Addictive Design
Interface patterns and features intentionally designed to maximize user engagement and time spent on a platform, often at the expense of user wellbeing. Examples include infinite scroll, autoplay, streak mechanics, and variable-ratio reinforcement (like pull-to-refresh). Several child safety laws now specifically target addictive design features when applied to minor users.
Age Assurance
An umbrella term encompassing all methods used to determine or estimate a user's age, including age verification, age estimation, and self-declaration. Age assurance measures may range from simple date-of-birth entry to document-based verification or AI-powered facial analysis. Regulators increasingly require some form of age assurance for platforms accessible to children.
Age Estimation
Technology that estimates a user's age range without requiring them to provide identity documents. Common methods include facial age analysis using AI, behavioral analysis, and device-level signals. Age estimation provides a privacy-preserving alternative to document-based age verification, though it is less precise and typically determines an age range rather than exact age.
Age Gating
A mechanism that restricts access to content, features, or entire platforms based on a user's age. Age gates can be implemented through self-declaration (entering a date of birth), parental consent flows, or technical verification methods. While simple age gates like date-of-birth fields are easy to bypass, more robust implementations combine multiple signals for higher confidence.
Age Verification
The process of confirming a user's exact age or that they meet a minimum age threshold, typically using identity documents, credit card checks, or government ID databases. Age verification provides higher confidence than age estimation but raises privacy concerns about data collection. Several US states and international jurisdictions now mandate age verification for certain online platforms.
Algorithmic Amplification
The use of recommendation algorithms to promote and increase the visibility of content to users, often prioritizing engagement metrics over user wellbeing. When applied to minor users, algorithmic amplification can surface harmful content including self-harm material, eating disorder content, and extremism. Multiple child safety laws now require platforms to disable or limit algorithmic amplification for minors.
Content Filtering
Automated systems that screen, block, or restrict access to content deemed inappropriate for certain audiences. Content filtering for child safety may operate at the platform level (blocking harmful search results), device level (parental control apps), or network level (ISP-based filters). Modern filtering systems use AI classification models alongside keyword and hash-matching approaches.
Content Moderation
The practice of monitoring and reviewing user-generated content to enforce platform policies and legal requirements. Content moderation combines automated detection systems with human review to identify and act on harmful material such as child sexual abuse material (CSAM), cyberbullying, and age-inappropriate content. Platforms must balance effective moderation with free expression concerns.
Dark Patterns
User interface designs that manipulate users into making choices they would not otherwise make, such as sharing more personal data, disabling privacy settings, or continuing to use a service. In the context of child safety, dark patterns include making privacy controls difficult to find, using confusing language in consent flows, and employing emotional manipulation to prevent account deletion. The EU DSA and California AADC specifically prohibit dark patterns targeting minors.