When algorithms reward the wrong signals
Artificial intelligence is supposed to make digital advertising more effective. Algorithms process large volumes of signals, predict user behaviour and optimise budget allocation automatically. Yet an algorithm can optimise only for the quality its data, objectives and controls are able to represent.
What happens when automated browsers generate clicks, synthetic interactions are mistaken for authentic user behaviour, or low-quality advertising environments generate apparently attractive performance metrics?
Under those conditions, an optimisation system may favour the very traffic sources an advertiser would reject on quality grounds. The challenge is not necessarily AI itself. It is the integrity of optimisation signals, the economic incentives across the supply chain and the limits of industry self-regulation.
1. The 71% question: what does the Spider AF report actually establish?
Spider AF’s Ad Fraud Investigation Report for the first half of 2026 covers approximately 4.006 billion advertising clicks recorded from January through June. According to the provider, 5.58% of paid clicks in its dataset were classified as invalid; 64.4% of observed bot traffic involved advanced bots; and 71% of estimated losses associated with made-for-advertising (MFA) websites were attributed to AI-optimised ad delivery.
These figures warrant attention, but the denominator matters. The 71% figure does not mean that 71% of AI-optimised impressions were fraudulent. Nor does it establish that AI caused the reported losses. Without comparable spending and exposure data, the figures cannot establish the relative risk of AI-optimised versus other campaigns.
Similarly, any market-wide loss estimate extrapolated from a vendor’s monitored traffic must not be presented as a directly observed global loss. The narrower, defensible conclusion remains significant: algorithmic optimisation does not by itself guarantee genuine traffic, good inventory or meaningful business outcomes.
2. How manipulated signals can become apparent performance
Clicks, conversions and modelled user responses can influence campaign optimisation. But a recorded click does not prove buying intent or human participation. A reported conversion does not automatically demonstrate incremental business impact.
If manipulated events escape detection, an algorithm may treat them as positive learning signals. A potential feedback loop emerges: suspect sources produce apparent successes; the system favours similar placements; more budget reaches those environments; and the resulting activity appears to confirm the original decision.
Whether this actually happened within a given platform or campaign requires empirical investigation. This is a plausible mechanism, not an allegation of proven behaviour by any particular platform. The appropriate response is forensic validation, not speculation.
3. MFA is not synonymous with ad fraud
Made-for-advertising sites are primarily structured around generating and monetising advertising exposure. High ad density, aggressive traffic acquisition and limited original editorial value may be warning signs. An MFA classification does not, by itself, prove intentional fraud; equally, a technically valid impression is not necessarily commercially valuable.
Advertisers need to assess three separate questions: inventory quality, traffic authenticity and advertising effectiveness. An impression may be technically valid but appear in a low-value environment. A human website visit does not prove the ad was seen. A recorded conversion does not prove advertising created incremental demand.
4. IAB: technical standards are not enforcement
The IAB and IAB Tech Lab have made legitimate contributions to interoperability and seller transparency. Specifications such as ads.txt, sellers.json and the OpenRTB SupplyChain Object can help identify authorised sellers and make certain forms of inventory spoofing harder.
However, the IAB operates within an industry whose members include major platforms and technology companies with direct commercial stakes in the advertising infrastructure. Participation in standard-setting by economically interested market actors creates a structural governance question. It is not, by itself, evidence of misconduct by any member.
Publishing an IAB specification does not make its adoption mandatory for all market participants. Nor does it create universal enforcement or guaranteed sanctions for non-compliance. Technical compliance schemes exist, but they are not equivalent to regulatory oversight across the entire market.
Most importantly, an authorised seller does not prove that an impression was generated by a real human. A documented supply chain does not prove viewability, authentic engagement or incremental impact. A standard is a tool, not a quality guarantee.
5. MRC: meaningful accreditation, limited scope
The Media Rating Council (MRC) must be distinguished from the IAB Tech Lab. Its accreditation process covers defined measurement services and includes independent audits and recurring assessments. That is a more substantial control mechanism than merely publishing guidance.
Nevertheless, participation in MRC accreditation is generally voluntary. Accreditation applies to the particular audited service and its documented scope; it is not a blanket assurance covering all products, all traffic or every form of sophisticated fraud. Full audit materials are not universally public.
The MRC distinguishes General Invalid Traffic (GIVT) from Sophisticated Invalid Traffic (SIVT), recognising that some forms of invalid activity require more complex detection. Yet every method has limitations in coverage, implementation and detection capability.
Not classified as invalid is not the same as positively verified as human. Our criticism concerns overclaiming what an audit or accreditation proves—not the principle of independent auditing itself.
6. The accountability gap in self-regulation
The advertising industry does not lack definitions. What it lacks is consistently enforceable accountability connecting technical requirements, independent verification and economic consequences.
Platforms control substantial portions of the delivery infrastructure and relevant reporting data. Advertisers pay for the resulting transactions but do not always receive sufficient access rights to examine the underlying quality claims independently. Voluntary standards alone cannot eliminate this information asymmetry.
H.O.T. AG therefore advocates verifiable quality thresholds, contractual audit rights, appropriate data access and pre-agreed consequences for documented breaches. No guideline or accreditation should be interpreted as proving more than its stated scope supports.
7. Why AI can scale existing weaknesses
Automated systems make large numbers of buying and budget-allocation decisions very quickly. Where questionable signals affect these decisions, quality problems can propagate faster and over more impressions.
Greater automation does not necessarily cause more fraud. It does, however, raise the requirements for trustworthy training and feedback data, independent measurement and clear governance. The more autonomous optimisation becomes, the more important a credible external evidence layer is.
8. Six practical controls for advertisers
1. Define quality before performance. A low CPA or high ROAS is not enough to establish quality; set inventory, measurement and traffic requirements before campaigns launch.
2. Validate supply paths rather than merely documenting them. Use ads.txt and sellers.json as starting points, then verify actual sellers, intermediaries and delivery destinations.
3. Combine independent in-ad and on-site evidence. Where technically possible, evaluate rendering, viewability, traffic origin, environment and behaviour across multiple signals.
4. Do not treat residual traffic as proven human traffic. Traffic that has not been flagged as invalid may still include undetected automation.
5. Compare AI optimisation using controlled test designs. Examine invalid traffic, inventory quality, authentic engagement indicators and, where feasible, incremental business effects alongside conventional performance metrics.
6. Contract for accountability. Secure audit rights, access to relevant data, documented escalation processes and appropriate remedies for proven violations.
9. The H.O.T. AG perspective: independent evidence over self-certification
H.O.T. AG combines strategic media expertise, programmatic supply-chain analysis, technical measurement and forensic interpretation. Together with FouAnalytics, more than 300 technical and behavioural signals can be examined, subject to implementation and measurement coverage.
Not every anomaly is fraud. Not every event that passes a filter is authentic. And not every valid impression delivers measurable advertising impact. Distinguishing these levels of evidence is essential to responsible media governance.
Our aim is not to replace industry standards with assertions of our own. It is to test quality claims against independently obtainable technical evidence—and to document where observation ends.
Conclusion: less self-assurance, more independent accountability
AI can make media buying more efficient, but it cannot independently guarantee that the signals it learns from are authentic. The Spider AF findings are a reason to investigate, not causal proof against artificial intelligence.
IAB specifications and MRC accreditations can be valuable components of an assurance framework. Neither creates universal, independently enforced verification of every digital advertising contact.
The digital advertising industry does not suffer from a shortage of guidelines. It suffers from a shortage of independent enforcement and verifiable accountability.
The decisive question is no longer simply how efficiently an algorithm optimises. It is: Who independently verifies that what it optimises is real?
Sources and methodological notes
- Spider AF — Ad Fraud Investigation Report H1 2026 (vendor dataset; estimates cannot establish global representativeness or causation)
- IAB Tech Lab — ads.txt and sellers.json (technical seller-transparency specifications)
- MRC — Audit and Accreditation Process (process and scope of accreditation)
- MRC — Standards and Guidelines (invalid-traffic measurement standards)
Methodological note: Spider AF's findings refer to its monitored dataset and must not be extrapolated without qualification. Algorithmic feedback loops are described as possible mechanisms, not proven cases involving specific platforms. Our assessment of industry governance is the independent professional interpretation of H.O.T. AG.