Client Overview
A leading AI-driven platform sought to improve its content moderation system by training an AI tool to effectively block profane words and inappropriate prompts. The primary objective was to create a safe and controlled environment for users, especially those under 18, by ensuring that offensive language and explicit queries were automatically detected and filtered out.
Project Scope and Challenges
The primary goal of this project was to enhance the AI tool’s ability to recognize and block profane words and inappropriate prompts from its platform. This was achieved by curating and generating a dataset containing explicit and non-explicit profane words and question-based prompts. The annotated dataset would help train the AI model to differentiate between acceptable and offensive content, thereby preventing harmful material from being processed or displayed to users.
Challenges and Requirements
The project required an extensive understanding of profane language across different contexts and variations. Annotators had to analyze and validate explicit words from a predefined profane word sheet and identify any missing words to expand the dataset. Additionally, all profane prompts had to be question-based and structured as inquiries directed toward an AI tool. Ensuring uniqueness in prompts, avoiding duplication, and maintaining grammatical accuracy were essential requirements. Another key challenge was to balance the sensitivity of content moderation while ensuring the AI tool could effectively distinguish between profane and contextually acceptable prompts.
Implementation Approach
The project followed a structured annotation process. Annotators first reviewed a predefined list of profane words in their working language, marking any ambiguous terms and adding new explicit words that were not included in the original dataset. They then created question-based prompts using profane language while ensuring diversity in phrasing and structure. If a prompt contained an explicit profane word, it was noted in the designated column; otherwise, the prompt was classified based on its potential to generate profane responses. Daily work was meticulously recorded in the 'Worksheet - Daily Record' to ensure accountability and track progress. A minimum of 100 prompts per day was required to maintain consistency and efficiency across the project timeline.
Results and Impact
The project successfully enriched the AI tool’s ability to detect and block inappropriate prompts with high accuracy. The dataset provided a robust foundation for training the AI model, leading to improved filtering capabilities and a safer user experience. The structured annotation and rigorous quality checks ensured that the AI system could effectively distinguish between different types of profane content. The implementation of strict guidelines and daily tracking contributed to the project’s efficiency, ensuring that all team members adhered to quality standards and met their daily targets.
Conclusion
This initiative played a crucial role in strengthening AI-driven content moderation for online platforms. By systematically training the AI model with real-world profane prompts, the project enhanced the platform’s ability to protect users from harmful content. The collaboration between annotators and AI developers resulted in a refined, adaptive moderation system, reinforcing ethical AI practices and fostering a safer digital environment.
