---
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 **transparent, verification-driven, and bias-aware** recruiting assistant built with Gradio, LangChain, and vector search.

This project is designed as a **decision-support system** to help recruiters evaluate candidates and generate outreach — while embedding safeguards for **fairness, auditability, and explainability**.

> 📘 For a detailed technical and governance walkthrough, see:  
> **[`Guidebook.md`](./Guidebook.md)**

---

## 🚀 What This Tool Does

### 1) Candidate Assessment (Recruiter View)

Recruiters can:

- Upload company culture documents (core values, principles, etc.)
- Upload resumes in bulk  
- Paste a job description  

For each candidate, the system produces:

- Deterministic technical skills score  
- Evidence-backed culture fit analysis  
- Policy-constrained hiring recommendation  
- Automated fact verification  
- Structured bias audit  

All outputs are designed to be **inspectable and reviewable** by humans.

---

### 2) Cold Email Generator (Candidate View)

Candidates can:

- Upload a resume  
- Paste a job description  

The system generates a tailored professional outreach email.

This feature is independent of the assessment pipeline.

---

## ✨ Key Differentiators

Most recruiting tools focus on automation and ranking.

This system prioritizes **trust and governance**:

- ✅ Evidence-first scoring  
- ✅ Self-verifying analyses  
- ✅ Deterministic policies  
- ✅ Bias and fairness auditing  
- ✅ Human-in-the-loop controls  

It is built to support **high-stakes hiring environments**, not replace recruiters.

---

## 🛡️ Responsible AI Safeguards

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

### 🔹 Input Anonymization

Before indexing:

- Emails, phone numbers, URLs, addresses, and demographics are removed  
- Likely name headers are masked  

This reduces demographic leakage into retrieval and reasoning.

---

### 🔹 Deterministic Verification

All required skills are:

- Extracted by LLM  
- Verified against the job description  
- Classified as:
  - Quote-verified  
  - Name-only  
  - Dropped (unsupported)  

Only quote-verified skills are scored.

---

### 🔹 Evidence-Based Fact Checking

All claims in:

- Skills analysis  
- Culture analysis  

Are verified against:

- Resume text  
- Job description  
- Culture documents  

Unsupported claims are flagged and may trigger automatic revision.

---

### 🔹 Bias Audit Chain

Each candidate run includes a bias audit that inspects:

- Pedigree and prestige bias  
- Subjective or exclusionary language  
- Inconsistent standards  
- Non-job-related reasoning  
- Overconfident claims  

Outputs include:

- Bias indicators  
- Recruiter guidance  

These signals never auto-disqualify candidates.

They highlight where human review is essential.

---

## 📘 System Design & Governance

This project is documented in detail in:

➡️ **[`guidebook.md`](./guidebook.md)**

The guidebook covers:

- End-to-end system flow  
- Deterministic vs LLM boundaries  
- Scoring rules  
- Verification logic  
- Implied competency handling  
- Self-correction policies  
- Audit artifacts  
- Model governance  

Reviewers, auditors, and contributors are encouraged to consult it.

---

## 🧪 Try It Out

### Candidate Assessment

1. Upload culture documents (PDF/DOCX/Text)  
2. Upload resumes (PDF/DOCX/Text)  
3. Paste job description  
4. Click **Analyze Candidates**

### Cold Email Generator

1. Upload resume  
2. Paste job description  
3. Click **Generate Cold Email**

Sample documents are available in the repository.

---

## ⚠️ Human-in-the-Loop Disclaimer

This tool provides structured decision support.

It does **not** replace:

- Recruiter judgment  
- Legal review  
- Hiring policy compliance  

Final hiring decisions must always be made by humans.

---

## 🧱 Technical Stack

- Gradio — UI  
- LangChain — LLM orchestration  
- Chroma — Vector storage  
- ChatGroq — Inference  
- Hugging Face Embeddings — Semantic search  

---

## 📣 Feedback Welcome

This is an evolving research prototype.

Feedback is welcome on:

- Bias mitigation  
- Evaluation transparency  
- UX  
- Failure modes  
- Responsible AI patterns  

Please share feedback via the Community tab.

---

Built by **Arjun Singh**