Designing a Ratings & Age Classification System for Online Games

Ratings Policy Intern | Netflix

context

As Netflix expanded into interactive entertainment, it faced a new challenge: how to responsibly classify and communicate online gaming content to a global audience.

Existing ratings systems like ESRB, PEGI, and IARC primarily focused on traditional game content—violence, language, and sexual themes—but underrepresented risks unique to online play, such as toxicity, gambling-like features, and user-generated content.

Goal: Develop a modern, scalable framework for rating Netflix games that reflects both traditional content and emerging behavioral risks, giving parents and players the clarity to make informed choices.

problem

Netflix needed a trusted ratings model that could extend its established maturity system into the world of games—balancing global consistency, cultural nuance, and digital safety.

Challenges included:

  • Lack of standardized signals for online interactivity and toxicity

  • Limited representation of monetization and gambling-like mechanics

  • Need for a scalable system that fits Netflix’s cross-media ecosystem

  • Parental confusion around what “online interactions” actually entail

Challenge: Build a next-generation ratings model that addresses the full spectrum of online experiences—from gameplay content to player behavior.

1. Research & Benchmarking

  • Conducted a comparative analysis of global rating systems (ESRB, PEGI, IARC, ACB).

  • Identified gaps in current models, particularly around interactivity, discrimination, and monetization.

  • Consulted with internal content policy, product, and operations teams to align goals.

process

2. Framework Design

Authored the exploratory policy report “Designing a New Ratings & Age Classification System for Online Games.”

Proposed a multi-dimensional Netflix Game Rating Scale covering five axes:

  1. Content Intensity – violence, sex, language, discrimination, drugs, horror

  2. Interactivity – multiplayer, chat, UGC, roleplay

  3. Monetization – loot boxes, gambling-like systems

  4. Discrimination & Toxicity – harassment, slurs, stereotyping

  5. Systemic Risks – persistent IDs, cross-platform exposure

Example rating output:

“Rated 12+ for violence; online interactivity may expose players to discriminatory chat; contains loot boxes.”

3. Policy & Tooling Proposals

  • Developed reviewer playbooks with decision trees for toxicity, gambling, and UGC moderation.

  • Recommended override-only advisories and optional interaction/stereotype tags for transparency.

  • Proposed ML classifiers to detect toxic chat, gambling mechanics, and user-generated risk content.

  • Outlined UX improvements for storefront advisories and parental controls.

4. Testing & Implementation Plan

  • Designed a 90-day rollout roadmap including pilot testing with 50 Netflix titles.

  • Proposed A/B testing of advisories to measure parental comprehension and complaint reduction.

  • Defined success metrics:

    • ↑ Parental understanding of advisories

    • ↓ Complaint rates for toxicity/gambling

    • ≥85% reviewer agreement across labels

“The system bridges entertainment and safety—helping families understand not just what’s in a game, but how it behaves online.”

outcome

  • Created a scalable, future-ready ratings framework adaptable to Netflix’s global gaming catalog.

  • Provided actionable recommendations for policy, product, and ML teams to enhance player transparency.

  • Established a foundation for cross-platform trust and parental confidence in Netflix Games.

impact

  • Advanced Netflix’s readiness for ethical and transparent game publishing.

  • Introduced policy innovation that merges entertainment standards with online safety and user trust.

  • Strengthened the bridge between content policy, technology, and player experience.

  • Deepened understanding of global regulatory alignment, from PEGI to IARC and ACB systems.