Who should put things right when an AI assistant gives voters the wrong answer? A companion analysis on Malaysian election integrity.
By Muhammad Syawalfiza.
Part of a three-essay series: This article is the second companion analysis to "Protecting Voter Autonomy in the Age of Artificial Intelligence (AI)" , following "The Invisible Architect: How AI Is Quietly Shaping What We See".
The original essay examines how AI could influence political judgement and voter autonomy. The first companion explores how information selection and presentation can quietly shape the choices people see. This second companion addresses a practical question: when an AI-generated election answer is wrong, who is responsible for correcting it?
1. Follow the information, not just the answer
Imagine a voter asking an AI assistant where to vote, what identification is required or whether a registration deadline has changed. The response is quick, clear and confident. It may even include a source link.
But confidence is not proof of accuracy.
A Malay proverb captures this neatly: "Sepandai-pandai tupai melompat, sekali gawal juga". Even the most skilful can make a mistake. An AI system should therefore be judged by the evidence behind its answer, not by how confidently or fluently it speaks.
The information might have originated with the Election Commission of Malaysia (SPR), passed through a data intermediary, been retrieved and summarised by an AI system, and finally appeared in a search interface or messaging application.
Think of it as a relay race:
SPR's official information → data intermediary → AI-generated answer → digital platform → voter
If something goes wrong, the mistake may have occurred at any point along the route.
The appropriate response is not to blame SPR automatically because the information began with an official record, or to assume that the AI provider is responsible for everything that appears on a platform. Responsibility should follow each actor's function, control over the process and ability to prevent or correct the error.
SPR should answer for official information within its control. Intermediaries should preserve its source and currency. AI providers should address failures in retrieval, synthesis and presentation. Platforms should address the distribution and reporting functions they control.
This is distributed but differentiated responsibility: each organisation should be accountable for the part of the process it operates.
2. The problem is already documented internationally
Research has established that AI assistants can produce inaccurate, incomplete or outdated election information. In a study of 300 responses to 100 questions about the 2024 UK general election, Simon, Fletcher and Nielsen (2024) found that ChatGPT-4o and Perplexity.ai provided correct answers in 78% and 83% of cases, respectively.
These figures apply to the particular systems, questions and testing period. They should not be treated as universal error rates or evidence that AI-generated election information has changed voting behaviour.
The wider governance challenge is recognised in International IDEA's "Artificial Intelligence for Electoral Management" (2024) and the United Nations Development Programme's "Global Landscape Analysis: Artificial Intelligence in Electoral Administration", published on 19 October 2026. The latter draws on a survey of 53 electoral management bodies across six regions (United Nations Development Programme [UNDP], 2026).
These precedents matter. The research opportunity is not to claim that nobody has considered AI accuracy or accountability in elections. It is to determine whether responsibilities are adequately allocated when official information passes through several independent systems—and whether a voter can obtain an effective correction when an answer fails.
A practical example already exists in the United States. Democracy Works' Elections API supplies data drawn from official government sources, offers links through which users can examine sources and report problems, and supports integration with generative-AI services.
On 20 October 2026, OpenAI reported that its Democracy Works integration had gone live in ChatGPT in the United States. It provides information about registration, deadlines, voting locations and other requirements, with links to official election sources (OpenAI, 2026).
These initiatives demonstrate a feasible approach to source-verified voter information. They are not, by themselves, proof that every generated answer is correct or evidence of a universal legal duty applying to all AI providers. The problem is already documented internationally
Research has established that AI assistants can produce inaccurate, incomplete or outdated election information. In a study of 300 responses to 100 questions about the 2024 UK general election, Simon, Fletcher and Nielsen (2024) found that ChatGPT-4o and Perplexity.ai provided correct answers in 78% and 83% of cases, respectively.
These figures apply to the particular systems, questions and testing period. They should not be treated as universal error rates or evidence that AI-generated election information has changed voting behaviour.
The wider governance challenge is recognised in International IDEA's "Artificial Intelligence for Electoral Management" (2024) and the United Nations Development Programme's "Global Landscape Analysis: Artificial Intelligence in Electoral Administration", published on 19 October 2026. The latter draws on a survey of 53 electoral management bodies across six regions (United Nations Development Programme [UNDP], 2026).
These precedents matter. The research opportunity is not to claim that nobody has considered AI accuracy or accountability in elections. It is to determine whether responsibilities are adequately allocated when official information passes through several independent systems—and whether a voter can obtain an effective correction when an answer fails.
A practical example already exists in the United States. Democracy Works' Elections API supplies data drawn from official government sources, offers links through which users can examine sources and report problems, and supports integration with generative-AI services.
