? Project Title: AI-Powered Content Moderation Platform for Social Networks

?? Delivery Lead: Dimitris Souris ?? Framework: Scrum

? Project Overview

The AI-Powered Content Moderation Platform is designed to automatically detect and manage inappropriate content (such as hate speech, misinformation, or graphic violence) on social networks. Using advanced machine learning techniques, including NLP (Natural Language Processing) and computer vision, the platform will improve moderation efficiency, reduce human intervention, and ensure user safety. The system will scale to handle millions of daily content submissions across various platforms and formats (text, images, video).

? Objectives

• Automate content moderation tasks, minimizing manual intervention by 70%.
• Increase the detection rate of inappropriate content by 95% within the first year of implementation.
• Reduce content moderation operational costs by 65% over the next three years.
• Enhance the platform’s reputation by ensuring user safety and minimizing the spread of harmful content.

? Timeline

• Project Initiation: Weeks 1-2
• Feasibility Study & Market Analysis: Weeks 3-5
• AI Model Development: Weeks 6-16
• Data Pipeline Integration: Weeks 17-22
• Front-End & Dashboard Development: Weeks 23-28
• Testing & Evaluation: Weeks 29-32
• Deployment & Monitoring: Weeks 33-36

? Scope Statement

The scope includes developing and deploying AI models that can analyze text, images, and videos in real-time. The platform will integrate with existing social media APIs for content ingestion and processing. It will provide both automated and human-assisted moderation, ensuring high accuracy in flagging harmful content. The project will involve three main teams working on AI development, data integration, and user interface. Using the Scrum methodology, each team will operate within two-week sprints, delivering iterative improvements and maintaining continuous feedback loops.

? Feasibility Study

?? Economic Feasibility The total estimated budget for this project is approximately €1.2 million. Key economic factors include the potential savings from automating content moderation, as well as the increase in platform trust, which can lead to higher user engagement and ad revenue.

• Labor Costs: This will form the largest part of the budget, covering the development, testing, and deployment phases. It is estimated that €350,000 will be required for labor across all teams, including AI specialists, data engineers, and front-end developers.
• AI Model Development & Training: Building and training advanced AI models (including text classification, image recognition, and video analysis) will cost an estimated €250,000. This includes data sourcing, labeling, and the computing power necessary for large-scale training.
• Cloud Infrastructure: The project will be hosted on AWS, with a cost breakdown including S3 for data storage, EC2 instances for computation, and Kubernetes for container orchestration. The total estimated cost for cloud services is €200,000, accounting for a scalable infrastructure that can handle millions of content submissions daily.
• Testing and Quality Assurance (QA): Testing will be a crucial phase, ensuring that the AI models meet the required accuracy and performance standards. A budget of €100,000 is allocated for thorough testing, including both automated and manual testing processes.
• Marketing and Sales: Once the platform is ready, it will require marketing to potential social media companies. A budget of €150,000 is allocated for promotional activities, sales strategies, and building partnerships with social platforms.
• Miscellaneous Costs: Other expenses include licenses for third-party tools, data acquisition, and regulatory compliance, estimated at €150,000.

? Budget Breakdown

• Labor Costs: This includes the salaries of the development team, AI specialists, data engineers, front-end developers, and project managers. Amount: €350,000
• AI Model Development & Training: Developing and training the AI models (text, image, and video moderation) will require substantial resources, including computing power and labeled datasets. Amount: €250,000
• Cloud Infrastructure: The platform will be hosted on AWS, using services such as EC2 for computation, S3 for data storage, and Kubernetes for scaling. Amount: €200,000
• Testing and Quality Assurance (QA): A robust testing phase will be essential to ensure that the AI models meet performance and accuracy benchmarks. Amount: €100,000
• Marketing and Sales: Once the platform is complete, a marketing strategy will be executed to attract clients such as social media platforms. Amount: €150,000
• Miscellaneous Costs: This includes additional licensing fees, data acquisition, and legal fees for regulatory compliance, such as GDPR. Amount: €150,000

