AI-Powered Smart Talent Screening & Interviewing System

1. Challenge Overview

This challenge focuses on developing an AI-driven hiring assistant that automates resume screening, candidate shortlisting, and AI-powered video interviews. The system should analyze resumes, conduct structured AI interviews, and generate automated candidate evaluations to assist recruiters in making data-driven hiring decisions.

The AI should eliminate bias, improve candidate-job matching, and fast-track hiring processes while ensuring a fair and engaging experience for candidates.

2. Problem Statement

Develop an AI-powered hiring assistant that:

• Screens resumes & ranks candidates based on job descriptions, skills, and experience.
• Uses NLP to analyze resumes and match candidates to job roles efficiently.
• Conducts AI-driven video interviews, asking job-relevant questions based on the candidate’s profile.
• Uses speech-to-text AI to transcribe responses and analyze them for sentiment, confidence, and relevance.
• Implements computer vision (face analysis) to assess engagement, stress levels, and emotions during interviews.
• Generates AI-based interview reports with candidate strengths, weaknesses, and hiring scores.
• Bonus Features:
  • Bias detection & diversity compliance to ensure fair candidate selection.
  • AI-based coaching suggestions to help candidates improve responses in real-time.
  • Multi-language support for global hiring.

3. Technical Approach

Participants must implement:

1. Resume Screening & Candidate Ranking (NLP-based Matching System)
   • Develop an AI model that parses resumes to extract skills, qualifications, and job experience.
   • Use BERT, SpaCy, or GPT models for semantic matching between resumes & job descriptions.
   • Rank candidates based on relevance scores, considering experience, certifications, and technical skills.
   • Implement bias detection models to ensure fair selection.
2. AI-Driven Video Interviewing System
   • Build an AI interview bot that asks structured questions based on the job role and candidate profile.
   • Use speech-to-text NLP (OpenAI Whisper, Google Speech-to-Text) to transcribe and analyze responses.
   • Implement sentiment analysis & confidence scoring to evaluate tone, clarity, and assertiveness.
   • Use OpenCV, DeepFace, or MediaPipe for facial expression analysis to assess stress, engagement, and sincerity.
   • Generate AI-powered interview reports summarizing performance.
3. Data Storage & Dashboard for Recruiters
   • Store candidate profiles, interview scores, and analysis reports in a database (PostgreSQL, MongoDB).
   • Develop a React/Flask/Streamlit-based dashboard for recruiters to:
     • View shortlisted candidates ranked by AI.
     • Watch AI-analyzed interview recordings.
     • Compare multiple candidates based on objective AI scores.
4. Fairness & Optimization Features (Bonus Features)
   • Implement bias detection to ensure fair hiring practices.
   • Integrate multi-language support to conduct interviews globally.
   • Provide real-time coaching feedback for candidates to improve responses.

4. Data Sources

Participants may use the following data sources:

• Resume Datasets:
  • Kaggle Resume Dataset
  • Job Description & Resume Matching Dataset
• Speech & Video Analysis Data:
  • YouTube Interview Dataset
  • OpenAI Whisper for speech-to-text
• Facial Analysis Data:
  • DeepFace GitHub
  • MediaPipe Face Analysis
• Bias Detection Data:
  • AI Fairness & Bias Detection Toolkit

5. Evaluation Criteria

Submissions will be assessed based on:

• Candidate Matching Accuracy (40%) – AI’s ability to correctly rank resumes based on job descriptions.
• Interview Analysis Efficiency (20%) – Speech & sentiment analysis quality, and video emotion tracking.
• Recruiter Experience (20%) – Ease of reviewing AI-generated interview summaries.
• Fairness & Bias Handling (10%) – Ability to detect and mitigate AI biases in hiring decisions.
• Code Quality & Documentation (10%) – Well-structured, maintainable, and well-documented code.

6. Deliverables

Participants must submit:

• Source Code & README – Clearly structured and well-documented code.
• Working AI Hiring Assistant – Functional tool that screens resumes, conducts interviews, and evaluates candidates.
• Evaluation Report – AI’s accuracy in job-candidate matching and interview assessments.
• Demo Video (Optional) – Short video showcasing system functionalities.

7. Implementation Guidelines

• Train NLP models for resume parsing & job-role matching.
• Use sentiment analysis & speech-to-text models for interview assessment.
• Integrate facial recognition & emotion tracking for interview evaluation.
• Develop a recruiter-friendly dashboard for reviewing AI-analyzed candidates.
• Ensure AI fairness & bias detection in the hiring process.