These initiatives demonstrate a feasible approach to source-verified voter information. They are not, by themselves, proof that every generated answer is correct or evidence of a universal legal duty applying to all AI providers.
3. Does Malaysia already have relevant laws?
Yes. Malaysia has electoral legislation, communications regulation, an online-safety framework, data-governance rules and national AI governance initiatives. The issue is whether these arrangements work together effectively for third-party AI answers.
MCMC and the Communications and Multimedia Act
On 15 November 2025, the Malaysian Communications and Multimedia Commission (MCMC) reminded social-media users to act responsibly during Sabah's state election. Its statement referred to section 233 of the Communications and Multimedia Act 1998 (Act 588) and the enforcement of applicable restrictions on online communications (Malay Mail, 2025).
This illustrates the regulator's public approach to online conduct during an election. However, a news report summarising a regulator's warning is not a substitute for the statutory text. Liability depends on the provision in force, its elements and the circumstances of the case.
An AI system giving an outdated polling location is not automatically equivalent to a person committing a criminal offence. Nor does section 233, by itself, establish a universal duty requiring every AI provider to cite official sources, update election information or correct every factual error within a prescribed time.
The Communications and Multimedia Act 1998 (Act 588) must be distinguished from the Malaysian Communications and Multimedia Commission Act 1998 (Act 589), which establishes MCMC and defines its institutional functions. Both Acts were amended in 2026, with the relevant amendment Acts (A1798 and A1799) coming into force on 14 October 2026. Their implications must be assessed provision by provision, taking account of the statutory wording, applicable service definitions and regulatory powers. The commencement of these amendments does not, by itself, establish a blanket duty to correct every AI-generated election answer.
The Online Safety Act
The Online Safety Act 2025 (Act 866) came into force on 1 January 2026. It establishes duties for specified service providers within its scope, including requirements relating to harmful-content risks, user assistance and reporting mechanisms.
This is an important regulatory development. Nevertheless, its harmful-content framework should not be treated as a general guarantee that every factual statement generated by an AI system will be accurate. An error about a polling location may require correction even where it does not fall within a particular statutory category of harmful content.
SPR and existing complaint channels
SPR already publishes official electoral information and maintains public complaint and feedback mechanisms. Its customer charter sets out targets for handling public complaints and forwarding relevant election-period feedback to the responsible parties.
This is useful existing infrastructure, not something the research should overlook. The unresolved question is whether these channels can handle errors originating outside SPR's own systems.
Suppose SPR publishes the correct polling information, an intermediary retains an old version and an AI assistant subsequently delivers it to a voter. SPR may be able to confirm the error, but who corrects the intermediary's data? Who fixes the AI system's retrieval process? How does the voter know that the correction has reached the service used?
Those questions require investigation of actual procedures. The existence of a complaint channel does not, by itself, establish that the whole correction chain is connected.
Malaysia's proposed AI Governance Bill
Malaysia's National AI Office has been developing a proposed AI Governance Bill. Its 2026 public consultation has closed, but the proposal should not be treated as enacted law.
The emerging framework offers an opportunity to consider sector-specific safeguards, including the treatment of errors passed between developers, data intermediaries and deployers. The research must establish what powers and obligations the eventual legislation actually creates rather than assume that a horizontal AI law will automatically resolve election-information errors.
The legal conclusion at this stage is measured: Malaysia has relevant laws and institutions, but the material reviewed does not establish a complete, universal process for correcting ordinary factual errors in every third-party AI voter-information service.
4. What should a workable accountability system look like?
For election information, that means checking source links, update arrangements and correction procedures before polling begins, rather than waiting until a voter receives the wrong answer.
A proportionate system should connect five responsibilities.
First, authoritative information. SPR should make important voter information identifiable, dated and accessible. Where practical, official records should have version identifiers and formats that digital services can reliably process.
Second, intermediary integrity. An aggregator should preserve source links, record update times, distinguish official information from unverified material and propagate corrections when its upstream source changes.
Third, AI-provider responsibility. Providers should test important voting-related questions, use authoritative sources where available, show sources that genuinely support material claims and acknowledge uncertainty when information cannot be verified. They should provide an accessible route for reporting errors and address defects within their control.
Fourth, voter redress. Voters should not have to diagnose the technical failure before reporting it. A coordinated process should receive the complaint, identify the likely point of failure, refer it to the responsible organisation and communicate the outcome.
Fifth, oversight. Regulators should act within their legal powers, require proportionate remediation where authorised and provide transparent reasons and opportunities for review.
