Thanks to many comments, esp. Ravi Iyer, Sahar Massachi, Tal Yarkoni, Rafael Burde, Grady Ward, Ines Moreno de Barreda, and Daniel Quigley.
Summary
It is difficult to anticipate the effect of AI on online communication. AI models have already had big effects on content moderation but they are now starting to have effects on content production, e.g. through generating synthetic spam and deepfakes. How is the balance likely to play out?
This note discusses the likely effect of AI on online communication, summarized with a set of predictions:
- The prevalence of policy-violating content on platforms will decline. Classifiers are approaching human-level performance and so the prevalence of policy-violating content (e.g. nudity, hate speech) should decline to very low rates. The same argument applies to censorship: governments will be able to near-perfectly identify messages that include a forbidden sentiment.
- The prevalence of “context-specific” violating content will increase. This refers to content which appears to be non-violating to the average person but is interpreted in a policy-violating way by its intended audience.
- The prevalence of variations of known-violating content will decrease. Content that is a match against databases of illegal sexual media (PhotoDNA), IP-protected content (ContentID), or terrorist recruitment content (GIFCT) will become less frequent because AI will make obfuscation harder.
- Platforms will not be able to identify bots from their behavior. Behavioral tests like CAPTCHAs will become ineffective against sophisticated actors and so platforms will have to rely relatively more on direct proofs of identity.
- People will not be able to discriminate between real and fake media based only on the content. Since the invention of audio and visual recording in the 19th century the existence of a recording has been evidence for the event depicted having occurred. The evidentiary value of media will become much less strong because it will be impossible for a human to discriminate between real and fake.
- Platforms will not be able to discriminate between real and fake media based only on the content. It seems unlikely that AI classifiers will be able to reliably detect AI-manipulated media.
- Fake media will not have a substantial influence on politics. It has always been possible to forge documents but the influence of forgeries on politics has been limited. Documents have always been evaluted not just on their content but their provenance. Advances in the ability to forge media seem likely to be accompanied by advances in skepticism and greater reliance on provenance.
- Computer-generated content will not be hyper-persuasive. LLMs with their current architecture are unlikely to produce text that is more persuasive than a skilled human could produce. Social scientists have warned about “hyper persuasive” technologies for a century and been wrong every time.
- Communication will migrate further towards large closed platforms. The rise of synthetic content will make it harder for individuals to discriminate between reliable and unreliable information sources, and so will likely to increase the demand for intermediaries, which implies greater demand for verification and closed platforms.
- Things will get weird. Our intuitions about meaning and interpretation are based on experience with human-created representations. When computers can generate representations then strange things are likely to happen.
A useful distinction: internal properties vs external properties. A common thread in this argument is a distinction between two types of property:
- An “internal” property of a message is a function solely of the content, e.g. whether an image contains nudity, whether text contains hate speech, whether a joke is funny. These properties hold independent of any outside facts. AI classifiers are rapidly approaching human-level accuracy for these properties and this means that platforms (and governments) will be able to near-perfectly filter by internal properties even if content-producers have access to the same technology.
- An “external” property of a message depends on some fact outside the message’s content: e.g. whether an image was computer-generated, whether a headline is true, whether a message came from a specific person. Platforms will get better at predicting external properties but they will be outpaced by motivated actors who can manipulate their fakes until they become indistinguishable from genuine articles, and able to manipulate lies so they’re indistinguishable from the truth.
This note additionally has short appendices on the history of forged documents and on relevant forecasting questions from Metaculus.
What I don’t talk about. Two important topics that I do not discuss here: (1) super-intelligent AI and alignment; (2) social and cultural biases in AI.
Predictions
Background on AI. Before discussing the predictions I note a couple of facts about recent progress in computer abilities:
- Computers surpassed human abililties at mathematical calculations in the mid 20th century.
- Over the last 50 years computers have been steadily getting better at recognizing text and images, with particularly rapid progress since around 2012. Today computers can match the best-performing humans on many classification tasks.
- Since around 2018 computers have additionally become good at synthesizing media, e.g. creating text, images, or video, to match a description.
The graph above from Kiela et al. (2023) shows the very recent history of computer performance: AI systems are regularly achieving human performance on benchmark tasks within a year of them being introduced, and so new benchmarks are being introduced more frequently.
