Thousands of victims have sued Apple over its alleged failure to detect and report illegal child pornography, also known as child sex abuse materials (CSAM).

The proposed class action comes after Apple scrapped a controversial CSAM-scanning tool last fall that was supposed to significantly reduce CSAM spreading in its products. Apple defended its decision to kill the tool after dozens of digital rights groups raised concerns that the government could seek to use the functionality to illegally surveil Apple users for other reasons. Apple also was concerned that bad actors could use the functionality to exploit its users and sought to protect innocent users from false content flags.

Child sex abuse survivors suing have accused Apple of using the cybersecurity defense to ignore the tech giant's mandatory CSAM reporting duties. If they win over a jury, Apple could face more than $1.2 billion in penalties. And perhaps most notably for privacy advocates, Apple could also be forced to "identify, remove, and report CSAM on iCloud and implement policies, practices, and procedures to prevent continued dissemination of CSAM or child sex trafficking on Apple devices and services." That could mean a court order to implement the controversial tool or an alternative that meets industry standards for mass-detecting CSAM.

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For years, hashing technology has made it possible for platforms to automatically detect known child sexual abuse materials (CSAM) to stop kids from being retraumatized online. However, rapidly detecting new or unknown CSAM remained a bigger challenge for platforms as new victims continued to be victimized. Now, AI may be ready to change that.

Today, a prominent child safety organization, Thorn, in partnership with a leading cloud-based AI solutions provider, Hive, announced the release of an API expanding access to an AI model designed to flag unknown CSAM. It's the earliest use of AI technology striving to expose unreported CSAM at scale.

An expansion of Thorn's CSAM detection tool, Safer, the AI feature uses "advanced machine learning (ML) classification models" to "detect new or previously unreported CSAM," generating a "risk score to make human decisions easier and faster."

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