The key distinction is between finding a mistake and fixing it. A warning that an AI answer may be wrong does not restore an outdated polling location. The underlying record, retrieval process or generated answer must be corrected where necessary.
5. Five Additional Safeguards for AI-Mediated Voter Information
As the Malay saying Sediakan payung sebelum hujan reminds us, preparation should come before trouble arrives (DBP, n.d.-b).
A workable system needs clear official sources, traceable intermediaries, tested AI answers, accessible reporting and proportionate oversight. Five additional safeguards could help Malaysia develop such a system.
5.1. Establish a local compliance route
Major AI providers may operate from outside Malaysia. Contacting an overseas provider during an election does not necessarily ensure a timely response.
Malaysia could examine whether designated high-reach or high-impact AI services should appoint a local legal representative or registered compliance contact, where legislation provides a valid basis. The EU's Digital Services Act and AI Act provide precedents for local representatives for specified non-EU providers.
A named contact could receive lawful notices, preserve relevant records and coordinate responses. But local representation would not automatically expand MCMC's or SPR's powers, guarantee overseas compliance or justify blanket data-localisation requirements. ASEAN cooperation and shared incident-reporting procedures could provide additional support (ASEAN Secretariat, 2025).
5.2. Make correction speed proportional to risk
A wrong polling location is more urgent than an error in general electoral history. Correction targets should reflect the severity and timing of the potential harm.
Malaysia could distinguish routine incidents, election-period errors and critical failures close to polling. A proposed 12–24-hour correction window could be tested, but should not be presented as an established international standard; it may be too slow for an error affecting voters on polling day.
For critical procedural questions, systems should use a verified fallback. If the current answer cannot be confirmed, the AI should stop improvising and direct the user to the appropriate official SPR service.
Australia's government AI technical standard supports risk mitigation through interventions such as safe-state recovery, manual override and system logging.
The practical rule is simple: when an AI system cannot verify where or when someone should vote, it should say so and direct the person to an authoritative source—not guess.
5.3. Connect reporting to a coordinated correction process
AI providers already have reporting mechanisms. Google's Gemini guidance lets users flag inaccurate or unsafe responses. OpenAI's reporting guidance explains how to report content and notes that reports about unreliable sources may lead to mitigations.
The gap is not an absolute absence of feedback tools. It is whether an election-related report can be traced to the responsible organisation and corrected throughout the information chain.
A proposed Malaysian protocol could record the disputed answer, time, cited source and error type, then route the report to SPR, MCMC or the provider according to their respective responsibilities. Case identifiers, response logs and repeat-error checks would make performance auditable.
A coordinated procedure could work as follows:
Voter reports error → responsible organisation checks it → source or system is corrected → provider updates or limits the faulty answer → outcome is recorded.
SPR should correct official information within its control; providers should remedy errors in their systems. A coordinated procedure may be sufficient initially; creating a new task force should depend on demonstrated need and clear legal authority.
5.4. Develop a privacy-preserving SPR data interface
AI systems often retrieve election information from web pages, search results and commentary that may be inconsistent or outdated. A structured application programming interface (API)—a controlled connection between computer systems—could allow participating services to obtain published official information directly.
International precedents include Democracy Works' Elections API and Google's Civic Information API guidelines. Google specifies that voting-location and contest information should not ordinarily be cached for more than 24 hours when no different cache-control instruction applies.
SPR could examine a versioned, read-only interface for non-personal information, including election dates, general voting procedures, published polling arrangements, source links and update timestamps. Its reliability would depend on accurate underlying records and effective update processes: an API is not automatically a source of truth.
Personal voter information must remain protected. Services such as MySPR Semak involve individualised lookups and should not become unrestricted public data feeds. Secure integration or direct links to SPR would often be safer than transferring identifying information to a general-purpose AI service.
5.5. Use cryptographic verification as one layer of defence
An attacker might attempt to place misleading information in websites or retrieval sources later used by an AI system. Research on knowledge poisoning in retrieval-augmented generation, including knowledge-graph-based systems, demonstrates that maliciously introduced information can undermine system performance (Zhao et al., 2026). This is a technical risk, not evidence that such an attack has occurred against SPR or a Malaysian election.
Digital signatures can help confirm that a record came from a recognised source and has not been altered since signing. The C2PA provenance standard illustrates how cryptographic methods can support the verification of origin and history.
SPR could consider signing suitable official feeds, using trusted keys and version identifiers. Providers could verify signatures, check freshness and prioritise approved sources for high-risk procedural answers.
