Sex Trouble

"Sex Trouble" in medical machine learning highlights the pervasive biases stemming from flawed assumptions about sex and gender, particularly their binary and static nature. Current research focuses on identifying and mitigating "sex confusion," "sex obsession," and "sex/gender slippage" in datasets and algorithms, emphasizing the need for more nuanced approaches to sex/gender data in medical applications. This work is crucial for improving the fairness, accuracy, and effectiveness of machine learning models in healthcare, ensuring equitable outcomes for all patients.