The Prevalence of Policy-Violating Content Will Decline
All large internet platforms use automated systems to detect policy-violating content. All major platforms ban or suppress various types of content, e.g. hate speech, incitement to violence, nudity, graphic content. It has not been practical to have a human review each message because the platforms have a high volume of messages being sent with low latency. However automated systems have always been used: early systems simply checked for the appearance of prohibited words or matched against media databases, later systems used classifiers trained on human labels.
Simple classifiers have high offline accuracy. Simple classifiers which just look for the appearance of specific words are often useful, e.g. certain words and phrases are highly predictive of whether text would be labelled as “toxic” or “hate speech.” However this method has many false positives (Chen (2022)) and false negatives (Heiner (2022)). Note that these classifiers typically have higher performance offline than online: once they are used in production then users will alter their messages to evade them.
Simple classifiers are easily evaded. It is typically easy to alter a violating message such that humans still think it is violating, but the classifier does not.
- Gröndahl et al. (2018) note that hate speech detectors can easily be fooled if you “insert typos, change word boundaries or add innocuous words.”
- Han and Tsvetkov (2020) note that simple models are poor at detecting “veiled toxicity” which they define as including “codewords, novel forms of offense, and subtle and often unintentional manifestations of social bias such as microaggressions and condescension.”
- A. Lees et al. (2021) note that simple models are poor at detecting “covert toxicity” which includes “types of toxicity that may not be immediately obvious. Covertly toxic comments may use obfuscation, code words, suggestive emojis, dark humor, or sarcasm …[and] [m]icroaggressions.” Both papers train models to identify toxicity where the ground truth is human rating of the text alone, without additional information on context or audience.
LLM-based classifiers are approaching human levels of performance. In August 2023 OpenAI described using GPT-4 as a content labeler (Weng, Goel, and Vallone (2023)) and said “[l]abeling quality by GPT-4 is similar to human moderators with light training … [h]owever, both are still overperformed by experienced, well-trained human moderators.”
LLM-based classifiers handle adversarial cases well. Google’s 2022 generation of text moderation models, which use transformer-based LLMs, are able to correctly classify many types of adversarial messages which are designed to evade simpler classifiers. A. W. Lees et al. (2022) say their classifier performs well against “code-switching, covert toxicity, emoji-based hate, human-readable obfuscation, [and] distribution shift.” Google’s 2023 generation spam classifier uses an embedding that is “robust against typos and character-level adversarial attacks” (Bursztein et al. (2023)).1
1 Arnaud Norman writes about how algorithms to scrape email addresses are often easy to evade, by adding special characters or other obfuscations, but that ChatGPT can straight-forwardly decode most such obfuscations.
Better classifiers will lower prevalence even if available to adversaries. Suppose an adversarial content-producer had access to the classifier used by the platform. They could keep testing different variants of a violating post until they found a variant that was not identified as violating, i.e. a false negative. However as the platform’s model becomes more accurate there will be fewer possible false positives, and so the task becomes relatively more time-consuming for the adversary, and so we should expect prevalence to decline.
The prevalence of policy-violating content has declined dramatically. Meta reports that the prevalence of nudity, bullying, hate speech, and graphic content each declined by a factor of between 2 and 5 between 2017 and 2022, and that the share of identified-violating content that was first identified by an ML model (“proactive rate”) is approaching 100% for most categories. I think much of this decline can be attributed to improvements in the quality of classifiers.2 Mark Zuckerberg has been making predictions for a long time that human raters could be substituted with AI. Although he was over-optimistic about the pace, I think he has been largely correct, e.g. in late 2018 he said “through the end of 2019, we expect to have trained our systems to proactively detect the vast majority of problematic content.”
2 It is important to remember that the “proactive rate” is the share of detected content that is detected by AI, the share of violating content that is detected by AI will certainly be significantly lower but is not generally reported. See Meta’s Community Standards report and my visualization of this data.
Employment of human moderators will likely decline. As computer accuracy improves fewer messages will need to be escalated for human review, additionally fewer humans will be needed to label training data.
This prediction also applies to government monitoring and censorship. Many governments use some kind of automated scanning tools to intercept or censor messages based on their content, e.g. the US’s NSA and Chinese government. Better AI will allow these agencies to classify every single post with reliability as high as if they had a human read each one, thus we should expect obfuscation will no longer be an effective workaround for censorship.
This prediction would fail if there were hard limits on the performance of AI. It’s conceivable that there are ways of obfuscating content that will remain difficult for an AI to identify for a long time. However even in this case I expect the windows to be relatively small. Suppose humans invent new types of obfuscation, e.g. misspelling words in a particular way, then an AI which is continually trained on human-labeled samples could likely learn the pattern and thus force humans to adopt a new pattern.
