How Do Misinformation and Disinformation Differ, and How Has AI Changed Them?

Key Takeaways

• Misinformation is usually wrong by error; disinformation is wrong by design.
• AI lowers the cost of false text, images, audio, video, and fake identities.
• Verification now depends on source checks, provenance, platform rules, and judgment.

How Misinformation and Disinformation Differ

On January 21, 2024, some New Hampshire voters received robocalls that used an artificial intelligence (AI) voice clone of President Joe Biden to discourage participation in the state’s Democratic primary. The Federal Communications Commission adopted a $6 million fine against political consultant Steve Kramer on September 26, 2024, for illegal robocalls using deepfake generative AI voice technology and caller ID spoofing. That event illustrates why the difference between misinformation and disinformation matters. The audio was false, but the more important issue was intent. It was designed to mislead voters, not accidentally shared by someone who misunderstood a fact.

Misinformation refers to false or misleading information shared without intent to cause harm. Disinformation refers to false or misleading information spread deliberately to deceive, manipulate, or cause harm. The Canadian Centre for Cyber Security uses a similar distinction: misinformation lacks harmful intent, disinformation is designed to manipulate, damage, or misguide. A mistaken post about a public-health rule, a wrong date for a launch, or an outdated image shared as current news may be misinformation. A coordinated campaign that knowingly spreads fabricated documents, fake audio, or invented claims is disinformation.

The distinction does not always appear clean from the outside. A person can share disinformation without knowing that a falsehood began as part of an organized campaign. That later sharing becomes misinformation at the individual level, even though the original operation remains disinformation. A false claim can also move between categories as it travels. A fabricated image created by a malicious actor can become misinformation when ordinary users repost it in good faith.

Malinformation adds another category. It uses information based on reality, but presents it in a misleading or harmful way. Examples include selective leaks, exaggerated fragments of truth, and material stripped of context. Malinformation matters because much public manipulation does not depend on total fabrication. A campaign can take one real photograph, one real quote, or one real financial figure and attach it to a false story.

AI has sharpened all three categories. It can produce convincing falsehoods for disinformation campaigns, help ordinary users generate wrong content by mistake, and make decontextualized material look more authoritative than it deserves. The technology does not create the motive. It changes the speed, cost, realism, and scale of information production.

Why AI Changes the Cost of False Content

Before generative AI, large influence campaigns required more human labor. Operators needed writers, translators, graphic designers, video editors, account managers, and technical support. AI systems reduce the labor needed to create believable text, synthetic images, fake audio, and fabricated personas. A small team can now create more content, in more languages, with more stylistic variation. That does not guarantee persuasion, but it increases the volume of material that journalists, platforms, election officials, researchers, and ordinary users must assess.

The change is most visible in synthetic media. AI image generators can create a fake photograph of a political event, a disaster, or a military incident. Voice-cloning tools can imitate public figures. Video systems can create convincing clips that look like camera footage. Large language models can write comments, articles, scripts, emails, and posts that resemble human writing. A Google DeepMind study published in August 2024 reviewed nearly 200 public generative AI misuse incidents reported from January 2023 through March 2024 and found repeated patterns involving impersonation, synthetic media, and public-opinion manipulation.

Cost reduction affects misinformation as well. AI systems can produce confident but wrong answers, fabricate details, blur dates, invent sources, or mix real facts with inaccurate context. Users may then copy that material into articles, emails, social media posts, presentations, or policy discussions. In that case, the person sharing the information may not intend to deceive. The harm can still be real because the output looks polished and may carry the style of authority.

New Space Economy has treated this pattern as an information-integrity issue in space markets. Its article on misinformation and the space economy notes that AI can create realistic-looking images, maps, dashboards, and written claims. That point applies beyond space. When false material imitates evidence, it can distort investor judgment, public debate, emergency response, and policy decisions.

AI also weakens some older credibility cues. In the past, poor grammar, awkward translation, rough photo editing, or low production quality could expose fabricated content. Better tools now let weaker actors produce smoother material. A fake local news site can publish hundreds of plausible articles. A scam can use a synthetic voice. A social media operation can generate biographies and profile images for fake accounts. The content may still contain clues, but those clues are harder for casual viewers to detect.

The scale problem is not only about creation. AI can help test messages, adapt them to audiences, translate them, summarize trending narratives, and generate variants. A campaign can try many versions of a claim, keep the phrasing that attracts attention, and discard the rest. That pattern makes disinformation resemble digital marketing, with narrative testing replacing honest persuasion.

