UNPACKING ALGORITHMIC BIAS IN ONLINE MENTAL HEALTH RESOURCES

The virtual landscape is rapidly evolving to deliver mental health assistance. However, these tools often rely algorithms that can propagate existing societal biases. This raises a serious threat as users seeking aid may encounter prejudiced outcomes based on their ethnicity, sex, or other personal traits. It is essential to unpack these biases and

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