However, a valid signature does not prove that information is correct or current. A signed record can still be outdated, an official source can contain an error, and an AI system can misinterpret genuine information. Cryptographic verification should therefore complement—not replace—source validation, monitoring and correction.
Together, the five (5) safeguards form a proposed Malaysian election-information assurance protocol. Their value should be assessed through measurable indicators such as source accuracy, correction time, repeat errors, verification failures and privacy impacts. They are adaptations of established practices, not five newly invented concepts.
6. Accuracy must not become a pretext for censorship
Any Malaysian framework should distinguish verifiable procedural information from contested political expression.
Polling locations, registration deadlines, ballot instructions and certified results can generally be checked against an authoritative record. Political opinions, policy judgements, satire and predictions are different. They should not be treated as though SPR were the sole authority on political truth.
A factual error may call for a correction, source link or update notice. Removal or sanctions require a separate legal basis and a proportionate assessment. Decisions should be transparent, reviewable and protected against partisan application.
A pantun recorded in DBP's Khazanah Pantun Melayu Riau offers a pointed reminder about political conduct:
Kalau hendak membeli kain,
Jangan dibeli kain luntur;
Kalau awak jadi pemimpin,
Jangan sekali bermain kotor.
The message is relevant beyond politicians. Those who administer election information, develop AI systems or enforce the law should apply consistent standards and resist the temptation to manipulate information for partisan advantage.
That does not mean every mistake is deliberate misconduct. It means that the response to an error should be fair, evidence-based and proportionate, regardless of which political interests may benefit from it.
Source labels should help voters understand the status of an answer. A useful label might identify whether information comes from an official SPR source, a secondary report, an AI-generated synthesis or an unresolved claim. Such labels cannot guarantee accuracy, but they can make verification easier and reduce unwarranted confidence.
The objective is not to suppress political discussion. It is to help voters distinguish reliable procedural information from material that needs further checking.
7. What would make this a meaningful Malaysian contribution?
The original essay, Protecting Voter Autonomy in the Age of Artificial Intelligence, considers how AI may influence political judgement. The Invisible Architect examines how the selection and presentation of information can shape what people see. This second companion addresses the next step: who is accountable when that information is wrong, and can the error be corrected?
A defensible research contribution would combine a provision-by-provision legal analysis with an empirical audit of publicly accessible AI systems used by Malaysian voters. It could also test whether simple interface features make errors easier to recognise and report—without creating a false impression that an answer is guaranteed to be correct.
Make reporting easy, but distinguish feedback from correction
Two major AI services already provide ways for users to flag problems.
Google's Gemini Apps Help guidance allows users to mark a response as good or bad, select a reason, add comments and, where available, attach a screenshot. Google also explains that the associated conversation and its prompts and responses may be included in the feedback, alongside other submitted material. Users should therefore consider privacy before attaching sensitive information.
OpenAI's content-reporting guidance describes ways to report conversations, responses, shared links, GPTs and other content that may violate its Terms of Use or applicable laws. It also states that reported domains or content may be reviewed by its Model Quality team, which may apply filters or other mitigations to reduce future reliance on unreliable sources.
These systems provide useful starting points: feedback is available close to the answer, users can give additional context, and reports can inform quality or safety processes. However, neither help page promises that every factual error will be corrected, that a correction will propagate to every subsequent answer, or that the person reporting it will receive a confirmed resolution.
For election information, a more targeted process could separate three types of report:
• Incorrect factual answer: for example, a polling location that conflicts with the current official record.
• Source or freshness problem: a cited page is unavailable, outdated or does not support the claim.
• Safety or legal concern: content that may breach applicable rules and requires a separate assessment.
A report should capture the answer, date and time, source link, type of problem and, where available, the product or model version. A screenshot may help, but should be optional. The interface should explain what information will be submitted, allow users to remove unnecessary personal details and provide a reference number or status where operationally feasible.
This would make a report more actionable than a generic thumbs-down rating. It would also help the service distinguish dissatisfaction with an answer from a verifiable factual error.
Use epistemic markers to show what is known—and what is not
An epistemic marker is a label or cue that helps a reader understand the evidence and status behind a claim. In this context, useful markers would say more about the source and verification process than about the model's own confidence.
A simple answer card might display:
Source: Official SPR page
Source updated: Date shown by SPR, if available
Checked by this service: Date and time of retrieval
Status: Linked source located; claim not independently verified
Correction: Report a factual error
Other appropriate labels could include “Sources conflict—check SPR”, “Official result”, “AI-generated summary” or “Correction issued”. The exact wording should match what the system can actually establish. A service should not display “verified” merely because it has inserted a link; verification requires checking that the source is authoritative, current and supports the material claim.