Specific Examples Show AI’s Uneven Effects

The 2024 New Hampshire robocall remains one of the clearest examples of AI-enabled political disinformation because the deception, channel, target, and enforcement response are well documented. The voice clone impersonated a sitting president and gave misleading election-related guidance. The Federal Communications Commission ruled on February 8, 2024, that AI-generated voices in robocalls fall under existing restrictions on artificial or prerecorded voices. That decision treated the technology as new, but the legal category as familiar: deceptive robocalls were already regulated.

Slovakia offered a different lesson. Two days before the September 30, 2023, parliamentary election, an audio recording circulated that purported to feature Progressive Slovakia leader Michal Šimečka and journalist Monika Tódová discussing election manipulation. A Harvard Kennedy School Misinformation Review article published on August 22, 2024, cautioned against simplistic claims that the disputed audio alone swung the election. The case still showed how audio can be hard to debunk during a short, tense election window.

In May 2023, a fake image purporting to show an explosion near the Pentagon circulated on social media and briefly unsettled financial markets. The Associated Press reported that Arlington police and fire officials said no incident had occurred at the Department of Defense headquarters. That example shows how a single synthetic or apparently synthetic image can create market confusion when it appears to show a national-security event. The claim did not need long-term believability. A short burst of uncertainty was enough to matter.

The Russian “Doppelganger” operation shows the institutional version of the problem. On September 4, 2024, the United States Department of Justice announced the seizure of 32 internet domains used in Russian government-directed foreign malign influence campaigns. The department said the operation used cybersquatting, fabricated influencers, fake profiles, and AI-generated false narratives on social media. The case connected AI-generated material to older tactics: fake domains, cloned media brands, paid ads, and covert state sponsorship.

Canada’s 2025 democratic-process threat update placed AI in a broader threat pattern. The Canadian Centre for Cyber Security reported that 40 of 151 national-level and European Union parliamentary elections from 2023 through 2024 were targeted by actors using generative AI to create or spread disinformation at least once during the 12 months leading up to the election. The agency also identified 60 unique synthetic disinformation campaigns and 34 known and likely cases of AI-enabled social botnets. The same update cautioned that AI changes how disinformation is created and spread, but not the motives behind it.

These examples point in different directions. AI can impersonate a candidate, fabricate a crisis, clone a media outlet, accelerate a bot network, or help a user produce polished inaccuracies. No single example proves that AI controls public opinion. The stronger finding is narrower and more defensible: AI makes deception cheaper, faster, more realistic, and harder to triage at scale.

How AI Helps Detection and Verification

AI is not only a production tool for false material. It can also assist detection, moderation, verification, translation, clustering, and forensic review. Platforms use automated systems to identify spam networks, detect duplicate claims, flag manipulated images, and prioritize content for human review. Researchers use machine learning to map influence networks, compare repeated narratives, and detect suspicious coordination. Newsrooms use AI-assisted tools to search archives, verify image reuse, and check whether a claim matches known evidence.

This defensive use has limits. Detection models can miss new formats, produce false positives, or fail against adversaries who test content before release. Audio deepfakes can be difficult to analyze quickly. A synthetic video can spread before technical review catches up. Content may also pass through screenshots, compression, cropping, translation, and reposting, which can strip away useful metadata. Verification is strongest when it combines technical tools with institutional routines: source checking, local confirmation, reverse image search, domain registration review, forensic analysis, and editorial judgment.

Provenance standards address the problem from another direction. The Coalition for Content Provenance and Authenticity provides an open technical standard for recording the origin and edit history of digital media. Content Credentials gives viewers a way to inspect provenance information when participating tools preserve it. Provenance does not prove that content is true. It can show where a file came from, what tool created it, or how it changed. That makes it harder for synthetic media to masquerade as original camera footage when the provenance chain remains intact.

The main weakness is adoption. A provenance label has little value if devices, software, platforms, publishers, and viewers do not support it. Metadata can also be removed. Bad actors have no reason to preserve honest provenance. That means provenance works best as part of a layered defense, not as a standalone solution. Trusted news organizations, government agencies, companies, and scientific institutions can use provenance to strengthen their own authentic material, but fake content can still circulate outside that chain.

New Space Economy’s article on public concerns about AI places misinformation, deepfakes, and information reliability within a larger trust problem. That connection matters because the goal of many disinformation campaigns is not belief in one false claim. It is exhaustion. If audiences conclude that every image, voice, and document may be fake, bad actors can damage trust even when individual falsehoods are debunked.

