AI Bias Audit Framework for SG

SG-specific obligations covered

The output is anchored on the regulations that apply to AI deployments in SG. The top frameworks cited:

• Personal Data Protection Act 2012 (PDPA, as amended 2020)legislation · In force

  Organisations must obtain consent for personal data collection, implement data protection policies commensurate with data sensitivity, notify the PDPC and affected individuals of significant data breaches, and appoint a Data Protection Officer.

• Model AI Governance Framework for Generative AI (IMDA/PDPC, 2024)voluntary_framework · Voluntary

  Organisations should establish internal AI governance structures, conduct regular risk assessments of AI systems for safety and fairness, implement human oversight mechanisms proportionate to decision consequence, and communicate AI use transparently to affected stakeholders.

• MAS Principles on Fairness, Ethics, Accountability and Transparency (FEAT)regulatory_principles · In force

  Financial institutions must ensure AI and data analytics deployed in financial services are fair, ethical, accountable, and transparent, and should conduct FEAT self-assessments using the Veritas Fairness Assessment Methodology.

• MAS Advisory on Use of Generative AI in Financial Services (2024)regulatory_guidance · In force

  Financial institutions deploying generative AI must implement robust governance frameworks, maintain human oversight for material financial decisions, ensure data quality and provenance, conduct ongoing model performance monitoring, and manage third-party AI vendor risks.

How the AI Bias Audit Framework approaches this

You describe your organisation and AI estate, then answer 25 self-assessment questions across four phases (use-case characterisation, current bias-testing maturity, governance posture, and a 5-question sector-specific block tailored to HR / Healthcare / Financial Services / Government / Education / Insurance / Universal). The tool maps your stated posture into a structured, evidence-based bias audit framework ready for your compliance, legal, and AI-governance practitioners.

The Executive Summary Word document is a one-page sign-off artifact — Risk Classification Scorecard, top 5 bias risks tied to specific AI systems, 30/90/365-day path forward, sign-off block, embedded heatmap + doughnut + gauge charts. The detailed Excel workbook is the working remediation instrument: tier each AI tool, pre-filled audit checklist, fairness testing protocol with explicit thresholds, RACI ownership, permitted/prohibited lists, monitoring cadence, action plan, and a live Dashboard. Both are AI-assisted drafting aids intended to accelerate review by qualified practitioners.

What you get

• ✓ Two artefacts, two jobs: Executive Summary (.docx) with embedded charts for board sign-off, Detailed Workbook (.xlsx) for the working remediation tracking — no overlap, no confusion.
• ✓ Intake-driven: every gap rating, risk-tier classification, and remediation action ties back to your stated YES/NO/PARTIAL/UNSURE answer — no generic checklist boilerplate.
• ✓ Sector-specific: HR customers get NYC LL 144 + EEOC 4/5ths-rule probes; healthcare gets clinical-algorithm fairness + FDA SaMD; financial services gets ECOA / Veritas FEAT — automatic per industry.
• ✓ Live dashboard with formulas + native radar + doughnut + per-section completion that auto-refresh as you mark items Done — no regeneration needed to see remediation progress. Tailored to your jurisdiction, industry, organisation size, and risk appetite.

Ready to generate?

$39 · one-time — answer a 6-question intake (including jurisdiction = SG), and download your tailored document immediately.

Also available framed for your sector → see industry-specific pages