Total Estimated Budget: €1.2 million

? Forecasting Metrics for Project Management

To ensure effective project management, key metrics will be tracked throughout the project lifecycle. These include:

• Sprint Velocity: This will track the number of story points completed by each team per sprint, helping to forecast the project’s progress and determine whether the project is on track.
• Burn-Down Chart: A daily burn-down chart will provide a visual representation of the remaining tasks, ensuring transparency and the ability to adjust the project plan as necessary.
• Cost Performance Index (CPI): This will monitor the relationship between actual costs and planned costs, ensuring that the project stays within budget.
• Schedule Performance Index (SPI): The SPI will track the project’s adherence to the planned timeline, comparing actual work completed to the planned schedule.
• Resource Utilization: This metric will monitor how effectively resources, such as developers and cloud infrastructure, are being utilized, ensuring there is no overallocation or underutilization.

? Development Phases

1. ?? Feasibility Study & Market Analysis (Weeks 3-5)A comprehensive analysis will be conducted to determine the technical and economic feasibility of the project. This phase will also include a detailed competitor analysis (such as Facebook and YouTube) to ensure the platform's differentiation and effectiveness.
2. ?? AI Model Development (Weeks 6-16)The core AI models for text, image, and video analysis will be developed during this phase. Key technologies include TensorFlow, PyTorch, and OpenCV, with data sourced from publicly available datasets, as well as proprietary data from social media platforms.
3. ?? Data Pipeline Integration (Weeks 17-22)Real-time data ingestion will be enabled through Apache Kafka, allowing the platform to handle up to 5 million content submissions per day. A scalable data pipeline will be built using Kafka Streams and Apache Flink for real-time processing.
4. ?? Front-End & Dashboard Development (Weeks 23-28)The front-end will focus on providing human moderators with an intuitive dashboard to review flagged content. Built using React and Node.js, the dashboard will display AI-detected content with detailed analysis and decision-making tools.
5. ?? Testing & Evaluation (Weeks 29-32)A rigorous testing phase will involve performance evaluation of the AI models, ensuring at least 95% accuracy in detecting inappropriate content. Both unit testing for the backend and user acceptance testing (UAT) for the dashboard will be conducted.
6. ?? Deployment & Monitoring (Weeks 33-36)The platform will be deployed on AWS using Kubernetes for container orchestration. Monitoring tools like Grafana and Prometheus will be implemented to ensure real-time system health monitoring, including latency, throughput, and error rates.

? Sprint Planning for the AI-Powered Content Moderation Platform

Sprint Duration: 2 weeks

Total Sprints: 18 Teams:

• Team 1: AI model development (NLP, image, video analysis)
• Team 2: Data pipeline and integration
• Team 3: Front-end/dashboard development

Sprint Breakdown

Sprint 1-2 (Weeks 1-4): Project Setup & Feasibility Study

?? Tasks:

• Team formation
• Initial feasibility studies (economic and technical)
• Setting up the project environment (Jira, Confluence)
• Defining project scope, initial user stories, and acceptance criteria

?? Outcomes:

• Feasibility report
• Project backlog creation

Sprint 3-5 (Weeks 5-10): AI Model Prototyping

?? Tasks:

• Begin prototyping AI models for text analysis (NLP)
• Initial research and development for image classification
• Collect and label training data
• Create foundational architecture for AI model integration

?? Outcomes:

• First prototype of NLP model
• Initial architecture and pipeline design

Sprint 6-8 (Weeks 11-16): Data Pipeline & Real-Time Streaming

?? Tasks:

• Set up data ingestion using Apache Kafka
• Develop pipelines for real-time content moderation
• Integrate AI models into the pipeline for text analysis
• Start training the image moderation models

?? Outcomes:

• Working pipeline for text moderation
• Basic version of image moderation integrated into the pipeline

Sprint 9-11 (Weeks 17-22): Dashboard Development

?? Tasks:

• Start front-end development for the moderation dashboard using React
• Create user interface for manual content review and AI feedback display
• Implement admin tools for custom content flagging
• Test front-end integration with AI pipeline