Verification needs speed, but it also needs restraint. A rushed debunking can repeat the false claim too widely. A slow debunking may arrive after the claim has shaped behavior. Strong verification systems need prepared channels, trained staff, clear escalation rules, and public-facing corrections that explain what is known, what is not known, and what evidence supports the correction.

Why Motive Still Separates Error From Deception

AI can blur signals of authorship, but motive still separates misinformation from disinformation. A student who uses a chatbot to summarize a science story and accidentally shares a false claim has spread misinformation. A political operative who uses a chatbot to create fake local news articles under invented bylines has created disinformation. A company that uses AI to overstate product performance may move from hype into deception if it knows the claims are false.

Motive is hard to prove from one post. Investigators look for patterns: coordinated timing, repeated narratives, fake identities, domain cloning, payment trails, technical infrastructure, internal communications, and links to known actors. In an October 2025 threat report, OpenAI said it had disrupted and reported more than 40 networks since public threat reporting began in February 2024. A February 2026 OpenAI update said threat actors typically use AI in combination with older tools such as websites and social media accounts rather than relying on one platform alone.

Misinformation can still cause damage without malicious intent. A person sharing a fake emergency image may send others into panic. A blogger repeating an inaccurate medical claim may affect decisions. A financial commentator using an AI-generated false statistic may distort a market discussion. Intent matters for moral and legal responsibility, but impact matters for public harm.

Disinformation campaigns often use layers of unwitting distribution. The originator designs the falsehood. Friendly influencers, partisan accounts, content farms, or ordinary users then spread it. Some know the claim is false. Others believe it. AI helps create many entry points into that chain. A campaign can produce memes for one audience, fake articles for another, video clips for another, and short comments for another. Each piece can carry the same narrative in a different style.

This is why the word “fake” is too simple. False content can be fully fabricated, partly true, wrongly dated, miscaptioned, mistranslated, impersonated, selectively edited, stripped of context, or presented under a false identity. AI can operate in each category. A deepfake may be entirely synthetic. A chatbot-generated article may cite real events but invent the connection between them. A fake account may share real news links in a manipulative sequence.

The New Space Economy article on disinformation campaigns describes how large language models can generate many versions of articles, comments, and posts, creating the appearance of grassroots agreement. That illusion of consensus is one of AI’s most important disinformation effects. It can make a marginal claim look socially accepted before readers have checked the underlying evidence.

How Platforms and Regulators Are Responding

Regulators have started to treat AI-generated deception as a practical governance issue rather than a distant technology debate. The Federal Communications Commission’s robocall action used existing law to address AI voice cloning. The European Union has built a broader framework through the Digital Services Act and the AI Act. The Digital Services Act places risk-management duties on very large online platforms and search engines. The AI Act includes transparency obligations for certain AI-generated and manipulated content.

Regulation faces three problems. The pace of content creation is faster than formal enforcement. Jurisdiction is messy because campaigns cross borders. Definitions must protect people from deception without giving governments broad tools to suppress legitimate speech. A law that requires labeling of synthetic content can help, but labels can be absent, ignored, removed, or distrusted. A platform rule against impersonation can help, but it depends on detection and consistent enforcement.

Platforms face their own tradeoffs. Strict moderation can remove harmful synthetic material faster, but it can also create disputes over political speech, satire, parody, journalism, and legitimate commentary. Loose moderation can leave users exposed to deception. Automated systems can process large volumes, but they lack context. Human reviewers understand context better, but they cannot review everything at machine speed.

The NIST AI Risk Management Framework and its generative AI profile give organizations a way to manage AI risks through mapping, measurement, governance, and mitigation. For information integrity, that means documenting how AI systems may generate false content, how outputs are reviewed, how users are warned, and how incidents are handled. The framework is voluntary, but it gives companies and public agencies a vocabulary for risk management.

Public institutions also need communication discipline. Election agencies, emergency managers, health agencies, courts, companies, and scientific bodies need official channels that the public can recognize before a false claim appears. A correction has more force when people already know where authentic updates will appear. Verification habits cannot be invented during a crisis.

New Space Economy’s coverage of AI risks in 2026 frames information integrity as part of deployment risk. That is the right placement. Misinformation and disinformation are not only content problems. They are operational problems for institutions that rely on trust, timing, and shared facts.

