Meta” izmantos MI un analizēs augumu, kaulu struktūru, lai noteiktu lietotāju vecumu – Kursors.lv

Meta Platforms Inc. Is developing artificial intelligence systems designed to estimate the age of its users by analyzing physiological markers, including bone structure and growth patterns, as part of a broader effort to enhance child safety and meet global regulatory requirements.

The technology focuses on computer vision and machine learning to analyze images and videos provided by users. By identifying specific skeletal landmarks and growth trajectories in the facial and cranial structure, the AI can differentiate between adolescents and adults with a higher degree of accuracy than traditional self-reporting methods.

This shift toward AI-driven age estimation is a response to increasing pressure from governments worldwide to prevent minors from accessing adult content and to restrict the data collection practices applied to children. On May 10, 2026, reporting indicated that Meta is prioritizing these biometric analysis tools to reduce the friction associated with government-issued identification, which many users are reluctant to provide.

Technical Implementation of Age Estimation

The AI system operates by scanning for biological indicators of maturity. Unlike simple facial recognition, which identifies a specific individual, age estimation analyzes general patterns of human development. This includes the ossification of certain facial bones and the proportional changes in the skull that occur during puberty and early adulthood.

Meta’s approach utilizes deep learning models trained on massive datasets of images across various age groups and ethnicities to minimize bias. The system analyzes the distance between facial landmarks and the curvature of the jawline and brow, which are influenced by underlying bone growth.

The company distinguishes this process as age estimation rather than age verification. While verification requires a legal document to prove a specific identity, estimation uses AI to predict an age range. If the AI determines a user is likely under the age of 18, the platform can then trigger a secondary, more stringent verification process or apply restrictive safety settings automatically.

Regulatory Drivers and Legal Compliance

The deployment of these tools follows a series of stringent legislative mandates in the European Union and the United Kingdom. The European Union’s Digital Services Act (DSA) requires very large online platforms to implement robust measures to protect minors, including restricting the profiling of children for advertising purposes.

Similarly, the United Kingdom’s Online Safety Act mandates that platforms take proactive steps to ensure children are not exposed to harmful content. Failure to implement effective age-gating mechanisms can result in significant financial penalties, often calculated as a percentage of a company’s global annual turnover.

By implementing bone-structure analysis, Meta aims to create a scalable solution that works across its suite of apps, including Facebook and Instagram, without requiring every user to upload a passport or driver’s license.

Business and Privacy Implications

From a business perspective, accurate age determination is critical for Meta’s advertising model. Advertisers often target specific age demographics, and the misclassification of users can lead to inefficient ad spend and potential legal liability if ads for age-restricted products are served to minors.

However, the use of AI to analyze bone structure raises significant privacy concerns. Critics argue that the collection of biometric data for age estimation could be repurposed for surveillance or lead to the creation of permanent biological profiles of users.

Meta has stated that the AI analysis is intended to be ephemeral, meaning the biometric data used for the estimation is processed and then deleted rather than stored in a permanent database. The company claims this approach balances the need for safety with the user’s right to privacy.

Industry Context and Market Trends

Meta is not the only entity exploring biometric age estimation. Several third-party verification services have already deployed similar AI tools that analyze “facial age” to help websites comply with age-restricted laws. The industry trend is moving away from static date-of-birth entries, which are easily falsified by minors.

The effectiveness of these tools remains a point of debate among technologists. Variability in lighting, camera quality, and the use of filters can impact the accuracy of AI bone-structure analysis. Meta is expected to use these AI estimations as one layer of a multi-signal approach, combining biometric data with behavioral patterns to determine a user’s age.

Behavioral signals may include the types of accounts a user follows, the language they use in posts, and their interaction patterns with other users. When these behavioral signals align with the AI’s bone-structure estimation, the platform gains higher confidence in the user’s age.