Feedback-Retrained Machine Learning Screening for Prohibited Game Content
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Solution Overview
Problem
Online gaming platforms face challenges in effectively identifying and preventing the access of users to inappropriate or prohibited content, particularly in the context of user-generated content, which can be difficult to monitor due to its volume and dynamic nature.
Innovation Solution
Implementing a machine learning model that classifies game content based on developer and user data, including tenure, behavior, and content characteristics, to automatically identify and restrict access to prohibited content, using feedback and periodic retraining to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content review is used to identify prohibited content, then classification accuracy can be maintained, but computational resources and time consumption increase significantly
Solution Approach 1:
The system performs preliminary automated classification of game content using machine learning models before manual review. This preliminary action filters out clearly safe content and flags only suspicious cases for manual review, significantly reducing the time and resources required for comprehensive content moderation while maintaining high accuracy.
Solution Approach 2:
A machine learning classification system serves as an intermediary between content upload and manual review processes. This intermediary automatically analyzes content characteristics, developer history, and user feedback to generate preliminary classifications, enabling efficient triage of content that requires human attention.
2Productivity
If automated classification systems are implemented to reduce manual review workload, then processing speed increases, but false positives may increase
Solution Approach 1:
The system incorporates feedback loops where manual review outcomes are used to retrain and refine the machine learning models. This continuous feedback mechanism reduces false positives over time by learning from actual human judgments, while maintaining high processing speeds through automated classification of the majority of content.
Solution Approach 2:
The classification system dynamically adjusts its thresholds and parameters based on accumulated data and performance metrics. This allows the system to optimize the balance between processing speed and false positive rate, adapting to emerging content patterns and reducing errors as it learns.
3Measurement precision
If comprehensive data collection from developers and users is performed to improve classification accuracy, then model performance improves, but data processing complexity increases
Solution Approach 1:
The system segments data collection and processing into distinct modules: developer profile analysis, content characteristic analysis, user feedback analysis, and classification model processing. This segmentation allows each module to handle specific data types efficiently, reducing overall system complexity while utilizing comprehensive data for improved accuracy.
4Reliability
If periodic retraining of machine learning models is performed to maintain accuracy, then classification reliability improves, but computational resources and time consumption increase
Solution Approach 1:
The system implements periodic retraining of machine learning models at scheduled intervals rather than continuously. This periodic action maintains classification reliability by updating models with recent data patterns while avoiding the continuous computational overhead of real-time retraining, thus balancing reliability with resource consumption.
Data Source
AI summary
Implementations described herein relate to methods, systems, and computer-readable media to automatically detect prohibited content. For example, a method can include receiving data associated with a game hosted on a gaming platform, the data indicative of at least one developer, at least one user, and game content, automatically assigning a classification to the game with a machine learning model, the classification based on the data indicative of the at least one developer, at least one user, and game content, determining whether the classification of the game meets a safety threshold, and based on determining that the classification of the game does not meet the safety threshold, identifying the game as having prohibited content.