Whether prevalence will decline all the way to zero depends on the definition of prevalence used. An AI model can never perfectly predict human-rater evaluation because humans are themselves noisy: there is both between-rater variation and within-rater variation in labelling for any given piece of content. Thus whenever the ground truth is human judgment then even an infallible classifier could not be used to drive prevalence all the way to zero.
The Prevalence of Context-Specific Violations Will Increase
Some messages have a violating significance only to their intended audience. We can define a message as violating in one of two ways: (1) has a violating significance to the average person (average citizen or average user), or (2) has a violating significance to the intended audience of that message. I will define a “contextual violation” as a message that is violating to its intended audience but not to the average person. This is stronger than just having a double meaning where both meanings are clear to all audiences. I am specifically talking about messages which are interpreted in distinct ways by different audiences. Of course contextual violations are often unstable, over time the average person will often learn the contextual meaning.
Many messages use contextual violations.3
3 A related phenomena is people using selective truths to give an impression that is false. E.g. it is common for anti-vaccination groups to post mainly true claims, but only those claims which reflect badly on vaccines. People with a bias against some ethnic group likewise often refrain from posting provably false claims but post only those true claims that reflect badly on the disliked group. Because the pool of claims that are true is enormous it is easy to collect a large set of true claims that collectively give a false impression.
- Saying “globalist” when your audience understands it to mean “jewish”
- Saying the opposite of what is meant, e.g. a bigot saying excessively positive things about an ethnic group, or a pro-anorexia poster making anti-anorexic statements sarcastically.
- Using euphemisms for illegal substances or illegal acts.
- Using emojis of eggplants and peaches with sexual connotations.
- Using photos without explicit nudity but which can be read as pornographic.
Improved detection of violations is likely to cause substitution towards contextual violations. As AI improves the ability to detect violations it seems likely that there will be at least some substitution towards context-specific violations, however as long as there is some cost to using a contextual-violation then we would expect a less than one-for-one substitution.
Platforms could detect contextual violations if they wanted to. When doing human evaluation then platforms could either (1) provide human raters with detail about the message’s context and audience, or (2) assign human raters to messages based on their experience with that community.4 Likewise AI models could be trained to include rich representation of the context. An additional advantage of adding context is that it can identify and exempt posts that violate the letter but not the spirit of the policy.
4 Platforms already have some policies that include context, e.g. Facebook’s “Bullying and Harassment policy” bans “repeatedly contacting someone in a manner that is unwanted or sexually harassing.”
Platforms may not want to remove contextual violations. There are reasons why platforms may be reluctant to use context in determining violations: it is more complex, and can lead to awkward PR where the platform is shown to be censoring words and images have a harmless interpretation. Additionally platforms may care more about being seen to restrict harmful content than about the actual harm prevented.
Contextual violations have long existed in broadcast media. There have been many cases where contextual violations have been tolerated: e.g. newspapers would allow classified advertisments for prostitutes if described as masseuses, vibrators if described as massage wands, contraception if described as marital aids, and abortion if described as “removal of obstructions”. Thus it seems plausible that platforms will tolerate a substantial amount of contextually-violating content to remain.5
5 In Facebook’s Marketplace it is prohibited to list guns for sale. As a consequence people began to list gun cases, with the understanding that a case was standing in for a gun. Facebook then updated their policy to prohibit selling gun cases. In turn people began to list gun stickers as stand-ins for guns. See WSJ reports from 2020 and 2021.
Government censorship is unlikely to be constrained by context-specific violations. Once a censor discovers that a term has an anti-government significance in a certain context then they are likely to start censoring that term. E.g. China has suppressed online mentions of Winnie the Pooh because it is associated with criticism of Xi Jinping, and in 2022 Hong Kong police arrested protestors for holding blank pieces of paper.6
6 https://www.axios.com/2022/11/28/china-protests-blank-paper-covid
The Prevalence of Variants of Known-Violating Content Will Decline
Platforms typically check content against databases of known-violating content. In addition to running classifiers on content platforms also check content against databases of known-violating content. The databases are often shared across platforms, known as “signal sharing”, e.g. databases of illegal sexual media (PhotoDNA), IP-protected content (Content ID), or terrorist recruitment content (GIFCT).7 As a consequence sophisticated uploaders often obfuscate their content, e.g. by adding noise, and platforms expand their matching algorithms using fuzzy matching.
