"Design a comprehensive AI-powered recruitment screening system that accurately evaluates candidate skills and experience while ensuring fairness and efficiency in the hiring process. Provide detailed technical specifications, including examples of machine learning models and data structures suitable for this application.** Address the following components:** ## 1. **Candidate Data Collection**: Outline methods for gathering and preprocessing candidate data, including resume parsing and social media integration. ## 2. **Skill Assessment**: Describe machine learning models (e.g., natural language processing, decision trees) that can be used to assess candidate skills and experience. ## 3. **Fairness and Bias Mitigation**: Explain strategies for ensuring fairness and reducing bias in the screening process, including data normalization and feature selection techniques. ## 4. **Efficiency Optimization**: Discuss data structures (e.g., graphs, hash tables) and algorithms (e.g., sorting, searching) that can enhance the system's efficiency and scalability. ## 5. **System Evaluation**: Provide metrics and methods for evaluating the system's performance, including accuracy, precision, and recall. Ensure the technical responses are tailored to advanced users, incorporating relevant examples and algorithms."