19arjun89/ai_recruiting_agent_usage
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nisha1321/talentiq-recruiter-suite:Great Work & Related Space
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Great Work & Related Space
#1 opened 2 days ago
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19arjun89
Great Web Page + Related Space
#1 opened 2 days ago
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Great Work & Related Space
#1 opened 3 days ago
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19arjun89
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3 days ago
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3374
Update: Making My AI Recruiting Assistant More Deterministic, Auditable, and Bias-Aware
Hi everyone — I wanted to share a progress update on my AI recruiting assistant and some recent changes focused on reliability and transparency.
The goal of this project is to build a decision-support tool for recruiters that doesn’t just “sound confident,” but can actually explain why it produces a given recommendation.
Link: 19arjun89/AI_Recruiting_Agent
Over the last few iterations, I’ve focused on three areas:
1) Deterministic Verification of Job Requirements (Skills)
Previously, required skills were extracted by an LLM from the job description. While this worked well, it still relied heavily on model behavior.
I’ve now added a verification layer that:
Requires every “required” skill to be backed by a verbatim quote from the job description
This means hallucinated skills are explicitly detected and removed before scoring.
The system now shows:
What the model extracted
What was verified
What was dropped
Why it was dropped
This makes the requirements pipeline auditable instead of opaque.
2) Evidence-Based and Weighted Culture Verification
Culture matching is usually where AI systems become vague or subjective.
I’ve reworked this part so that:
Culture attributes are framed as observable, job-performance-related behaviors (e.g., audit readiness, operational reliability, security rigor)
Each matched attribute must include verbatim resume evidence
Matches are classified as:
Direct evidence (full weight)
Inferred evidence (partial weight)
Scoring is now weighted:
Direct = 1.0
Inferred = 0.5
This prevents “vibe-based” culture scoring and makes the math transparent.
The output now shows:
The weights used
Which attributes were supported directly vs inferred
Which attributes were missing
3) Improved Bias Audit Prompt
I’ve also upgraded the bias audit prompt to be more structured and actionable.
Hi everyone — I wanted to share a progress update on my AI recruiting assistant and some recent changes focused on reliability and transparency.
The goal of this project is to build a decision-support tool for recruiters that doesn’t just “sound confident,” but can actually explain why it produces a given recommendation.
Link: 19arjun89/AI_Recruiting_Agent
Over the last few iterations, I’ve focused on three areas:
1) Deterministic Verification of Job Requirements (Skills)
Previously, required skills were extracted by an LLM from the job description. While this worked well, it still relied heavily on model behavior.
I’ve now added a verification layer that:
Requires every “required” skill to be backed by a verbatim quote from the job description
This means hallucinated skills are explicitly detected and removed before scoring.
The system now shows:
What the model extracted
What was verified
What was dropped
Why it was dropped
This makes the requirements pipeline auditable instead of opaque.
2) Evidence-Based and Weighted Culture Verification
Culture matching is usually where AI systems become vague or subjective.
I’ve reworked this part so that:
Culture attributes are framed as observable, job-performance-related behaviors (e.g., audit readiness, operational reliability, security rigor)
Each matched attribute must include verbatim resume evidence
Matches are classified as:
Direct evidence (full weight)
Inferred evidence (partial weight)
Scoring is now weighted:
Direct = 1.0
Inferred = 0.5
This prevents “vibe-based” culture scoring and makes the math transparent.
The output now shows:
The weights used
Which attributes were supported directly vs inferred
Which attributes were missing
3) Improved Bias Audit Prompt
I’ve also upgraded the bias audit prompt to be more structured and actionable.
posted
an
update
8 days ago
Post
127
🔄 Update: One-Click Demo Mode Added to AI Recruiting Agent
I’ve added a one-click demo mode to the Candidate Assessment tab of my Hugging Face Space: AI Recruiting Agent.
👉 19arjun89/AI_Recruiting_Agent
🚀 What’s new
You can now experience the full recruiting workflow end-to-end with a single click, without uploading any files.
The One-Click Demo automatically:
Clears any existing vector data
Loads sample company culture documentation
Loads a sample candidate resume
Loads a sample job description
Runs the full candidate evaluation pipeline
All using sample documents included directly in the Space repo.
🧪 What the demo showcases
Skills match analysis
Culture fit evaluation
Final hiring recommendation
Hallucination verification & self-correction
Bias audit (pedigree bias, subjective language, non-traditional paths)
This makes it much easier for first-time users, reviewers, and evaluators to understand what the system does and how responsible AI safeguards are applied—without setup friction.
🛡️ Responsible by design
The demo uses the same safeguards as real usage:
Resume anonymization (no PII in embeddings or prompts)
Fact verification against source documents
Bias audit chain surfaced to the user
Human-in-the-loop framing (decision support, not automation)
If you’re exploring AI-assisted hiring workflows or interested in transparent, bias-aware evaluation pipelines, I’d love feedback.
I’ve added a one-click demo mode to the Candidate Assessment tab of my Hugging Face Space: AI Recruiting Agent.
👉 19arjun89/AI_Recruiting_Agent
🚀 What’s new
You can now experience the full recruiting workflow end-to-end with a single click, without uploading any files.
The One-Click Demo automatically:
Clears any existing vector data
Loads sample company culture documentation
Loads a sample candidate resume
Loads a sample job description
Runs the full candidate evaluation pipeline
All using sample documents included directly in the Space repo.
