HR technology
TalentFilter: explainable AI screening across three evaluation layers
I contributed to a candidate-screening platform that combined video analysis, adaptive technical assessment, and interview intelligence without turning an AI score into an unexplained hiring decision.
The system I joined
High-volume technical hiring created inconsistent CV reviews and expensive phone screens before interviewers had reliable evidence of a candidate's knowledge or communication.
The legal and product requirement was explicit: every automated score needed criterion-level reasoning that a recruiter could inspect rather than a single opaque ranking.
Where I contributed
I worked across the structured evaluation pipeline, audio and video quality controls, adaptive-exam model, and recruiter-facing intelligence brief.
The design treated model output as reviewable evidence. Scores, reasons, confidence, and source quality travelled together through the system.
Decision trail
Constraints became architecture.
Each decision below connects a concrete limitation to the engineering response and its practical effect.
- 01
Constraint
Free-form model explanations used inconsistent criterion names and could not support reliable audits.
Decision
Constrain every evaluation to a versioned JSON schema with an allowed criterion vocabulary, score, evidence, and confidence.
Impact
Reasoning outputs used the defined vocabulary correctly in 94% of validation cases and became comparable across roles.
- 02
Constraint
Fixed technical tests spent too many questions confirming what the system already knew about a candidate.
Decision
Use item-response theory to select each next question from the current ability estimate and recalibrate question parameters from production data.
Impact
Recalibrated question banks produced 18% lower score variance and targeted knowledge boundaries more precisely.
- 03
Constraint
Browser recording quality varied enough to distort transcription and delivery analysis.
Decision
Run a local pre-submission signal-quality check and carry transcription confidence into the final evaluation.
Impact
Candidates who rerecorded after a warning produced audio with 22% better speech-recognition error rates.
Outcome
What the work produced
- The highest-volume screening step required approximately 80% less recruiter effort.
- A 200-candidate pool could be processed overnight.
- Interviewers using the intelligence brief probed predicted knowledge gaps 67% more often.
- Every evaluation retained explicit reasoning and data-quality indicators for human review.
Technology
Explainability improved the product as well as the audit trail. Requiring evidence for every score exposed weak criteria early and gave interviewers something concrete to test.
Abrar Ahmed