There is a recognised technical basis for preserving source lineage. The World Wide Web Consortium's PROV model describes how to record the entities, people and processes involved in producing or changing information. It can support traceability between the original record, an intermediary's transformation and the delivered answer (World Wide Web Consortium [W3C], 2013). It is a data-provenance model, not a guarantee that the underlying information is true.
Research on explanations in AI-assisted decision-making also points to three useful design goals: helping people understand the system, recognise uncertainty and calibrate their trust appropriately (Wang & Yin, 2022). These are objectives to evaluate, not outcomes that can be assumed merely because a label or explanation has been added.
In a preregistered experiment involving 495 US-based adults with postgraduate degrees, Ojewale et al. (2026) found that visual confidence indicators improved participants' subjective ability to distinguish correct from incorrect AI outputs, but also increased behavioural agreement with incorrect answers in the experimental setting. Combining visual indicators with verbal uncertainty cues produced still greater over-reliance and less independent source-checking. These findings do not mean that every visual label is harmful. They show why uncertainty cues must be evaluated by how people actually use them, rather than assumed to improve trust calibration simply because they are displayed.
The study does not show that every visual label is harmful. It does demonstrate that adding more cues is not automatically better. For electoral information, source identity, dates, actual verification status and a clear route to check the official record may be more useful than a decorative confidence gauge.
Use simple visual and productivity tools where they solve a real problem
The study does not need an elaborate dashboard or a new application to be useful. A few carefully chosen tools could make the accountability chain easier to understand and operate.
A simple information-flow diagram can show the voter how an answer travels from SPR through an intermediary and an AI service. A source card can display the actual source, its date and the answer's verification status. A correction tracker can record when a report was received, which organisation is handling it, what was corrected and whether the issue recurred. These tools make different responsibilities visible without requiring the voter to understand the underlying technology.
For researchers and regulators, a lightweight dashboard could summarise the number of errors found, source failures, average correction time, repeat errors and differences across tested languages. A secure spreadsheet or case-management queue may be sufficient for an initial pilot; a complex visual interface should be justified by an actual operational need.
The design should remain readable on a mobile phone, accessible to people with disabilities and usable in Bahasa Melayu and English. Colour should not be the only way to distinguish statuses. Labels should use ordinary language, and detailed provenance should be available without overwhelming the main answer.
These choices are consistent with work on data provenance and human–AI interaction, but their effectiveness in Malaysian election services still needs to be tested. The aim is not to make the answer look more trustworthy. It is to make verification and correction easier.
Test whether the tools actually help
The audit could compare three versions of the same service:
1. A plain AI answer with its existing citations.
2. The same answer with source, date and verification-status markers.
3. The marked answer with an accessible error-reporting process and a clear confirmation or status route.
Researchers could assess whether participants identify incorrect answers more reliably, open authoritative sources more often, submit complete error reports, understand the status labels and calibrate their trust to the actual accuracy of the output. The test should include Bahasa Melayu and English, and should report differences among users rather than assume that one design works equally well for everyone.
The evaluation should also measure possible drawbacks: false reassurance from a label, excessive dependence on a visual indicator, confusion over technical terms and unwillingness to consult the official source. Results should determine which features are retained.
This design would combine established principles with a testable intervention. It would not presume that epistemic markers, reporting tools or visualisations have already been proven to improve Malaysian voters' decisions.
The original essay, Protecting Voter Autonomy, concerns the protection of political judgement; The Invisible Architect considers how information presentation can influence what people see. This study adds a practical accountability question: can voters identify the source of an AI answer, understand its status and get an error corrected?
The answer should come from legal analysis and empirical evaluation, not assumption.
Conclusion: make responsibility traceable.
When a voter receives a wrong answer, the practical questions are straightforward. Where did the information come from? When was it last updated? Who controlled the process that introduced the error? Can the voter report it? Can the responsible organisation correct it? And can the correction be verified?
Malaysia already has relevant electoral, communications and online-safety institutions. The task is to determine whether their powers and procedures connect effectively across the voter-information chain.
The strongest approach is not to treat every AI error as a crime or to create sweeping powers over political discussion. It is to establish clear sources, traceable updates, tested systems, accessible complaints and proportionate remedies, with independent oversight and protection for lawful expression.
Responsibility should follow function, control and the capacity to correct. Voters should not have to understand the technology behind an answer before they can expect a reliable correction.
Research note: This is a policy analysis, not a formal legal opinion.
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