As we navigate through October 2026, the Malaysian technology landscape is undergoing a profound structural evolution. The Ministry of Digital’s proposed Artificial Intelligence (AI) Governance Bill is currently being refined, and the Online Safety Act 2025 (ONSA) is actively reshaping how platforms manage online risks. Yet, amidst these sweeping federal regulations, a critical legal grey area remains: When an artificial intelligence assistant makes a systemic error, who ultimately takes the fall?
For local tech startups, civic NGOs, and media entities deploying AI chatbots to assist voters, this is far more than an abstract theoretical debate. Under current laws, if an AI hallucinates—fabricating a false voting deadline or incorrect polling station—the local company deploying it faces severe legal exposure. To prevent a chilling effect on local innovation, Malaysia urgently requires a structured legal framework: a Modified "AI Safe Harbour".
Visualising the Risk: The "Relay Race" of AI Information
If you aren't a lawyer or a software engineer, the concept of "algorithmic supply chain liability" sounds like jargon. So, let’s break it down into something ordinary people can easily picture: a relay race.
Imagine a 4x100m relay race where the "baton" is a piece of important civic information—for example, the location of your polling station.
* The First Runner (The Data Source): This is the official authority, like the Election Commission (SPR). They hold the correct, original information.
* The Second Runner (The Model Developer): This is the tech giant that designed and trained the foundational AI model (like OpenAI or Anthropic). They build the "brain" that processes the data.
* The Third Runner (The Local AI Operator/Deployer): This is a local Malaysian news website or NGO that integrates the Developer's AI into a friendly chatbot on their own website.
* The Fourth Runner (You, the Voter): You ask the chatbot where to vote, waiting at the finish line to receive the baton.
Here is the problem: sometimes, the AI (the Second Runner) gets confused and accidentally alters the information before passing it on, resulting in an "AI hallucination."
Under strict liability rules, if the AI stumbles and passes a hallucinated baton, the referee penalises the third runner—the local Malaysian Operator—because they were the ones who handed the baton to you. Because the penalty is so severe, local businesses are terrified to join the race at all.
Who Holds the Steering Wheel? The Open vs. Closed Weight Distinction
To create fair laws, regulators must understand exactly how much control the Third Runner (the local Operator) actually has over the AI. In the tech industry, this depends heavily on how the AI is packaged:
* Closed-Weight Models (The Locked Box): If the local Operator uses an API (like GPT-4), the AI is effectively a locked box. The Developer controls the internal code and "weights." The local Operator has zero power to fix the core brain if it hallucinates; they can only build safety filters around it.
* Open-Weight Models (The Open Hood): If the local Operator downloads an open-weight model (like Meta’s Llama) and runs it on their own servers, they can look under the hood. They have the technical ability to fine-tune the model's core behaviour.
* Developer & Operator: If a massive tech company builds its own AI from scratch and offers it directly to the public, they are running both the second and third legs of the relay race. They absorb all the liability and do not need a deployer's Safe Harbour.
Because a closed-weight Operator has far less control than an open-weight Operator, the law should not treat them identically.
Two Distinct Legal Scenarios
To understand why local innovators are vulnerable today, it helps to examine how Malaysian courts currently evaluate liability.
It is important to clarify that we are not talking about National Critical Information Infrastructure (NCII) like core government banking servers or defence networks, which carry specialised statutory burdens. Ordinary civilian chatbots fall under two clear common law and statutory tests:
Scenario A: The Active Publisher Risk (Mkini Test)
* The Legal Precedent: In the landmark Federal Court case Mkini Dotcom Sdn Bhd v Peguam Negara (2021), the court ruled that a news portal could be held liable for third-party content on its platform once it had constructive knowledge, rejecting absolute passive immunity.
* Application to AI: Generative AI is not a passive pipe; it actively curates and synthesises text. Without a modified safe harbour, local Operators are treated as active publishers, exposing them to strict liability under Section 233 of the Communications and Multimedia Act (CMA) 1998.
Scenario B: The Non-Delegable Institutional Duty (Columbia Asia Test)
* The Legal Precedent: In Siow Ching Yee v Columbia Asia Sdn Bhd (2024), the Federal Court established that institutions owe a non-delegable duty of care and cannot contract away their liability by pointing fingers at independent third parties.
* Application to AI: A local Malaysian Operator cannot simply blame an overseas AI Developer if their chatbot provides faulty civic data. Choosing to operate the tool creates an institutional duty to ensure its reliability.
The Solution: Designing a Modified "AI Harbour"
To protect innovators while maintaining public trust, policymakers should institute a Modified AI Harbour built on two pillars:
1. The "Verified API" Mandate (Securing Data Provenance)
To qualify for statutory protection, local Operators must architect their chatbots to fetch civic and electoral information exclusively from official cryptographic government APIs (such as the SPR database). This restricts the AI's ability to scrape unverified web data for sensitive topics.
2. Scalable Process-Based Immunity
If an Operator implements these technical guardrails, they earn protection from prosecution under the CMA 1998. However, this immunity must scale based on control:
* For Closed-Weight Operators: Earning the Safe Harbour simply requires proving they implemented robust prompt filters and routed queries to the official SPR database.
* For Open-Weight Operators: Because they have the power to alter the AI's core code, they must meet a higher standard, proving they did not negligently fine-tune the model with corrupted data before they can claim immunity.
Looking Forward
As Malaysia solidifies its position as a premier digital hub in Southeast Asia, our regulatory frameworks must evolve. We cannot expect local enterprises to innovate if they are forced to absorb the infinite legal liabilities of foreign-built AI systems.
By instituting a Modified AI Harbour that acknowledges the nuance between Developers and Operators, the Malaysian government can establish a golden standard for the Global South—demanding rigorous safety processes while safeguarding the innovators building our digital future.
The case for an AI Safe Harbour is only part of the discussion. A further question arises when AI systems move beyond generating answers and begin taking actions through connected tools and services: who should be responsible when those actions cause harm? A companion analysis examines the legal, technical and human-accountability issues raised by agentic AI, and proposes safeguards for a conditional AI Safe Harbour in Malaysia.
Disclaimer: This article provides a policy analysis.