?? Outcomes:

• Prototype of the dashboard
• Front-end and back-end integration

Sprint 12-14 (Weeks 23-28): Testing & AI Model Refinement

?? Tasks:

• Begin QA testing for the platform, especially AI models
• Optimize and fine-tune the AI models for better accuracy and lower latency
• Perform load testing on data pipelines
• Implement feedback loops for continuous learning in AI models

?? Outcomes:

• Improved AI accuracy and performance
• Full integration of real-time pipeline with dashboard

Sprint 15-17 (Weeks 29-34): Deployment & Monitoring Setup

?? Tasks:

• Deploy the platform on AWS using Kubernetes for scaling
• Set up monitoring with Grafana and Prometheus
• Train users on dashboard functionality
• Final system checks and testing

?? Outcomes:

• Fully deployed system
• Monitoring system live for real-time performance tracking

Sprint 18 (Weeks 35-36): Go Live and Final Review

?? Tasks:

• Go live with the AI-powered moderation system
• Conduct final retrospective
• Ensure all KPIs (Model Accuracy, Latency, and Moderation Efficiency) meet project targets

?? Outcomes:

• Successful deployment
• Final review and handoff

? KPIs for Monitoring Progress

• Model Accuracy: The key goal is to achieve 95% accuracy in detecting harmful or inappropriate content, reducing false positives and negatives.
• System Latency: The platform aims for sub-100ms latency for real-time content moderation to ensure a seamless user experience on social platforms.
• Moderation Efficiency: Manual intervention should be reduced by at least 80%, with flagged content requiring human review only in 20% of cases.
• Customer Satisfaction: A decrease in content-related complaints by 40% within six months of implementation will be monitored to gauge the platform’s impact on user experience.

? Risk Management

• AI Model Bias: There is a risk of biased decisions in AI models, especially with content involving sensitive topics. This will be mitigated by using diverse and representative training data, as well as ongoing monitoring and retraining of the models.
• Infrastructure Scaling: The platform must be able to scale as content volume increases. Kubernetes will be used for auto-scaling, ensuring the platform can handle peak traffic without performance degradation.
• Regulatory Compliance: Compliance with global regulations like GDPR is critical, especially regarding user data privacy. Legal teams will work closely with developers to ensure all data handling is compliant with local and international laws.
• Performance Bottlenecks: Real-time content moderation requires low latency and high throughput. Continuous monitoring and the use of performance optimization tools like Prometheus will help identify and resolve bottlenecks quickly.

? Architecture

The architecture follows a microservices model, ensuring scalability, resilience, and independent development of each component. Core components include:

• ?? Content Ingestion: APIs will gather content from social media platforms, processing it in real-time using Kafka.
• ?? AI Moderation: The system will deploy AI models for analyzing text (NLP), images (computer vision), and videos (video processing) using TensorFlow and PyTorch.
• ?? Data Storage: Large volumes of content will be stored in AWS S3, with metadata and flagged content stored in PostgreSQL and Redis for quick access.
• ?? Moderation Dashboard: The front-end dashboard will be built in React, providing moderators with a visual interface to review flagged content. Moderators will see detailed explanations of why the content was flagged, alongside options for override or escalation.

? Data Flow

1. Content Submission: User-generated content is submitted via API from various social media platforms.
2. Data Streaming: Apache Kafka handles real-time data streaming, ensuring smooth ingestion and processing.
3. AI Moderation: Content is processed by AI models that detect harmful or inappropriate elements, flagging content that requires human review.
4. Flagged Content Storage: Redis will store flagged content temporarily for quick access, while PostgreSQL will handle metadata.
5. Manual Review: Moderators review flagged content through the dashboard, deciding whether to approve or escalate.

? Tech Stack

• AI Frameworks: TensorFlow, PyTorch
• Cloud Infrastructure: AWS (S3, EC2, Kubernetes)
• Data Streaming: Apache Kafka
• Data Storage: PostgreSQL, Redis
• Frontend: React, Node.js
• Monitoring: Grafana, Prometheus