Effective CSAM filters are impossible because what CSAM is depends on context

Effective filters for detecting child sexual exploitation materials (CSAM) face a significant challenge due to the contextual nature of what constitutes CSAM. The distinction between innocent content and CSAM depends heavily on the context in which the material is shared. Even identical images can be harmless when exchanged within a family but become CSAM when circulated in inappropriate settings. Technical solutions, such as hashing algorithms or AI, are often proposed but fall short in accurately identifying CSAM without invading privacy or mislabeling innocent content. The key issue lies in the fact that crucial contextual information, like the relationship between individuals and the nature of their communication, is not embedded in the media files themselves. Without access to this context, any filtering tool would struggle to differentiate between harmless and harmful content effectively. Addressing the serious problem of sexual exploitation of children requires a comprehensive approach beyond relying on technological solutions, emphasizing the need for targeted programs and interventions instead of unrealistic filtering mechanisms.