README.md

metadata

title: AI Recruiting Agent
emoji: 🤖
colorFrom: indigo
colorTo: blue
sdk: gradio
python_version: '3.10'
app_file: app.py
fullWidth: true
header: default
short_description: Bias-aware RAG AI recruiting agent with hallucination checks
suggested_hardware: cpu-upgrade
pinned: true
tags:
  - gradio
  - langchain
  - chroma
  - rag
  - recruiting
  - hr-tech
  - responsible-ai
  - fairness
  - bias-mitigation
  - llm
  - vector-database
  - ai-agent
  - nlp
  - demo
  - explainable-ai
  - enterprise-ai
models:
  - openai/gpt-oss-120b
thumbnail: >-
  https://huggingface.co/spaces/19arjun89/AI_Recruiting_Agent/raw/main/thumbnail.png
disable_embedding: false
startup_duration_timeout: 45m

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🧠 AI Recruiting Agent

A bias-aware, verification-driven recruiting assistant built with Gradio, LangChain, and vector search.
This Space is designed as a decision-support tool to help recruiters assess candidate fit and generate outreach emails — while embedding safeguards for fairness and transparency.

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🚀 What This Space Does

1) Candidate Assessment (Recruiter View)

• Upload company culture documents
• Upload resumes in bulk
• Paste a job description
• The system evaluates each candidate across:
  • Technical skills match
  • Culture fit
  • A final hiring recommendation
  • Claim verification against source inputs
  • A structured bias audit

2) Cold Email Generator (Candidate View)

• Upload a single resume
• Paste a job description
• Generates a tailored professional cold email for outreach

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🛡️ Responsible AI Safeguards

This prototype embeds multiple layers of protection against hallucination and bias.

🔹 Input Anonymization

Resumes are sanitized before embedding and analysis:

• Emails, phone numbers, URLs, addresses, and explicit demographic fields are redacted
• Likely name headers are masked

This reduces demographic leakage into both vector search and LLM reasoning.

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🔹 Fact Verification

All skills and culture analyses are checked against:

• Resume content
• Job description
• Culture documents

Unsupported claims are flagged and can trigger a self-correction routine.

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🔹 Bias Audit Chain

For each candidate, the assistant runs a dedicated bias audit prompt that inspects:

• Over-reliance on education pedigree or past employers
• Penalization of nontraditional career paths
• Subjective or exclusionary language in cultural fit
• Reasoning not grounded in the job description or culture docs

The output includes:

• Bias Indicators
• A Transparency Note for recruiter review

These signals do not disqualify candidates automatically — they flag where human judgment is critical.

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🧪 Try It Out

1. Upload sample culture documents (PDF or text)
2. Upload resumes (PDF or text)
3. Paste a job description
4. Click Analyze Candidates

Or:

• Upload a single resume
• Paste a job description
• Click Generate Cold Email

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⚠️ Human-in-the-Loop Disclaimer

This tool is intended as decision support only.
It does not replace recruiter judgment, legal review, or organizational hiring policies.

Final hiring decisions must always be made by humans.

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🧱 Technical Stack

• Gradio for UI
• LangChain for LLM orchestration
• Chroma for vector storage
• ChatGroq for LLM inference
• Hugging Face Embeddings for semantic search

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📣 Feedback Welcome

This is an early prototype. I’d love feedback on:

• Bias mitigation approaches
• Evaluation transparency
• UX improvements
• Failure modes or edge cases
• Responsible AI design patterns

Please share feedback via the Community tab on this Space.

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Built by Arjun Singh