7 Other signal sharing programs: National Center for Missing & Exploited Children Child Sexual Abuse Material (NCMEC-CSAM), Non-consensual Intimiate Imagery (StopNCII), ThreatExchange.
Improvements to AI will help platforms relatively more. Here the ground truth is whether a piece of content is a variation of another piece of content.
Platforms Will Not Be Able to Identify Bots from Their Behavior
Most online platforms struggle with automated users (bots) who are disruptive in a variety of ways. One way of protecting against bots is with behavioral tests, e.g. a CAPTCHA test asking users an image-recognition task8, or by using on-platform behavior to detect whether a user is human. However improvements in AI mean that computers have human-level performance on image-recognition tasks, and can learn to imitate human-style behavior patterns, thus it seems likely these behavioral tests will become ineffective against sophisticated actors. Searles et al. (2023) finds that most contemporary CAPTCHAs can be solved by computers with higher-than-human accuracy (p10).9
8 CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart.
9 Similarly behavioural fingerprinting will become ineffective against advanced actors, e.g. using voice recognition to verify identity.
This does not imply that the prevalence of bots will increase. All platforms need some defense against bots so they will have to rely relatively more on other forms of authentication, such as monetary payment, offline identity credentials (government ID, credit card number), hard-to-fake metadata (unique IP address, device ID), or 3rd-party identity provider (Sign in with Google, OpenID).10 Thus the barriers to signing up for a service, and especially posting on it, will become higher, but the effect on equilibrium prevalence of bots is ambiguous.
10 The 3rd-party identity providers will themselves have to rely on some other ground truth when accepting signups.
Platforms Will Find It Hard to Discriminate between Real and Fake Media
In some cases the ground truth depends on properties outside the content. I will refer to these properties as “external” in contrast to “internal” properties which depend only on the content such as whether a picture depicts nudity. I discuss the distinction at greater length below. Some examples of external properties:
- Whether a piece of media was generated in the traditional way (photographing a scene, recording a sound), or has been manipulated or synthesized.
- Whether text was written by a human.
- Whether text was written by a specific person, e.g. by Shakespeare.
Advances in AI will help with both forgery-detection and forgery-creation. It is clear that a better statistical model of genuine artefacts will help detect forgeries but it will also help create convincing forgeries.
Determined forgers will be able to fool humans. It seems likely that the latter effect will dominate: it will gradually become possible to camouflage computer-generated content such that neither a computer nor a human could tell them apart. If the content-producer has access to the platforms’ model then they can keep perturbing their fake media until it is labelled as non-fake.
We cannot reliably discriminate between real and AI-generated media. As of late 2023, programs to detect synthetically generated media have relatively poor accuracy: OpenAI announced a model to detect LLM-created text in January 2023 but then shut it down in July because of poor performance. In June 2023 the NY Times compared a variety of tools to detect computer-generated images and found that with minimal effort they could all be reliably fooled.
The prevalence of synthetic media will increase on unmoderated platforms. The major platforms have incentives to limit the prevalence of fake media,11 and can control the prevalence even without reliable classifiers. E.g. Meta and YouTube dramatically decreased the prevalence of misinformation over 2016-2020 not primarily through real-time detection of whether a given claim is false, but by (1) adjusting ranking to penalize publishers who tend to circulate false claims; (2) punishing publishers who circulate proven-false claims. Thus I do not expect overall prevalence of fake factual media to substantially increase on the major platforms.
11 The goals of platforms in content moderation are discussed in my note on ranking, Cunningham (2023).
Fake Media (Deepfakes) Will Not Have a Substantial Influence on Politics
As synthetic media becomes common people will rely more on provenance. As it becomes cheaper to manipulate and synthesize media then people are likely to become more skeptical and rely relatively more on the provenance of information. Thus although synthetic media will likely circulate I do not think it will have a substantial influence on beliefs in equilibrium.
It has always been easy to create misleading documents.It is not difficult to forge or alter documents, or edit video in a misleading way. As a consequence mainstream media organizations typically do not publish leaked materials unless they have either a chain or provenance for the leaks or independent confirmation of their content.
Influential forgeries of documents have been historically rare. In an Appendix below I compile a simple dataset of politically influential document leaks in the US over the past 25 years and estimate around 10% of them were based on forged materials.1213
12 I know of two forged documents that were widely taken as true in the last 25 years, from around 15 substantial leaks that I could find: (1) the “yellowcake” letters from Iraq to Niger, cited in the 2002 US case for war against Iraq; (2) a fake G W Bush military transcript reported on by CBS and Dan Rather in 2004. It’s notable both that these cases are somewhat rare, and that each was passed through a chain of apparently reputable parties.
