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When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity (SSIR)

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In 2019, Genevieve (co-author of this article) and her husband applied for the same credit card. Despite having a slightly better credit score and the same income, expenses, and debt as her husband, the credit card company set her credit limit at almost half the amount. This experience echoes one that made headlines later that year: A husband and wife compared their Apple Card spending limits and found that the husband's credit line was 20 times greater. Customer service employees were unable to explain why the algorithm deemed the wife significantly less creditworthy. Many institutions make decisions based on artificial intelligence (AI) systems using machine learning (ML), whereby a series of algorithms takes and learns from massive amounts of data to find patterns and make predictions.



When governments turn to AI: Algorithms, trade-offs, and trust

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The notion reflects an interest in bias-free decision making or, when protected classes of individuals are involved, in avoiding disparate impact to legally protected classes.3


When governments turn to AI: Algorithms, trade-offs, and trust

#artificialintelligence

The notion reflects an interest in bias-free decision making or, when protected classes of individuals are involved, in avoiding disparate impact to legally protected classes.3


When governments turn to AI: Algorithms, trade-offs, and trust

#artificialintelligence

The notion reflects an interest in bias-free decision making or, when protected classes of individuals are involved, in avoiding disparate impact to legally protected classes.3