From the impending United States midterms to Israel's October 2026 elections and Malaysia's preparations for its 16th General Election (GE16), we like to believe our votes are the product of independent thought. We gather facts, weigh policies and make rational choices. But what happens when the digital systems we rely on to gather those facts present information in ways that shape our judgments—sometimes unintentionally, and sometimes through deliberate misuse?
This companion analysis builds on Protecting Voter Autonomy in the Age of AI by examining how generative AI intersects with behavioural economics. The concern extends beyond false content: AI systems may also shape which political information voters encounter, how it is presented and which options they consider.
As we navigate the 2026 global election cycle, here are four of the most critical, often-overlooked dynamics at play, and what experts say we can do to build resilience against them.
1. The Demand-Side Challenge: Why Misleading Political Content Can Gain Traction
Political misinformation can spread not only because people mistake false information for truth, but also because prior beliefs, group identities and emotional reactions can influence how they evaluate and share political content.
In a polarised environment, some voters may be more inclined to engage with material that reinforces their existing views or criticises a political opponent. This does not mean that voters knowingly want falsehoods, or that every person who encounters misleading content will believe or share it.
Disclosure labels and watermarks may help, but they should not be treated as a complete solution. Their effectiveness depends on how people interpret them, whether they trust the source, the context in which the content appears and what other information is available. Experimental research on AI-generated political images suggests that well-designed labels can reduce perceived credibility and, in some settings, reduce belief in misleading claims or willingness to share them. However, effects vary with the label design and research setting. Labelling can be a useful safeguard, but it should not be treated as a guarantee that voters will reject misleading content.
The practical question is therefore not whether voters secretly want lies. It is which combinations of disclosure, source verification, timely correction and media literacy best reduce the belief and spread of misleading political content.
These risks are becoming increasingly relevant to election campaigns, although deliberate psychological manipulation must be established case by case.
In the lead-up to Israel's 2026 elections, the potential use of AI to shape political perceptions warrants close scrutiny. In his October 14, 2026 essay, Dean Livne, acting director-general of Israel's Central Elections Committee, warns that AI systems may distort voter choices through misleading information, incomplete candidate coverage and opaque recommendations.
These concerns identify credible risks, but they should not be treated as proof that specific political campaigns are already using AI to manipulate voters' emotions. Establishing such activity requires separate evidence, such as documented campaign materials, independently verified incidents or credible investigations.
Whether confirmation bias or motivated reasoning explains why a person shared a particular deepfake should be established through evidence about that incident or audience.
In Layman's Terms (Visualize This): Imagine a political deepfake is a box of junk food. Regulators think that putting a giant "Unhealthy Ingredients" warning label on the box will stop people from eating it. But if that junk food perfectly satisfies a person's craving for outrage against a politician they already dislike, they may still be tempted to consume it. The problem isn't just that AI makes the junk food; outrage can make it tempting to consume.
2. The Illusion of Algorithmic Parity: Confident but Incomplete
We often focus on whether AI can write a better speech than a human. But the true danger lies in its scale and manufactured confidence. The States United Democracy Center's study found that AI platforms directed voters to official state election websites less than half the time overall. ChatGPT mentioned a state website in 39.4% of responses, while Google AI did so in 55.6%. Both systems also showed gaps in the completeness and sourcing of election information.
The Brennan Center’s 2026 tests found that several chatbots struggled to recognise synthetic images, including images generated by their own tools. After OpenAI released a tool to verify images generated by its systems, ChatGPT identified some of its own tested images but not all. The broader tests found that most chatbots still struggled to identify AI-generated images.
In Layman's Terms (Visualize This): Imagine a highly confident tour guide who speaks perfectly but only shows you half the map. They assure you that you've seen the whole city, and because they sound so professional, you don't bother to double-check. The AI isn't necessarily lying to you; it is just confidently leaving out crucial pieces of reality.
3. The Erasure of the Local: How Algorithms Narrow Our Choices
The Dutch findings illustrate how AI chatbots can act as “choice architects”: the parties they recommend may influence which political options voters notice. An audit by the Dutch Data Protection Authority, published in March 2026, found that specific local political parties were recommended as the first choice in fewer than 1% of the chatbot voting recommendations examined, despite local parties having received more than 30% of the votes in the preceding municipal elections.
The regulator identified limited and less up-to-date online information about local parties as one possible explanation. The findings demonstrate a problem with the representation of political choices in the systems tested, but they do not establish a single cause for that behaviour.