13 This argument implies that, prior to AI, anonymously leaked video would be more likely to be published and circulated than anonymously leaked documents, because video is harder to fake. In fact I cannot think of many cases of influential anonymous leaks of videos. When Trump’s “Access Hollywood” tape was leaked to the Washington Post they got confirmation before publishing it. In fact maybe leaked video has always been untrustworthy because it has always been easy to make deceptive edits.
The quantity of false claims circulating on the internet is not primarily constrained by the quality of their content. A great deal of false claims already circulate on the internet, especially in loosely moderated parts: e.g. by email, on Telegram, 4chan, Truth Social, WhatsApp, Twitter. It’s not clear that the quality of the faked media is an important constraint on the volume that circulates. It’s not uncommon to find a clip of an interview with a politician edited to make it appear that they are admitting to a crime or secret agenda.14 If people already take what they see at face value then adding deepfakes seems unlikely to change their opinions substantially. Alternatively if people are skeptical and look for corroborating sources then, again, deepfakes would be unpersuasive. It seems that deepfakes would only be influential if there are a significant population who are exposed to many lies but are not persuaded because the documentary evidence is not sufficiently strong.
14 Snopes.com has an enormous database of both false claims and misleadingly manipulated media that has circulated since 1994. A typical recent example is an edit of a Bill Gates interview to make it appear he wants to use vaccination to reduce population growth.
Communication Will Migrate Towards Large Closed Platforms
Small platforms will be overrun with AI-created content. In particular, AI-created bots, spam, obfuscated violating content, and fake media. This would imply that consumers will tend to migrate to larger closed platforms with more effective defences, and which have more restriction on participation. This continues a general movement over the last 20 years of communication moving from small open platforms (independent email, small forums, mailing lists, independent websites) to large closed platforms (large email providers, large social media platforms).
People will rely more on established sources of truth. E.g. they will rely relatively more on Wikipedia, Community Notes, and mainstream recognized media sources. The ordinary content-based signs of trustworthiness will become less reliable: having a professional website, well-edited text, well-argued reasoning, and documentary evidence.
People will rely more on cryptographic signing to verify authenticity. I am not sure how strong this effect will be: it is generally more efficient for an intermediary to verify authenticity of senders than for users to do it themselves. I think we’ve seen that in other domains: (1) PGP signing of email has been less important than email providers filtering spam and phishing; (2) SSL certificates in browsers have been less important than browsers giving warnings for suspected phishing sites (e.g. Google’s safe browsing database of sites with phishing or malware is used to give warnings in Chrome and Safari).
Pedigree will become more important in publication. As an editor accepting submissions (e.g. an academic journal, a literary magazine, a newspaper letters page) the quality of the work submitted is typically correlated with more superficial features such as the grammaticallity and the length. As it becomes easy to synthesize text then those superficial features will become less informative about quality and editors are likely to rely relatively more on hard-to-fake signals like the pedigree of authors: what have they published before, and which college the author went to.
Entertainment will Become Largely Synthetic
A classifier that can detect whether a photo is pretty can also generate a synthetic photo that is pretty, and a classifier that can detect whether a joke is funny should also be able to generate funny jokes.15 On average people spend around 3 hours per day watching entertainment (TV, YouTube, TikTok, Instagram). It seems likely that trained models will be able to synthesize content that is highly engaging though it’s hard to anticipate what it will look like.
15 I think language models haven’t yet been very good at jokes because they generate one word at a time (autoregressive), while jokes typically have a logical structure such that the setup is probable given the punchline, but not the other way around. When we get language models which generate text using different statistical algorithms (e.g. diffusion instead of autoregressive generation) then it seems likely they’ll be able to create good jokes.
Things Will Get Weird
Much of our common-sense understanding of media will be violated when we routinely use AI models to manipulate and synthesize artefacts. Some examples:
People will synthesize completely new violating images/videos. Thiel, Stroebel, and Portnoff (2023) say that, as of early 2023, less than 1% of child sexual abuse media (CSAM) appears to be synthetically generated. However the ability to synthesize has been advancing rapidly, “to the point that some images are only distinguishable from reality if the viewer is very familiar with photography, lighting and the characteristics of diffusion model outputs … it is likely that in under a year it will become significantly easier to generate adult images that are indistinguishable from actual images.”
