Trust & Governance
Every model we deploy is bounded by human judgement, documented for explainability, and audited for fairness. This page describes how we build, govern, and continuously improve our AI-native talent engine.
Model governance
Every model is trained on validated datasets, benchmarked against historical outcomes, and tested for robustness before it reaches any client environment. We do not use production candidate data for training without explicit consent and contractual basis.
Before deployment, models undergo differential fairness testing across demographic proxies. Any model exhibiting disparate impact above a predefined threshold is sent back for retraining or architectural review.
Model weights, training configurations, and inference logs are stored in isolated environments with role-based access. Inference is logged for every recommendation, creating a complete audit trail from input to output.
Deployed models are monitored for concept drift, performance decay, and emerging bias signals. If a model's behaviour deviates from its baseline, it is automatically flagged for human review and can be rolled back within minutes.
Human-in-the-loop
Every shortlist is validated by a panel with varied backgrounds and expertise. This prevents single-reviewer bias and ensures cultural and functional fit is assessed from multiple perspectives.
Human reviewers can override any algorithmic recommendation with a single click. The system captures the reason for override, feeding it back into model improvement — but never overriding the human's authority.
Every decision — human or algorithmic — is logged with its justification, reviewer identity, and timestamp. This creates a defensible record for compliance, litigation, and internal review.
Explainability
We do not ship black-box predictions. Every match, score, and ranking is accompanied by a structured, interpretable justification designed to satisfy right-to-explanation expectations.
Each recommendation arrives with a logical breakdown of the factors that influenced the score — skill adjacency, behavioural fit, predicted retention, and comparative benchmarking against high-performing incumbents.
Where possible, our explainability layer surfaces what would need to change for a candidate to receive a different score. This turns opaque ranking into actionable feedback for candidates and recruiters alike.
Our justification format is designed to comply with emerging right-to-explanation regulations. Outputs are produced in human-readable prose and machine-structured JSON for downstream compliance tooling.
In video evaluation, we analyse structured language and role-specific rubric responses only. We explicitly avoid facial recognition, emotion detection, and biometric inference — all known sources of bias and legal exposure.
Accountability
This page is maintained by Acquire Talent Global to describe our AI governance, explainability, and human-in-the-loop practices. It is not an independent certification or legal opinion. For specific compliance questions, contact our governance team.
Our governance team is available for procurement security reviews, vendor assessments, and custom audit arrangements.