For Malaysia, this raises a legitimate question rather than a proven outcome: would widely used AI systems provide complete and balanced information about parliamentary, state and local political choices? The answer should be established through systematic testing across Malaysian parties, languages and constituencies, rather than assumed from overseas findings.
Malaysia's electoral map raises a separate issue: malapportionment, where constituencies have different numbers of voters and therefore unequal relative voting weight. Gerrymandering—the strategic drawing of boundaries for political advantage—is a distinct problem. Neither is the same as an AI system omitting candidates from its answers. Whether AI systems provide complete and balanced information about Malaysian candidates should be tested across constituencies and languages, measuring candidate coverage, factual accuracy and source quality. The Dutch findings justify conducting those tests; they do not establish that Malaysian systems reproduce the same pattern. Any numerical comparison of Malaysian constituencies should identify the election year, cite official Election Commission electorate figures and show how the disparity is calculated.
In Layman's Terms (Visualize This): You walk into a massive supermarket (the AI) to buy groceries (political information). You want local, farm-fresh produce. But the store's systems have left local items out of sight in the back warehouse and only stocked the main aisles with two giant national brands. You end up buying a national brand, thinking it was your own free choice.
4. The Decentralisation Dilemma: The Open-Weight Models and Enforcement”
Regulatory approaches vary, but safeguards aimed at major AI providers cannot address every form of misuse. Some open-weight models can be run locally, potentially allowing users to generate synthetic political material without relying on a major commercial chatbot. The practical barriers vary with hardware, model capability and technical expertise. This complicates enforcement when content is generated outside a provider's hosted service, but it does not mean every local model is unrestricted or that oversight is impossible.
In a July 2026 opinion piece, Chithra Latha Ramalingam argued that Malaysia's proposed AI Governance Bill may not adequately address election-related deception. She questioned whether a risk framework that prioritises physical harm would fully recognise societal harms, including misleading voters. With GE16 ahead, her argument reinforces the case for examining these gaps before the next campaign.
A Malaysian case illustrates the importance of distinguishing allegations of AI-enabled election deception from independently verified findings. During the July 2026 campaign for the Chuah by-election in Negeri Sembilan, MCA alleged that AI-generated images and doctored material were being used to link its candidate to a viral cash-handout video. The Star reported that the candidate lodged a police report on July 29 and noted that the underlying cash-handout footage remained unverified at the time of publication.
This incident highlights the potential for disputed or manipulated media to become part of an election controversy. However, the report alone does not establish that all the disputed material was AI-generated, who created it, or whether an investigation subsequently confirmed the allegations. Those questions require separate verification.
In Layman's Terms (Visualize This): Regulators are trying to stop bad information by controlling major broadcasting towers. But some technically capable users can run their own pirate radio stations from home. The government may be guarding the front door while new side doors keep opening.
Building Civic Resilience: Moving Beyond Doomerism
It is easy to look at these behavioural vulnerabilities and adopt a fatalistic "doomer" mindset. But human cognition is not a lost cause; it is a system that requires updating. We cannot rely solely on legal bans or tech companies to save democratic autonomy.
As the Malay proverb puts it, “bagai menghela rambut dalam tepung: rambut jangan putus, tepung jangan terserak.” DBP records this proverb as a reminder that difficult matters must be handled with great care. That is precisely the balance election safeguards must strike: addressing AI-enabled deception without unnecessarily damaging legitimate political speech, fair competition or due process.
Expert advice for 2026 points toward immediate, proactive resilience:
- Immediate Electoral Intervention: Dr. Chithra Latha Ramalingam argues that Malaysia cannot wait for overarching AI bills to pass. Her recommendations include requiring labels on AI-generated political campaign material, assigning an accountable person to each political avatar, and asking the Election Commission to act before GE16 rather than waiting for the proposed bill to pass.
- A Multipronged, Defensive Approach: At an August 2026 roundtable convened by Singapore's S. Rajaratnam School of International Studies, panellists concluded that no single technical solution could adequately address disinformation. They advocated a multipronged approach combining AI-based and non-AI-based defence.
- Build Algorithmic Literacy: Discussing the 2026 Israeli elections, expert Haleva Amir warns that we cannot regulate our way out of this: "Laws will not be enough or sufficient because technology always exceeds the law... The real answer to all these things should come from the people…by raising awareness and educating people and giving them tools to criticise and reflect."
