False positives in child safety AI erode trust, create injustices, and pose significant legal and social challenges. This article analyzes their causes, how different system architectures handle them, and specific engineering choices to mitigate these errors.
Tuteliq is a real-time harm detection layer for AI assistants, focusing on child safety and online threats. It offers 50 detection tools across text, voice, image, and video, providing structured risk scores and compliance features for GDPR and KOSA.
This article argues that fairness evaluation, specifically demographic parity, is a critical and non-negotiable deployment constraint for AI systems in child safety. Disproportionate flagging harms users, creates legal risks, and undermines trust, while also missing threats in underrepresented groups due to biased datasets.
Pennsylvania approved the SAFECHAT law, regulating AI chatbots for minors by prohibiting explicit or self-harm content and requiring disclosure of non-human status. This forces companies to choose between proactive safety in design or reactive compliance with increasing AI child protection regulations.