Producers will synthesize content to sit on the edge of a category. If platforms take action whenever content passes some threshold then adversarial actors will generate or perturb content such that it sits right below the threshold. If a platform removes a photo whenever more than 50% of raters would say it depicts nudity then producers would upload photos which 49% of raters would say depicts nudity. People would upload movies which almost look like an existing IP-protected movie, and students might submit essays that are close to existing sources but don’t quite trigger the plagiarism detector.
Appendix: Historical Observations on Forgeries
Influential leaks of US political documents since 1997:
| Tripp Tapes | 1997 | audio | Linda Tripp to Kenneth Starr |
| [FORGERY] Iraq letters to Niger (“yellowcake”) | 2002 | documents | Unknown to Italian intelligence to CIA |
| [FORGERY] Bush military transcripts (“Killian”) | 2004 | fax of 1970s memo | Unknown to retired colonel to Dan Rather / CBS |
| Abu Ghraib photos | 2004 | photos | Unkown to CBS |
| Baghdad Airstrike (“Collateral Murder”) | 2007 | video | Chelsea Manning to Wikileaks |
| US Iraq war logs | 2010 | digital docs | Chelsea Manning to Wikileaks |
| US Diplomatic cables | 2010 | digital docs | Chelsea Manning to Wikileaks |
| Romney Fundraiser Tape (“47%”) | 2012 | audio | Bartender to Mother Jones |
| NSA Surveillance Leaks | 2013 | digital docs | Edward Snowden to the Guardian, WaPo |
| DNC emails | 2016 | emails | Unknown to Wikileaks |
| Podesta emails | 2016 | emails | Unknown to Wikileaks |
| Colin Powell emails | 2016 | emails | Unknown to DCLeaks |
| Panama papers | 2016 | documents | Unknown to Süddeutsche Zeitung |
| Donald Trump Access Hollywood Tape | 2016 | video | Unknown to Washington Post |
| China Cables | 2019 | digital docs | Unknown to the ICIJ |
| Hunter Biden laptop | 2020 | docs,audio,video | computer shop to Giuliani to NY Post |
| Los Angeles Council call (“changuito”) | 2022 | audio | Unknown to Reddit to LA Times |
Why are forgeries not more common? I can think of three possible reasons:
- It’s difficult to forge credible documents – e.g. even a simple memo is hard to fake because there are lots of small details like the letterforms and formatting and jargon used.
- It’s easy to forge credible documents but intelligence agencies and the media won’t believe them without independent confirmation.
- It’s easy to forge credible documents and for them to get coverage but not many people are motivated to try.
Other examples of influential forgeries.
| Mark Antony’s will | 33BC | read out by Octavian in the senate (disputed whether forgery) |
| Dreyfus letters sharing military info w Germany | 1894 | fabricated by French military |
| Protocols of Elders of Zion (Jewish plans for domination) | 1903 | |
| Castle Document, letter to British govt in Dublin | 1916 | unclear source, contributed to 1916 Easter rising |
| Zinoviev letter from Russia to UK Labour party | 1924 | unclear source |
| Tanaka Memorial (Japanese plans for world domination) | 1929 | unclear source |
| Macron emails | 2017 | emails |
Notes.
The Macron email leaks seemed to include both real and fake content (ref). In fact one report says that Macron’s team sent each other outrageous implausible emails as a pre-emptive defense to make any subsequent leaks seem less credible (ref).
The Steele dossier isn’t really a forgery: it doesn’t purport to have a different author than its true author. The problem with the dossier is that the author makes knowingly false claims of fact.
Appendix: Relevant Forecasts from Metaculus
| deadline | Metaculus Jun 30 2023 | |
|---|---|---|
| Will a deepfake cause damage & make front page of a major news source? | 2023 | 89% |
| Will a deepfake be blamed by G20 politician for election loss? | 2025 | 80% |
| Will AI be used in an attack on infrastructure costing >$1B? | 2025 | 4% |
| Will AI be used in a theft of intellectual property cost >$10M? | 2025 | 30% |
| Will AI cause a stock exchange to halt trading for >24 hours? | 2025 | 15% |
| Will AI be used in a major attack on voting systems in G20? | 2025 | 10% |
| Will a deepfake about politicial running for office get 2M+ views? | 2018 | (resolved false) |
| Will a wide-scale video hoax put words in a famous figure’s mouth? | 2017 | (resolved false |