Enterprises increasingly rely on AI for hiring, lending, and healthcare decisions, but most have no reliable way to detect bias or prove fairness, exposing them to regulatory penalties, reputational damage, and customer distrust — a 2022 WEF study found 43% of organizations faced issues from biased AI.

smartData built the AI Bias Platform for Calvin, a single system that replaces scattered point tools with one connected view of every AI model an organization runs. It scores each model against Fairness, Explainability, Transparency, Usability, and Quality (FETUQ), watches for bias drift in real time, and lets teams simulate a model's real-world impact on different demographic groups before it ever reaches production — turning bias detection from an after-the-fact audit into a preventive practice, backed by expert consultation and ongoing subscription-based monitoring.
 

Features

• AI Portfolio & Model Lifecycle Tracker — a single registry and audit trail for every AI model in use
• FETUQ Scoring Engine — one comparable fairness score (Fairness, Explainability, Transparency, Usability, Quality) for every model
• Real-Time Bias Monitoring — live drift alerts and risk heatmaps that catch bias as it emerges, not after
• Compliance & Governance Hub — maps every model against live regulations with board-level review and sign-off
• What-If & Harm Simulation — predicts a model's real-world impact on affected groups before it goes live

Technical Challenges

• A fairness score needed to mean the same thing whether it came from a compliance audit, a real-time monitor, or a hypothetical what-if simulation — otherwise scores across the platform wouldn't be comparable. : Built FETUQ as one shared scoring engine that every module — Audit Center, Real-Time Monitoring, Model Comparison, What-If Simulator — calls into, so a given score means the same thing no matter where in the platform it appears.
• Bias was traditionally caught only after a model was already deployed and had already affected real people, leaving no way to intervene beforehand. : Added What-If and Harm Simulation modules that replay a model's decisions against synthetic personas and demographic groups pre-deployment, surfacing disparate impact and harm scores before the model goes live.
• Regulations like the EU AI Act evolve independently of a client's models, so compliance can't be treated as a one-time checkbox. : Built a Compliance Hub that maps every model against live regulation packs, tracks compliant / non-compliant / under-review status per model, and surfaces a remediation checklist the moment a rule or a model changes.
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