UNPACKING ALGORITHMIC BIAS IN ONLINE MENTAL HEALTH RESOURCES

The online landscape is rapidly evolving to offer mental health assistance. However, these tools often rely algorithms that can propagate existing societal biases. This raises a serious threat as users seeking support may encounter prejudiced outcomes based on their background, identity, or other protected characteristics. It is crucial to analyze

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