conducted by Sugat and Rochana Chaturvedi, the researchers analyzed over 300,000 job ads from India's National Career Services. They discovered that AI models tend to preferentially select men, particularly for higher-paying positions, and perpetuate gender stereotypes that position women for lower-paid roles. This issue arises from biases in training data, compounded by an 'agreeableness bias' in reinforcement learning.
The findings reiterate that open source AI tools, like traditional resume screening methods, show a clear bias toward male candidates, especially in higher-wage roles.
Researchers suggest that entrenched gender patterns influence AI systems, pushing equally qualified women towards lower-wage roles due to inherent biases in training data.
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