Update: Making My AI Recruiting Assistant More Deterministic, Auditable, and Bias-Aware
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Update: Making My AI Recruiting Assistant More Deterministic, Auditable, and Bias-Aware
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Hi everyone — I wanted to share a progress update on my AI recruiting assistant and some recent changes focused on reliability and transparency.
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Hi everyone — I wanted to share a progress update on my AI recruiting assistant and some recent changes focused on reliability and transparency.
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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.
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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.
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Link: 
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Link: 
https://huggingface.co/spaces/19arjun89/AI_Recruiting_Agent
19arjun89/AI_Recruiting_Agent
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https://huggingface.co/spaces/19arjun89/AI_Recruiting_Agent
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Over the last few iterations, I’ve focused on three areas:
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Over the last few iterations, I’ve focused on three areas:
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1) Deterministic Verification of Job Requirements (Skills)
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1) Deterministic Verification of Job Requirements (Skills)
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Previously, required skills were extracted by an LLM from the job description. While this worked well, it still relied heavily on model behavior.
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Previously, required skills were extracted by an LLM from the job description. While this worked well, it still relied heavily on model behavior.
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I’ve now added a verification layer that:
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I’ve now added a verification layer that:
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Requires every “required” skill to be backed by a verbatim quote from the job description
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Requires every “required” skill to be backed by a verbatim quote from the job description
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This means hallucinated skills are explicitly detected and removed before scoring.
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This means hallucinated skills are explicitly detected and removed before scoring.
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The system now shows:
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The system now shows:
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What the model extracted
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What the model extracted
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What was verified
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What was verified
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What was dropped
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What was dropped
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Why it was dropped
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Why it was dropped
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This makes the requirements pipeline auditable instead of opaque.
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This makes the requirements pipeline auditable instead of opaque.
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2) Evidence-Based and Weighted Culture Verification
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2) Evidence-Based and Weighted Culture Verification
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Culture matching is usually where AI systems become vague or subjective.
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Culture matching is usually where AI systems become vague or subjective.
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I’ve reworked this part so that:
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I’ve reworked this part so that:
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Culture attributes are framed as observable, job-performance-related behaviors (e.g., audit readiness, operational reliability, security rigor)
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Culture attributes are framed as observable, job-performance-related behaviors (e.g., audit readiness, operational reliability, security rigor)
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Each matched attribute must include verbatim resume evidence
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Each matched attribute must include verbatim resume evidence
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Matches are classified as:
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Matches are classified as:
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Direct evidence (full weight)
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Direct evidence (full weight)
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Inferred evidence (partial weight)
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Inferred evidence (partial weight)
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Scoring is now weighted:
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Scoring is now weighted:
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Direct = 1.0
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Direct = 1.0
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Inferred = 0.5
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Inferred = 0.5
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This prevents “vibe-based” culture scoring and makes the math transparent.
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This prevents “vibe-based” culture scoring and makes the math transparent.
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The output now shows:
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The output now shows:
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The weights used
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The weights used
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Which attributes were supported directly vs inferred
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Which attributes were supported directly vs inferred
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Which attributes were missing
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Which attributes were missing
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3) Improved Bias Audit Prompt
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3) Improved Bias Audit Prompt
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I’ve also upgraded the bias audit prompt to be more structured and actionable.
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I’ve also upgraded the bias audit prompt to be more structured and actionable.
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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: https://huggingface.co/spaces/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.

🚀
unmodeled-tyler John6666 loleg fdwork