🧪 What the demo showcases
Skills match analysis
Culture fit evaluation
Final hiring recommendation
Hallucination verification & self-correction
Bias audit (pedigree bias, subjective language, non-traditional paths)
This makes it much easier for first-time users, reviewers, and evaluators to understand what the system does and how responsible AI safeguards are applied—without setup friction.
🛡️ Responsible by design
The demo uses the same safeguards as real usage:
Resume anonymization (no PII in embeddings or prompts)
Fact verification against source documents
Bias audit chain surfaced to the user
Human-in-the-loop framing (decision support, not automation)
If you’re exploring AI-assisted hiring workflows or interested in transparent, bias-aware evaluation pipelines, I’d love feedback.
Roadmap + Weekly Updates for AI Recruiting Agent (Feedback Welcome)
1
#1 opened 13 days ago
by
19arjun89
replied to
their
post
12 days ago
Some technical details for those interested:
- Resume and culture docs are chunked into Chroma vector stores
- Candidate scoring runs in two independent chains (skills + culture)
- Each analysis is fact-checked against source inputs
- Unverified claims can trigger a self-correction routine
- A separate bias audit prompt triangulates across skills, culture, and the final recommendation
Everything is designed as decision support — not an automated hiring gate.
replied to
their
post
12 days ago
🙏 Feedback Welcome
I’d love input on:
• Fairness pitfalls or bias signals I may have missed
• Edge cases in resume anonymization
• Verification or audit patterns you’ve seen work well
• Features that would make this more useful in real hiring workflows
Happy to answer technical questions or iterate based on feedback.
published
a
dataset
13 days ago
Related Space and Feedback Sharing
#1 opened 13 days ago
by
19arjun89
General Feedback and Related Space
#1 opened 13 days ago
by
19arjun89
Great Work and Related Space
#3 opened 13 days ago
by
19arjun89
liked
a
Space
17 days ago
posted
an
update
19 days ago
Post
298
🧠Introducing AI Recruiting Agent — A Responsible Hiring Assistant in Hugging Face Spaces
Hi everyone! đź‘‹
I’m excited to share a new Hugging Face Space I’ve built: AI Recruiting Agent — a Gradio-powered assistant designed to help automate candidate evaluation and cold email drafting while embedding responsible safeguards into the workflow.
👉 Check it out here:
19arjun89/AI_Recruiting_Agent
🚀 What It Does
This Space helps recruiters and talent teams do two main things:
1. Assess Candidate Fit
• Upload a batch of resumes and company culture documents
• Paste in a job description
• The assistant evaluates each candidate across skills match, culture fit, and produces an actionable hiring recommendation
2. Generate Candidate-Specific Emails
• Upload a single resume + job description
• It produces a professional cold outreach email tailored to that candidate
đź‘Ą Who This Is For
This Space is designed for:
• Recruiters and talent teams experimenting with AI-assisted screening
• ML engineers building responsible RAG systems
• Researchers exploring bias mitigation and LLM verification
• Product teams prototyping decision-support tools
🛡️ Built-In Bias Mitigation & Verification
Because AI recruitment comes with real risks, I’ve built several safeguards into the design:
🔹 Input Anonymization
Resumes are sanitized of email, phone numbers, URLs, physical addresses, and other personal identifiers.
🔹 Fact Verification
Every analysis (skills & culture) is verified against source inputs using a custom fact-checking prompt. Claims that aren’t supported are flagged and can trigger a self-correction routine.
🔹 Bias Audit Chain
bias audit prompt that reviews:
• Over-reliance on pedigree or subjective language
• Penalization of nontraditional career paths
• Unsupported reasoning outside the job description or culture docs
These checks aren’t meant to replace human judgment, but to make AI support more transparent and fair.
Hi everyone! đź‘‹
I’m excited to share a new Hugging Face Space I’ve built: AI Recruiting Agent — a Gradio-powered assistant designed to help automate candidate evaluation and cold email drafting while embedding responsible safeguards into the workflow.
👉 Check it out here:
19arjun89/AI_Recruiting_Agent
🚀 What It Does
This Space helps recruiters and talent teams do two main things:
1. Assess Candidate Fit
• Upload a batch of resumes and company culture documents
• Paste in a job description
• The assistant evaluates each candidate across skills match, culture fit, and produces an actionable hiring recommendation
2. Generate Candidate-Specific Emails
• Upload a single resume + job description
• It produces a professional cold outreach email tailored to that candidate
đź‘Ą Who This Is For
This Space is designed for:
• Recruiters and talent teams experimenting with AI-assisted screening
• ML engineers building responsible RAG systems
• Researchers exploring bias mitigation and LLM verification
• Product teams prototyping decision-support tools
🛡️ Built-In Bias Mitigation & Verification
Because AI recruitment comes with real risks, I’ve built several safeguards into the design:
🔹 Input Anonymization
Resumes are sanitized of email, phone numbers, URLs, physical addresses, and other personal identifiers.
🔹 Fact Verification
Every analysis (skills & culture) is verified against source inputs using a custom fact-checking prompt. Claims that aren’t supported are flagged and can trigger a self-correction routine.
🔹 Bias Audit Chain
bias audit prompt that reviews:
• Over-reliance on pedigree or subjective language
• Penalization of nontraditional career paths
• Unsupported reasoning outside the job description or culture docs
These checks aren’t meant to replace human judgment, but to make AI support more transparent and fair.
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published
a
Space
12 months ago
updated
a
Space
12 months ago
published
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Space
12 months ago