Taiwan's AI Basic Act: A practical playbook for innovation, governance, and risk
Taiwan just set a clear stance on artificial intelligence. The AI Basic Act took effect on January 14, giving government and industry a common rulebook while Taiwan ramps up work on homegrown AI, including a Traditional Chinese large language model.
If you work in government, IT, or development, this is the moment to get your house in order: risk classification, data pipelines, compliance controls, and talent. The Act lays out the principles, the regulators, and the next two-year roadmap. Here's what matters and what to do next.
What the law actually defines
The Act defines AI in line with the EU's approach-systems that generate predictions, content, recommendations, or decisions for people. This makes cross-border work easier and keeps Taiwan compatible with global practice. If you're aligning with international vendors or exporting AI-enabled services, that helps.
The National Science and Technology Council is the lead regulator. The Ministry of Digital Affairs (MODA) handles the heavy lifting: governance, risk, and policy promotion.
The seven principles you'll be judged by
- Sustainable development and well-being
- Human autonomy
- Privacy protection and data governance
- Cybersecurity and safety
- Transparency and explainability
- Fairness and nondiscrimination
- Accountability
Accountability isn't just a slogan here. Expect clear expectations on who is responsible when AI is deployed-and how to prove you met your obligations.
High-risk AI: Classification, warnings, and controls
When the government classifies an AI product or service as high-risk, you must provide warnings or alerts about potential risks. MODA will set the categories with other agencies, using tools built through multistakeholder consultations-civil society, academics, industry, and legal experts included.
Translation for teams: build an internal risk review that mirrors what MODA will expect. If your system impacts safety, rights, or critical services, assume it's a candidate for high-risk and prepare documentation now.
An interoperable AI risk framework
MODA will reference global standards to keep Taiwan interoperable with major markets. Agencies will adapt this into sector-specific guidelines and codes of conduct. That means different sectors-health, finance, mobility, public services-will get practical playbooks that fit their risk profile.
Want to see where this is headed? The EU's approach offers clues: EU AI Act overview. MODA's official site will host updates as rules roll out: MODA.
Where AI is restricted or prohibited
The Act draws a line where AI harms people or society. Prohibitions and strict controls apply if AI threatens life, bodily integrity, freedom, or property; undermines social order, national security, or environmental sustainability; or enables bias, discrimination, false advertising, misinformation, or fraud in breach of existing laws.
The government will also define liability standards and set up remedy, compensation, and insurance mechanisms. If you operate high-impact systems, prepare for incident response, audit trails, and proof of due care.
Data governance and training data
Expect an open data framework to increase access to high-quality datasets for AI training and secondary use. At the same time, privacy rules still apply, with data minimization as a core requirement in AI development.
New legislation on data governance and open data is on the table. It will start with opening government-held data and encourage private sector participation over time. Copyright protections remain intact; the focus is on creating lawful, higher-quality data pipelines without weakening IP rights.
Funding, sandboxes, and talent
The government plans to increase funding for AI R&D, strengthen infrastructure, and offer incentives like tax deductions. A regulatory sandbox is also possible, which is useful for testing higher-risk use cases with guardrails.
Talent is a priority. Public-private collaboration will expand AI education and workforce training, while worker protections are kept in frame so transformation doesn't come at the expense of labor rights.
Two-year legislative roadmap
Within two years, agencies must review existing laws and pass new ones to align with the Act. MODA has already started building the risk classification framework and related mechanisms.
For industry, this is a clear signal: start compliance work now, not later. The bar will rise as sector-specific rules arrive.
Action plans for government, IT, and development teams
For government agencies
- Map AI use across programs and vendors; flag anything that could touch safety, rights, or critical services.
- Stand up an AI governance board with legal, security, privacy, and domain leads.
- Adopt risk assessment templates that track purpose, data sources, model lineage, evaluations, human oversight, and incident response.
- Prepare citizen-facing notices for AI-assisted decisions, including appeal or human review paths.
- Build to open data standards and publish data dictionaries to improve quality and reuse.
For private companies
- Inventory models and AI features across products, internal tools, and vendor services.
- Classify risk by impact area (safety, rights, compliance, financial, operational). Treat "high-impact" as "likely high-risk."
- Implement model governance: versioning, evaluation benchmarks, red-teaming, monitoring, and rollback plans.
- Introduce user risk warnings where appropriate and document your rationale.
- Review data pipelines for lawful basis, minimization, provenance, and opt-out controls.
Data and engineering checklist
- Provenance and consent: track source, license, and terms for all training and fine-tuning data.
- Security: restrict model and dataset access; log actions; run supply chain checks on third-party models.
- Testing: bias and safety tests pre-release; continuous evaluation in production with drift detection.
- Human-in-the-loop: define clear escalation points for high-stakes decisions.
Talent and capability
- Upskill teams in AI risk, privacy-by-design, and secure ML ops.
- Pair domain experts with ML engineers for policy-aligned delivery.
- For structured learning paths by job function, see curated options: AI courses by job.
Why this matters now
Taiwan is building an AI ecosystem with clear guardrails and global compatibility. The AI Basic Act sets the direction; sector rules will add the details.
Teams that move early-auditing their AI footprint, tightening data governance, and standing up practical risk controls-will ship faster with fewer surprises. That's how you build useful AI and keep trust intact.
Your membership also unlocks:
