Greek discussion partners examine an advertising panel, an empty gift parcel and verification tools beside platform revenue coins.

Liability for Scam Adverts

Section 230 shields platforms from liability for what users post, and the same shield covers adverts a platform was paid to place and target. Senators Ruben Gallego and Bernie Moreno want a duty to take reasonable steps against scam adverts, and no immunity for those that get through ^[2]. Representatives Dan Meuser and Lou Correa brought the same bill to the House on 12 February 2026, backed by banking associations and AARP; neither chamber has voted on it.

2.1 billion dollars reported lost in 2025 to scams that began on social media, eight times the 2020 figure ^[1]
29 percent fewer scam adverts at once in Meta's own tests of compulsory advertiser verification ^[4]
117.7 billion dollars spent on social media advertising in the United States in a year ^[3]

The proposal

Require platforms to take reasonable steps against fraudulent adverts, and remove their immunity for the ones they are paid to run.

The analysis

Liability for Scam Adverts

49% of the weight is for, 51% against.

An argument's weight combines three questions: how big the effect is, how certain it is and how much the good at stake matters. The larger the slice, the more the argument weighs.

For

Against

Whether liability for scam adverts is a good proposal, the analysis leaves open.

Unclear

Unclear: Under the assumptions used, the proposal brings more than it costs in 44 of 100 model runs.

Overall effect: small

How strongly the proposal works overall, in five steps from very small to very large, comparable across all debates. The ring is always full, even when the overall effect is small; this step says how large it is.

The analysis agrees with the sponsors that fraud losses are the largest single item, but finds costs of almost the same size on the other side. Three items carry most of the weight: the fraud that stops, honest adverts that the screening refuses, and the cost of checking every advertiser. Behind them, what being defrauded does to people beyond the money is the strongest smaller reason for the bill. The outcome turns on two unmeasured quantities. One is how much fraud the duty stops rather than pushes into messages and posts, the other how many honest adverts a platform refuses once it can be sued.

In detail

All arguments

Arguments for3 arguments

Fraud losses that stop

11points

Americans reported losing 2.1 billion dollars in 2025 to scams that began on social media, eight times the 2020 figure. A large share of it starts with an advertisement the platform was paid to deliver. Where verification of advertisers has been made compulsory, scam adverts fell at once. The money goes to organised fraud operations, and a gain obtained by deception counts for nobody.

Value 5 · Household budgetsImpact 3.0Plausibility 7

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The stream is money, priced at the middle of the scale, but it is not a transfer in the ordinary sense. A payment obtained by deception is not an exchange either side chose on informed terms, and the recipient is a fraud operation: this site gives the perpetrator's side of a stolen sum no weight at all, so nothing offsets the victim's loss in this accounting. Where the recipient sits makes no difference to that — a fraud ring in the United States would be treated the same way as one abroad, and a foreign victim of an American advert would count like an American one, because the accounting is global. That is why the whole amount is counted rather than a difference in what a dollar is worth at two ends. The distress of being defrauded is a separate stream and is counted separately, because losing four thousand dollars to a fake investment is not the same event as spending four thousand dollars. The value is the middle of the scale, and the whole sum is counted because a gain obtained by deception carries no weight on the other side.

Americans reported 2.1 billion dollars of losses in 2025 to scams that began on social media, eight times the 2020 figure, with about 1.1 billion of it in investment fraud ^[1]. How much of that starts with a paid advertisement rather than a message, a marketplace listing or an organic post is the first estimate: 35 percent is used, in a range from 15 to 60, since investment and shopping fraud are the ones most often advertised. The second and larger correction is under-reporting: the trade commission's own work suggests only a small minority of victims ever file a report, and a multiplier of four is used, in a range from two to ten. That gives about 2.9 billion dollars of actual losses from paid scam adverts. How much of it the duty stops is the open number. Meta’s own tests found that compulsory verification in Taiwan and Singapore cut scam adverts by as much as 29 percent at once ^[4]. That is the best result observed, not the typical one, and fewer adverts is not yet fewer losses: some fraud moves to messages and organic posts, and some adverts are rerouted to other countries, which Meta’s experiments also observed ^[4]. A fifth of the losses is used, in a range from 10 to 40 percent; Taiwan’s reported fall of 96 percent in investment scam adverts is left out of the range, because the government ran its own ad scanning on top of verification ^[4]. The victims carry a weight of 1.2, which gives a Normalised Impact of 3.04 on the scale of this evaluation. Varying all three estimates together, the range runs from about 2.3 to 8.2. The Impact is the largest in this debate: fraud that does not happen, about twice the cost of verifying every advertiser.

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	Reported losses to scams that began on social media [1]	2.1 billion dollars at 1.16 dollars to the euro, eight times the 2020 figure	1,810 million euro-equivalent
×	Share starting with a paid advertisement Setting, range 15 to 60 percent: investment and shopping fraud, the two largest categories, are the ones most often advertised [1]	35 %	634 million euro-equivalent
×	Correction for losses never reported Setting, range 2 to 10: the trade commission's own work suggests only a small minority of defrauded people file a report, which implies a higher multiplier than the one used	× 4	2,530 million euro-equivalent
×	Share prevented once platforms face liability Meta’s tests: compulsory verification cut scam adverts by up to 29 % at once, the best result observed; range 10 to 40 percent for fraud moving to messages, posts and accounts abroad; Taiwan’s −96 % is left out because the state scanned adverts on top [4]	20 %	506 million euro-equivalent
×	Weight of a dollar at these incomes fraud losses fall across the income distribution and hit hardest relative to means below the middle of it	1.2	607 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	3.04

Score 3.04 Impact × 5 Value × 7 Plausibility ÷ 10 = 11 of 100

The duty in the bill is concrete: before an advert runs, the platform has to establish who the advertiser is, with identification and documents ^[2], and once platforms are liable for the adverts they are paid to run, they have every reason to apply it. An advertiser who cannot pass the check cannot buy the advert, and fraud operations working through fake or stolen identities are exactly those advertisers. That compulsory verification removes scam adverts has been observed: Meta’s internal tests in Taiwan and Singapore showed them falling immediately ^[4]. That is one company’s before-and-after comparison without a published control group, and it counts adverts, not losses. Between the two sits behaviour the bill does not control: fraud rings will test the checks with synthetic identities, move to messages and organic posts, or buy adverts through accounts abroad. A partial escape of that kind is carried in the range; what remains here is the chance that it eats the effect entirely. The Plausibility is high but not certain: the screening follows from the duty, its effect on adverts has been observed once, and whether the losses follow depends on how fraud adapts.

Evidence: Multiple precedents · plausibility at most 7 Evidence of effect: Controlled · at most 7

What being defrauded does to people

3.2points

Losing a retirement account to a fake investment is not the same experience as spending it. Victims report shame that stops them telling anyone, damage to marriages, and in the worst cases they do not recover. That is a separate harm from the money and it is not small.

Value 9 · HealthImpact 0.8Plausibility 4.5

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The stream is what happens to a person after they discover they have been deceived: the depression and anxiety, the shame that keeps most victims from telling their family, the marriages that do not survive it, and at the far edge the suicides that follow the largest losses. This site places it in the class it uses for life and health, one step below the top because most people do recover. It is genuinely separate from the money, which is counted above: a household that loses the same sum in a bad investment it chose does not experience this. What is priced is the victim's own condition, not the effect on the people around them, which nothing here measures. The value sits one step below the maximum: the stream is health, and health that can be regained.

Behind about 2.9 billion dollars of actual losses from advertised fraud sit roughly 390,000 victims a year. That count comes from dividing the losses by an average loss of about 7,500 dollars, in a range from 4,600 to 11,600: the average has to be used rather than the median, because the distribution is heavily skewed — more than half the money is investment fraud, where the typical victim loses around 10,000 dollars, while the far more numerous shopping frauds cost a few hundred each ^[1]. Dividing by the median instead would invent a third more victims than the money can contain. If the measure prevents a fifth of the losses it prevents a similar share of the victimisations: about 78,000 people a year who are not defrauded ^[4]. What that spares each of them is set at 0.05 quality-adjusted years, in a range from 0.02 to 0.15 — a few weeks of serious distress on average, which is a low reading given that the tail of this distribution contains people who lose everything they have. That gives about 3,900 quality-adjusted years a year. Nothing is counted for the family members who carry it with them, and nothing for the smaller but far more numerous group who are targeted and not deceived. The Impact is a quarter of the money it accompanies, which is the ordinary proportion when a harm is severe for a minority and moderate for most.

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	Actual losses from advertised fraud [1]	from the argument above	2,530 million euro-equivalent
÷	Victims a year Setting, range 4,000 to 10,000 euro-equivalent (about 4,600 to 11,600 dollars): the average, not the median, because more than half the money is investment fraud at around 10,000 dollars a victim while the many shopping frauds cost a few hundred each [1]	about 6,500 euro-equivalent each on average (about 7,500 dollars)	390,000 people
×	Share spared by the measure the same share as the losses prevented above [4]	20 %	77,850 people
×	Quality-adjusted years spared each Setting, range 0.02 to 0.15: a few weeks of serious distress on average, which is low given that the tail contains people who lose everything	0.05	3,890 quality-adjusted years
×	Value of the years the value of a healthy life year used across this site	40,000 euro-equivalent each	156 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	0.78

Score 0.78 Impact × 9 Value × 4.5 Plausibility ÷ 10 = 3.2 of 100

That fraud victims suffer measurable psychological harm is documented in a substantial clinical and victimology literature, and the direction is not disputed by anyone. What that literature lacks is a proper comparison: it studies people who were defrauded, generally after the fact, against population norms rather than against a matched group who were targeted and escaped. Selection is therefore unresolved — people in difficult circumstances may be both easier to defraud and worse off afterwards for reasons that have nothing to do with the fraud. The victim count used here is derived from the loss figure and an assumed average loss rather than counted, and the quality-adjusted figure is a construction of this evaluation with no source behind it. What holds the argument up is that its direction is beyond dispute and its size is deliberately set at the low end. That hardship may come before the fraud rather than after it is a live concern and the reason for that choice. The Plausibility is below the middle: the harm is certain and every number attached to it here is assumed.

Evidence: Mechanism · plausibility at most 5.5 Evidence of effect: Associational · at most 5.5 Placement: Chain closed, unevidenced · plausibility 4–5

Open: Fraud reports carry contact details and platforms hold records of who saw which advert. A follow-up of reported victims against targeted non-victims would give the first clean estimate and could carry P to 6.

Clean adverts are worth more

2.4points

Scam advertisers bid for the same attention as honest ones, so a share of the impressions goes to fraud instead of to a product somebody wanted. They also teach users to distrust adverts generally, which lowers what every honest advert achieves. Both effects run against the businesses that pay for the platform.

Value 6 · OutputImpact 1Plausibility 4

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The stream is the efficiency of a market that moves about 117.7 billion dollars a year in the United States: advertisements reaching people who want the product rather than people who will be defrauded. It belongs to the class this site uses for economic systems and prosperity. What is counted is not the advertising spending, which moves from a company to a platform and stays inside the economy, but the difference between what the channel delivers when it is trusted and what it delivers when it is not. What clearing prices do is deliberately left out: if honest advertisers pay less once fraudulent bidders are gone, that is money moving from the platforms to the advertisers and not a gain to anyone in the aggregate, so it carries no weight here. The value sits in the middle-upper part of the scale, at the level this site uses for economic output.

American social media advertising was worth 117.7 billion dollars in 2025 ^[3]. Two real things go wrong when fraudulent advertisers share the auction. First, the impressions they win are impressions an honest product does not get — attention allocated to a fake shop rather than to a real one is a match that did not happen. Second, their presence makes users warier of every advert they see, so honest adverts convert less well. The lower clearing prices honest advertisers would enjoy once the fraudulent bidders are gone are not counted: that is a transfer from the platforms to the advertisers, not a gain to anyone in the aggregate. A gain of 0.2 percent in what the channel delivers is used for the two real effects together, in a range from 0.05 to 0.8 percent — about 240 million dollars a year. That figure is a construction; nobody has measured what a scam-free feed would be worth to the businesses buying it. What is not counted is the reputational value to the platforms themselves, which is theirs rather than anyone else's. The Impact is the smallest on this side, which is the honest scale of an efficiency gain in a market where fraud is a small share of the volume.

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	American social media advertising [3]	117.7 billion dollars at 1.16 to the euro	101 billion euro-equivalent
×	Gain in what the channel delivers once fraudulent bidders are gone Setting, range 0.05 to 0.8 percent: impressions won by fraud instead of by honest products, and users made warier of every advert; the lower clearing prices honest advertisers would pay are a transfer from the platforms and are not counted; nobody has measured the size	0.2 %	202 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	1.01

Score 1.01 Impact × 6 Value × 4 Plausibility ÷ 10 = 2.4 of 100

The mechanism is standard auction economics and the size has never been estimated for this market. The comparison is with the same advertising market with scam advertisers still bidding. Two links are visible and one is not: that fraudulent bidders win impressions honest products would otherwise have won follows from how the auctions work, and that users who distrust adverts respond to fewer of them is well established in advertising research, but nothing connects those to a number for this channel. The main doubt runs against the argument: platforms already remove large volumes of fraudulent adverts under their own policies, so the marginal effect of a legal duty on top may be small. That is unresolved. Nothing suggests the link runs the other way. The Plausibility is below the middle: the mechanism is uncontroversial, the size is invented, and the platforms' existing enforcement may already capture most of it.

Evidence: Mechanism · plausibility at most 6 Evidence of effect: Mechanistic · at most 6 Placement: Chain closed, unevidenced · plausibility 4–5

Open: Advertising clearing prices by category are visible to large buyers. Comparing them before and after a platform tightens advertiser verification would put a number on the auction effect.

Arguments against4 arguments

Honest advertisers get refused

8.1points

A platform that can be sued for a bad advert will reject anything that looks like one. That means new advertisers with no history, small businesses without documentation, anything about health or money, and adverts in languages the reviewers do not read. None of them are scams and all of them look like risk.

Value 6 · OutputImpact 3Plausibility 4.5

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The stream is business that does not happen: a shop that cannot reach customers, a service that never finds its market, a founder who gives up because the only channel available to them is closed. It belongs to the class this site uses for economic systems and prosperity. What is counted is the value of the transactions that do not occur, not the advertising spending itself, which simply goes somewhere else. Nothing is priced for the unfairness of being refused, which is a real grievance and not a separate good. The value sits in the middle-upper part of the scale, at the level this site uses for economic output.

The screening this bill forces is not costless in accuracy. Every system that reviews adverts at scale refuses legitimate ones, and the rate rises sharply when the reviewer bears the cost of a mistake in one direction and not the other. Two percent of legitimate advertising being wrongly refused is used here, in a range from half a percent to six percent: about 2.4 billion dollars of the 117.7 billion dollar market. The advertisers refused do not lose that spending — they spend it elsewhere or keep it — so what is lost is the surplus the advert would have generated above its cost, put at 30 percent, in a range from 15 to 60. That gives about 700 million dollars a year. The loss concentrates among the advertisers least able to argue: new businesses, small ones, and those advertising in languages the review systems handle worst. The Impact is half the money the measure saves, which is the honest cost of asking a platform to judge which of its customers are criminals.

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	American social media advertising, 117.7 billion dollars at 1.16 dollars to the euro [3]		101 billion euro-equivalent
×	Legitimate advertising wrongly refused Setting, range 0.5 to 6 percent: refusal rates rise when the reviewer bears the cost of a mistake in one direction only	2 %	2.02 billion euro-equivalent
×	Surplus the advert would have generated above its cost Setting, range 15 to 60 percent: the advertiser keeps or redirects the spending, so what is lost is the business it would have brought	30 %	606 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	3.03

Score 3.03 Impact × 6 Value × 4.5 Plausibility ÷ 10 = 8.1 of 100

That liability produces over-removal is one of the better-established regularities in platform regulation, observed wherever a takedown duty has been imposed with penalties on one side only. The comparison is with the current position, in which a platform's incentive is to sell the advert. What has no measurement is the rate. Platforms do not publish wrongful refusal figures, no regulator collects them, and the two percent used here is a construction — the same figure could be justified anywhere between a fifth of that and three times it. The counter-mechanism is real and partly answered: the bill sets a reasonable-steps standard rather than strict liability, which is designed precisely to stop platforms refusing everything ambiguous, and how far a court would let that defence run is unknown. Nothing suggests the link runs the other way. The Plausibility is below the middle: over-removal is a well-observed response to one-sided liability and its rate here has never been measured.

Evidence: Mechanism · plausibility at most 6 Evidence of effect: Mechanistic · at most 6 Placement: Chain closed, unevidenced · plausibility 4–5

Open: Platforms hold appeal and reinstatement rates for refused adverts. Publishing them before and after the duty takes effect would measure over-removal directly and could carry P to 6.

Verifying every advertiser costs money

7.1points

The bill spells out what reasonable steps means before an advert runs: the advertiser's legal name and physical location, a government-issued identification or the documents of a business entity, contact details, and measures against stolen or synthetic identities. Doing that for the millions of advertisers on a large platform is a real operation, and it has to be redone as advertisers churn.

Value 5 · Enforcement costImpact 1.5Plausibility 9.5

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The stream is money spent on checking rather than on anything the checking produces: verification staff, identity services, appeal handling, record keeping. It is priced at the middle of the scale like any other money and is a genuine cost rather than a transfer, since the hours and the systems are consumed. Whether the platform absorbs it or passes it to advertisers makes no difference to the weight, and it will mostly be passed on. Nothing is counted here for the enforcement burden on regulators or courts, which is small beside the private cost. The value is the middle of the scale, the level this site uses for money spent on running a rule.

Large platforms carry something in the order of ten million active advertisers between them, most of them small and many short-lived. The bill leaves little room over what a verification has to contain: the legal name and physical location of the advertiser, a valid government-issued identification or, for a business, the documents establishing that it exists and who is buying on its behalf, contact details that allow follow-up, and reasonable measures against false, stolen or synthetic identities ^[2]. A documentary check of that kind, with appeals and re-verification as advertisers churn, costs something like 35 dollars a year each, in a range from 23 to 100. That gives about 350 million dollars a year. The figure excludes the review of individual adverts, which platforms already do at scale and would extend rather than build. It also excludes the cost of litigation itself, which is counted separately below. The Impact is a quarter of the money the measure saves, which is the ordinary proportion for a duty that requires knowing your customer.

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	Active advertisers across large platforms [3]		10 million
×	Cost of verifying each to the standard the bill sets out Setting, range 20 to 90 euro-equivalent (about 23 to 100 dollars): legal name, location, a government identification or business documents, contact details and anti-circumvention checks, redone as advertisers churn; the lower end is where existing checks already cover part of the work [2]	30 euro-equivalent a year (about 35 dollars at 1.16 dollars to the euro)	300 million euro-equivalent
×	Weight of a dollar in company budgets the standard weight this site uses for business money	1.0	300 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	1.5

Score 1.5 Impact × 5 Value × 9.5 Plausibility ÷ 10 = 7.1 of 100

A duty to verify every advertiser before an advert runs means verifying every advertiser: the cost arises whenever the duty applies, and nothing in it depends on how anyone responds. The comparison is current practice, in which advertiser verification is partial and voluntary. The bill removes most of the doubt about what a check must contain: it lists the elements, so a payment-card check on its own would not satisfy it ^[2]. Unit costs are known, because identity verification is a mature commercial service with published prices, and the platforms report their advertiser numbers themselves. What remains uncertain is how many advertisers are genuinely additional, since the largest platforms already verify some categories under their own policies and political advertising rules. That doubt sits in the range of 23 to 100 dollars per advertiser and is not counted a second time here. The remaining doubt is enforcement: the commission has to check that platforms verify to the standard, and some may verify less thoroughly than the text requires. The Plausibility is very high: the cost follows from the duty itself, and only how fully it is enforced is open.

Evidence: Mechanism · plausibility at most 9.5 Evidence of effect: Definitional

Smaller platforms stop selling adverts

1.6points

The verification operation this requires has a fixed cost that a platform with a billion users barely notices and one with two million cannot carry. The bill applies to every public-facing service built around user content, with no size threshold. The predictable result is that advertising concentrates further with the four companies that already have most of it.

Value 6 · CompetitionImpact 0.6Plausibility 4.5

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The stream is competition in a market that is already concentrated: fewer places for an advertiser to go, fewer platforms able to fund themselves without being acquired. This site places that in the class it uses for economic systems and the working order of markets. What is priced is the lost competitive pressure rather than any particular company's survival, and no weight is given to smallness for its own sake. The compliance cost to the smaller platforms themselves is inside the previous argument and is not repeated here. The value sits in the middle-upper part of the scale, at the level this site uses for the working order of markets.

American social media advertising is already dominated by a handful of companies, and the fixed cost of a verification and appeals operation is exactly the kind that entrenches such a position. The bill's definition of an online platform is any public-facing website, service or application that predominantly provides a forum for user-generated content — with no user count, no revenue floor and no exemption for small operators, so a forum with two million users owes the same procedures, the same 72-hour investigations and the same records as one with two billion ^[2]. Smaller platforms have three options: build it, buy it from a vendor at a worse unit price, or stop selling advertising to anyone they cannot easily check. The value of the competitive pressure lost is put at a Normalised Impact of 0.6 on the scale of this evaluation, in a range from 0.13 to 2, which is roughly a tenth of a percent of the market. It is a price set rather than derived, and the reason it is small is that the concentration it worsens is already close to complete. The Impact is the smallest in this debate, and unlike a duty with a size threshold it reaches every operator selling adverts.

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	Value of the competitive pressure lost as advertising concentrates further Setting, range 25 to 400 million euro-equivalent: roughly a tenth of a percent of a market already dominated by a handful of companies; the bill's platform definition carries no size threshold, so every operator selling adverts is inside the duty [2][3]		120 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	0.6

Score 0.6 Impact × 6 Value × 4.5 Plausibility ÷ 10 = 1.6 of 100

The mechanism is standard and its application here is speculative. That fixed compliance costs favour incumbents is among the better-supported findings in regulatory economics, observed across banking, pharmaceuticals and financial services. The comparison is with the current advertising market. What is absent is any estimate for this case. One link that could have removed the argument is settled by the text: the bill's definition of a platform carries no size threshold, so the smallest operators are inside the duty ^[2]. The counter-mechanism is genuine — vendors sell advertiser verification as a service, and a small platform buying it at a per-check price faces a much smaller fixed cost, though the investigation deadlines, the reporting tool and the record-keeping remain its own. That is not resolved. Nothing suggests the link runs the other way. The Plausibility is a little below the middle: the mechanism is well supported in general, the duty reaches every operator, the size is a stated price, and a vendor market may remove much of the fixed cost the argument rests on.

Evidence: Mechanism · plausibility at most 6 Evidence of effect: Mechanistic · at most 6 Placement: Chain closed, unevidenced · plausibility 4–5

Open: Advertising revenue shares by platform, before and after the duty takes effect, would measure the concentration effect directly. Vendor prices for per-check verification would settle how much fixed cost a small operator actually faces.

Lawsuits that have to be fought

0.69points

Removing the section 230 defence is meant to let cases proceed, and the bill adds a private right of action, state attorney-general suits and up to treble damages. Each case consumes lawyers' and courts' time on both sides, whatever it ends up paying out.

Value 5 · Enforcement costImpact 0.3Plausibility 5.5

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The stream is the time of lawyers, judges and compliance staff consumed by the proceedings the bill makes possible: the individual suits of people who lost money, the suits state attorneys general may bring on behalf of residents, and the commission's own enforcement. It is priced at the middle of the scale like other money spent on running a rule, and it is a real cost rather than a transfer, because the hours are used up whatever the outcome. What the cases pay out — actual damages, up to three times that for wilful violations, costs and fees — is money moving from a platform to a victim and is not counted here at all. Nothing is counted for the deterrent value of the cases, which is inside the prevented losses on the other side. The value is the middle of the scale, the level this site uses for the cost of running a rule.

The bill gives anyone injured by a violation a federal cause of action, lets state attorneys general sue on behalf of their residents, awards costs and attorney's fees to a prevailing plaintiff, and allows up to treble damages where a violation was wilful ^[2]. The volume that produces is a setting: about 2,000 proceedings a year is used, in a range from 1,000 to 5,000 — a small fraction of the hundreds of thousands of people who report a loss each year, because most losses are too small to litigate individually and many claims will be gathered into class actions, and because the bill's presumption of compliance for platforms with an approved programme narrows what a plaintiff can win. What a proceeding consumes on both sides, at the counsel, discovery and court time an action against a large platform takes, is put at about 29,000 dollars, in a range from 12,000 to 58,000. That gives about 58 million dollars a year. Nothing is counted for the payouts themselves. The Impact is a sixth of the verification cost, which is the scale of a duty whose main tool is the threat of a suit rather than the suit itself.

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	Proceedings a year under the new cause of action Setting, range 1,000 to 5,000: individual suits, state attorney-general actions and commission enforcement; most losses are too small to litigate alone and the presumption of compliance narrows the cases [1][2]	about 2,000	2,000 proceedings a year
×	Lawyers' and courts' time consumed per proceeding, both sides Setting, range 10,000 to 50,000 euro-equivalent (about 12,000 to 58,000 dollars): counsel, discovery and court time of an action against a large platform; payouts are transfers and are not counted	25,000 euro-equivalent (about 29,000 dollars at 1.16 dollars to the euro)	50 million euro-equivalent
÷	Normalised Impact scale of this evaluation	200 million euro-equivalent a point	0.25

Score 0.25 Impact × 5 Value × 5.5 Plausibility ÷ 10 = 0.7 of 100

That a new cause of action produces proceedings is close to certain, but it is still a response: the cost arises only when people decide to sue and attorneys general decide to act. The comparison is the current position, in which section 230 ends most such suits before discovery. The number of proceedings is a setting rather than a count, because no comparable federal cause of action exists to draw on. Two forces pull in opposite directions, and neither is measured. The presumption of compliance and the reasonable-steps standard will end many cases early, which lowers the cost per case. The fee-shifting provision invites cases that would otherwise not be brought, which raises the count. The Plausibility is a little above the middle: the right to sue is in the text, how often it is used is a setting, and the two unmeasured forces pull in opposite directions.

Evidence: Projection · plausibility at most 6 Evidence of effect: Mechanistic · at most 6

How robust the result is

Under the assumptions used, the proposal brings more than it costs in 44 of 100 model runs.

gains more than it costscosts more than it gains

Each dot stands for 200 of the 20,000 model runs.

Reported fraud losses are multiplied by four here to reach actual losses, because the trade commission's own work suggests only a small minority of victims file a report. Money taken by deception is counted as lost, not as a transfer to the fraudster. A dollar lost to fraud is weighted at 1.2, because the losses hit hardest relative to means below the middle of the income distribution. The share of losses the duty prevents is set at 20 percent, below the 29 percent of the best observed test, because fewer adverts is not yet fewer losses. The measure comes out ahead in a good four of ten runs and a little behind in the average run.

Show the distribution of the model runs

20,000 simulated futures

Unclear · 44 %

In 44 of 100 simulated futures the proposal brings more than it costs.

No effect is known exactly; every argument has a range. The calculation draws a possible size for each argument from its range 20,000 times and counts how often the benefits exceed the costs.

Net effect in points per future. Left of zero the drawbacks outweigh the benefits, right of zero the benefits win. Eight in ten futures fall between −13 and +10 points.

Scale and assessment

Scale of this evaluation: Normalised Impact, unitless and calibrated to this topic. For comparison, one point here corresponds to about 200 million euro-equivalent a year.

Size class:small

Effect small (−0.27) · 48 % of the effect is positive

Areas show individual argument weights. The positive share of the effect nets out money transfers between the two sides; the probability of a positive net effect is determined separately.

How we score →

Review and sources

Last reviewed by Claude Opus 5.5 · October 29, 2026 · 9× AI, 3× human

October 29, 2026AI reviewClaude Opus 5.5record updated
Page texts for the new page layout (draft G) with quotes from primary sources; numbers unchanged.
October 21, 2026AI reviewClaude Fable 5.1record updated
Money amounts in argument texts stated in dollars per style-en.md; scores, chains and balance unchanged
October 20, 2026AI reviewClaude Fable 5.1record updated
Summary and scale note rewritten to the style of 20 Oct 2026; numbers unchanged
October 14, 2026Assessed by a human, implemented by AIClaude Opus 5.5re-scored
Julian 14.10.: pro-1 nicht mehr definitorisch, P 9,5 → 7 (kontrolliert; Plattformen setzen die Pflicht um, Betrug weicht teilweise aus); Mitte 29 % → 20 % (Metas 29 % ist der beste beobachtete Wert), Spanne aus allen drei Schätzungen; pro-2 mitgezogen; P(D > 0) 0,77 → 0,58, ausgeglichen.
October 2, 2026Assessed by a human, implemented by AIClaude Opus 5.5re-scored
Julian 02.10.: pro-1 auf belegten Eintritt umgestellt — Metas Tests zeigen bis zu −29 % Scam-Anzeigen bei Pflicht-Verifizierung (Reuters 31.12.2025); Mitte 29 % statt 40 %, Spanne 10–60 %, P 9,5; pro-2 mitgezogen; P(D > 0) 0,49 → 0,77, besser.
October 2, 2026Assessed by a human, implemented by AIClaude Opus 5.5re-scored
Regel ‚Eintritt und Höhe getrennt' (Julian 02.10.) angewendet: con-2 (Verifizierungspflicht) P 6 → 9,5; con-4 (Prozesskosten) auf mechanistisch korrigiert, weil sie erst entstehen, wenn jemand klagt; r 0,62 → 0,58, Bilanz 2.0 erstmals geschrieben: P(D > 0) 0,487, ausgeglichen.
September 14, 2026AI reviewClaude Opus 5re-scored
Gesamtprüfung 08.09.: pro-2 Opferzahl aus Durchschnitts- statt Medianschaden (I 0,5→0,312), con-2/con-3 am Gesetzestext S. 3774 korrigiert (P 5,5→6, 4→4,5), neues con-4 Prozesskosten (I 0,05), Täterseite ausdrücklich ohne Gewicht; r 0,67 → 0,62.
September 6, 2026AI reviewClaude Opus 5record updated
i_spanne an allen 6 Argumenten aus den englischen Ketten, kein Transfer im Record. Kategorie kippt von Besser (r 0,67) auf Ausgeglichen (P(D>0) 0,69).
September 6, 2026AI reviewClaude Opus 5First evaluation
Created for the English side: reported fraud losses corrected upward for under-reporting, with the over-removal cost of one-sided liability booked against it.

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Federal Trade Commission: Reported losses to scams on social media eight times higher than in 2020. ftc.gov
Congress.gov: S. 3774, Safeguarding Consumers from Advertising Misconduct Act (SCAM Act), as introduced 4 February 2026. govinfo.gov
Interactive Advertising Bureau: Internet Advertising Revenue Report. iab.com
Reuters (via Claims Journal): Meta created 'playbook' to fend off pressure to crack down on scammers, documents show (Jeff Horwitz, 31 December 2025). claimsjournal.com