How AI Could Change Information Operations After 2026

AI systems are likely to make information operations more personalized. A campaign that once produced one message for a large audience may produce thousands of variations for smaller groups. Language, tone, examples, images, and messengers can be adapted to local identities or interests. This personalization does not need perfect accuracy about each person. Even rough segmentation can make a message feel more relevant.

Synthetic audio and video will become more ordinary. As people become familiar with generated media in entertainment, advertising, education, and customer service, they may become less shocked by deepfakes. That can help reduce panic, but it can also reduce attention. The danger may shift from spectacular fake videos to ordinary synthetic content embedded in daily feeds, chat threads, search results, and private messages.

AI agents may change the distribution layer. A future campaign may use automated systems to identify trending topics, draft posts, create images, schedule publication, monitor reactions, and adjust tactics. Human operators may supervise the campaign rather than write every message. That could make influence operations more persistent. It could also make attribution harder because the same operator can test more narratives across more platforms.

Search and answer engines create another risk. Many users now ask AI systems for direct answers instead of browsing source pages. If false content enters training data, retrieval systems, or the live web sources that answer engines consult, the falsehood can appear in polished answer form. That turns source quality into a central information-integrity issue. Publishers, agencies, and researchers need clear, machine-readable, current, and authoritative pages so answer systems can retrieve better material.

The space economy shows how this can affect specialized sectors. False claims about satellite imagery, launch failures, spectrum rights, orbital debris, or defense and security applications can influence investors, customers, and policymakers. New Space Economy’s article on AI workloads and orbital data centers shows how AI infrastructure debates already overlap with space systems, data, and public claims. As more markets connect to AI, specialized misinformation will matter more, not less.

AI may also improve defensive capacity. Better tools can cluster repeated claims, compare media against authenticated originals, identify bot-like coordination, flag impersonation, translate foreign-language narratives, and assist fact-checkers. The stronger future defense will not come from one detector. It will come from a chain of practices: authenticated publishing, provenance, platform transparency, user education, legal enforcement, newsroom verification, and institutional readiness.

Forecasts should stay restrained. AI will not make every false claim persuasive. People reject many deepfakes. Some influence operations fail to gain real engagement. The greater risk is cumulative. More synthetic content, more fake identities, more automated distribution, and more confusion about source authenticity can weaken public trust in slow, measurable ways.

Summary

Misinformation and disinformation differ mainly by intent. Misinformation spreads false or misleading material without a deliberate plan to deceive. Disinformation uses false or misleading material as a tool of manipulation. AI has not changed that core distinction. It has changed the machinery around it.

The most important shift is cost. AI makes it cheaper to produce plausible text, synthetic images, cloned voices, fake videos, fake accounts, fake local news, and repeated narrative variations. It also makes accidental misinformation easier when users rely on outputs that sound confident but contain errors. The same technology can support detection, verification, and provenance, but defensive systems need adoption, governance, and human judgment.

The clearest examples from 2023 through 2026 show mixed effects. The New Hampshire robocall demonstrated targeted AI voice deception in an election setting. The Slovakia audio case showed how hard it can be to debunk audio close to election day. The fake Pentagon image showed how a single fabricated crisis can disturb markets. The Doppelganger case showed how AI-generated material can plug into older state-directed influence methods. Canada’s 2025 democratic-process update showed that synthetic disinformation had already appeared across many election settings.

AI’s future effect will depend less on one spectacular deepfake and more on routine synthetic persuasion. Personalized messaging, automated content testing, fake identities, AI-written articles, synthetic audio, and manipulated evidence can merge into ordinary online communication. That makes source verification, provenance, trusted institutional channels, platform accountability, and media literacy important parts of public resilience.

Appendix: Useful Books Available on Amazon

• This Is Not Propaganda
• The Hype Machine
• Network Propaganda
• Active Measures
• LikeWar
• Trust Me, I’m Lying
• Data and Goliath

Appendix: Top Questions Answered in This Article

What Is the Difference Between Misinformation and Disinformation?

Misinformation is false or misleading information shared without a deliberate plan to deceive. Disinformation is false or misleading information spread intentionally to manipulate, damage, or misguide. The same false claim can move between categories when a planned campaign creates it and ordinary users later share it by mistake.

Why Does Intent Matter So Much?

Intent matters because it separates error from deception. A person who shares an inaccurate claim in good faith has created a different problem from an actor who knowingly manufactures falsehoods. Legal, moral, and platform responses often depend on evidence of planning, coordination, impersonation, concealment, or repeated manipulation.

