Bangladesh releases a national roadmap for ethical AI: what IT and dev teams need to do now
Bangladesh has released a national roadmap focused on ethical AI. For teams building software, selling into government, or operating data-heavy systems in the country, this sets clear expectations: safer products, better documentation, tighter governance, and skills to match.
The move also signals where budgets and compliance will go next. If you align early, you'll ship faster, win more RFPs, and avoid costly rework.
What this roadmap likely covers
- Governance and oversight: defined roles, approvals, audits, and accountability across the AI lifecycle.
- Data rights and privacy: lawful sourcing, consent, minimization, retention, and deletion standards.
- Safety and risk: pre-release testing, red teaming, incident response, and ongoing monitoring.
- Transparency: disclosures, model/system cards, and user communication for high-impact use cases.
- Fairness and inclusion: bias testing, representative datasets, and documented mitigations.
- Skills and adoption: workforce development, AI in public services, and R&D incentives.
What this means for engineering leaders
Treat AI work like safety-critical software. Your backlog should include governance, risk, and documentation tasks-not just features.
- Classify AI use cases by risk (e.g., user-facing, decision-making, health/finance/education).
- Run data mapping: what you collect, why, where it lives, who can access it, and retention rules.
- Add human oversight for high-risk flows (review, escalation paths, appeal rights).
- Stand up an evaluation pipeline with pre-release gates and live monitoring.
Implementation checklist (ship-ready)
- Data: lawful source docs, consent flows, PII handling, retention/deletion policy.
- Documentation: model cards, system cards, changelogs, decision logs.
- Testing: bias/accuracy across segments, adversarial prompts, jailbreak defense.
- Safety: content filtering, rate limits, guardrails for tools and actions.
- Security: secret management, dependency scanning, isolation for inference.
- Monitoring: drift detection, data quality, output anomalies, abuse signals.
- Auditability: traceable logs, dataset lineage, model versioning, prompt/output logging with DLP.
- Vendors: third-party risk reviews, DPAs, security questionnaires, right-to-audit.
- Incidents: playbooks, rollback procedures, user notification criteria.
MLOps upgrades that pay off
- Offline eval suite with red team sets; block release if thresholds fail.
- Shadow mode and canary releases before full rollout.
- Prompt templating with versioning; store stimuli, context, and outputs.
- Automated PII scanning at ingest and pre-output; auto-mask where feasible.
- Feature flags to disable risky capabilities instantly.
Policy alignment (use proven frameworks)
Anchor your program to widely accepted standards. They translate policy goals into practical controls and metrics.
- NIST AI Risk Management Framework: governance, maps, measures, and manages risk.
- ISO/IEC 23894:2023 (AI risk management): lifecycle controls you can integrate into SDLC.
Data location and cross-border flows
Expect stronger scrutiny on where data is stored and processed, especially for public sector and regulated domains. Keep an inventory of data residency, subprocessors, and model hosting regions-make this exportable for buyers and auditors.
Procurement signals for vendors
- Clear problem statements and impact assessments for AI features.
- Security attestations, DPAs, and model lineage documentation.
- Evaluation reports (accuracy, bias, safety) with test sets and methods.
- Incident SLAs, rollback capabilities, and kill switches.
- Right-to-audit and periodic compliance updates.
For startups
Document early and keep it light but real. Simple model cards, structured changelogs, and a single source of truth for data and evaluations will save you weeks during procurement. If a regulatory sandbox appears, bring your eval reports and aim for fast iterations with tight feedback loops.
90-day action plan
- Weeks 1-2: Inventory AI use cases, data flows, and third-party services. Pick your standards (NIST/ISO).
- Weeks 3-4: Write baseline policies (data, model governance, incidents). Create risk tiers.
- Weeks 5-6: Build the evaluation pipeline and safety filters. Add logging and lineage.
- Weeks 7-8: Run bias, safety, and security tests. Fix gaps and set release thresholds.
- Weeks 9-10: Train teams on procedures. Pilot shadow/canary for one high-impact flow.
- Weeks 11-12: Vendor review, privacy checks, and a mini internal audit. Publish docs to stakeholders.
Upskill your team
If your roadmap includes new AI initiatives or compliance goals, a focused training path helps. Explore curated options by role here: AI courses by job.
Bottom line
Bangladesh's ethical AI roadmap raises the bar on safety, transparency, and accountability. Teams that operationalize these practices now will move faster, face fewer procurement hurdles, and build systems users trust.
Your membership also unlocks:
