Concerns arise over the reliability of deepfake detection models, particularly regarding inherent biases in training data that may lead to unequal error rates across different racial and gender groups. Research from the University of Southern California highlights the potential dangers of these biases, suggesting that existing detection tools may not be adequately addressing these issues. As the technology is still in its early stages, the effectiveness of current solutions remains questionable.