How Has AI Changed Misinformation?

AI can make accidental misinformation look polished and authoritative. A chatbot can produce a wrong answer, invented detail, or inaccurate summary in fluent language. Users may then copy that material into posts, presentations, articles, or emails without realizing that the output contains errors.

How Has AI Changed Disinformation?

AI makes deliberate deception cheaper and faster. It can generate fake articles, synthetic images, cloned voices, fabricated profiles, and many versions of the same narrative. Disinformation campaigns still need strategy and distribution, but AI lowers the labor needed to produce persuasive material.

Are Deepfakes the Main AI Disinformation Threat?

Deepfakes are highly visible, but they are not the whole threat. AI-written articles, fake local news sites, automated comments, synthetic profile images, and targeted messages can be more common. The largest risk may come from routine synthetic content that blends into ordinary online communication.

Did AI Change the Outcome of Major Elections?

Available evidence does not support broad claims that AI alone changed major election outcomes. Documented cases show that AI has affected campaign discourse, voter confusion, impersonation, and enforcement activity. The safer conclusion is that AI has changed the operating environment for political communication.

Can AI Detect AI-Generated False Content?

AI can help detect patterns, repeated claims, coordinated posting, manipulated media, and suspicious accounts. It cannot provide complete protection because adversaries adapt, metadata can disappear, and context matters. Strong verification combines technical tools with human review, official records, and source checking.

What Is Media Provenance?

Media provenance is information about where a digital file came from and how it changed. Standards such as C2PA can attach origin and edit history to images, audio, and video. Provenance does not prove truth, but it can help viewers assess authenticity.

How Should Institutions Respond to AI-Driven False Content?

Institutions should maintain trusted public channels, authenticate official media, prepare rapid correction processes, train staff, and document AI-related risks. Clear communication before a crisis matters because people need to know where reliable information will appear when false claims spread.

How Could AI’s Information Role Change After 2026?

AI could make manipulation more personalized, automated, and persistent. Campaigns may use agents to draft content, test narratives, monitor reactions, and adjust messages. Defensive tools will also improve, but public resilience will depend on provenance, platform transparency, media literacy, and trusted institutions.

Appendix: Glossary of Key Terms

Artificial Intelligence

Artificial intelligence refers to computer systems that perform tasks usually associated with human cognition, such as generating language, recognizing patterns, classifying images, translating text, or making predictions. In this article, AI mainly refers to generative tools that create text, images, audio, video, and synthetic identities.

Botnet

A botnet is a network of automated or semi-automated accounts that can post, share, like, or amplify content. In information operations, botnets can make a claim look more popular than it is and can push narratives into trending spaces.

C2PA

The Coalition for Content Provenance and Authenticity is a standards organization focused on digital media provenance. Its technical standard can attach verifiable information about a file’s origin and edit history, helping viewers assess whether media came from an authentic source.

Deepfake

A deepfake is synthetic or manipulated media that makes a person appear to say or do something they did not say or do. Deepfakes can involve video, audio, or images. Political impersonation, fraud, harassment, and false evidence are common risk areas.

Disinformation

Disinformation is false or misleading information created or spread deliberately to deceive, manipulate, or harm. It can use fabricated material, distorted truth, impersonation, fake accounts, cloned media brands, and coordinated distribution networks.

Generative AI

Generative AI refers to systems that create new content from user prompts or other inputs. Outputs can include text, software code, images, audio, video, summaries, designs, or simulations. Its value comes from production speed, but that same speed can support deception.

Malinformation

Malinformation uses information based on reality in a misleading or harmful way. Examples include selective leaks, stripped context, exaggerated claims, or private information released to manipulate public reaction. It differs from misinformation because the underlying material may be real.

Media Provenance

Media provenance records information about a file’s origin, edits, and handling. It can help distinguish authentic camera footage from synthetic or altered media when the provenance chain remains intact and viewing tools preserve the relevant information.

Misinformation

Misinformation is false or misleading information shared without intent to deceive. It can still cause harm because people may act on wrong information. AI can increase misinformation when users trust fluent machine-generated outputs that contain errors.

Synthetic Media

Synthetic media refers to images, audio, video, or text generated or heavily altered by digital tools. It can serve legitimate uses in entertainment, education, accessibility, and design, but it can also support impersonation, fraud, and disinformation.