DISSECTING ALGORITHMIC BIAS IN ONLINE MENTAL HEALTH RESOURCES

The virtual landscape is rapidly evolving to provide mental health support. However, these tools often rely algorithms that can reinforce existing societal biases. This raises a serious issue as users seeking aid may encounter discriminatory outcomes based on their ethnicity, sex, or other sensitive attributes. It is crucial to analyze these biases

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