AI claim case library with evidence gaps and buyer questions
Use these cases to compare a vendor's wording with documented evidence gaps before you check the exact sentence yourself. The library shows public wording, evidence gaps, and buyer questions from reviewed sources. These are not legal judgments, company ratings, or compliance certifications. When you see a similar pattern, take the exact vendor sentence into the AI product claims checker.
How to read these cases
- Read the source first Each case is anchored to an official or high-confidence source. The source and date define what the page can support.
- Compare the claim pattern Use the risk pattern to decide whether a vendor claim you saw is similar enough to carry the same evidence burden.
- Use the evidence gap as a question list The evidence gap and buyer questions tell you what to request from a vendor before relying on similar wording.
- Check the wording boundary Open the case detail to see what narrower wording would disclose, such as test scope, limits, or remaining human review.
- Check your exact wording separately Cases explain documented examples. The checker reviews the specific sentence or public URL you are evaluating.
Filter cases
Showing 24 cases
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Workado AI detector accuracy
The FTC finalized an order finding Workado misrepresented the accuracy of its AI content detector. This case illustrates the evidence burden behind numeric accuracy claims on AI detection tools.
Risk pattern: Numeric accuracy without disclosed test scope or error rates
Evidence gap: Benchmark design and scope, content types included in testing, AI model versions covered, sample size, false positive rate, false negative rate, and how frequently the benchmark is updated as AI models change.
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accessiBe automated accessibility claims
The FTC finalized a $1 million settlement finding accessiBe overstated what its AI overlay could do for web accessibility compliance. This case shows the evidence burden behind automated compliance claims.
Risk pattern: Automated compliance promise without stated audit method or human review boundary
Evidence gap: Which WCAG version and success criteria the tool covers, the audit method and test scope, which issues remain outside automated detection, what human review and testing remains necessary, and how the tool handles dynamic content, third-party scripts, or ongoing maintenance.
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NIST AI text detector reliability study
NIST published a GenAI pilot study on text generation and AI-based discriminator tasks. Use this official research source to ask vendors about AI text detector reliability, benchmark design, and false-positive evidence.
Risk pattern: Detector accuracy claim without benchmark design, discriminator metric, or task-scope disclosure
Evidence gap: Benchmark dataset source, human and machine text categories, generator model coverage, discriminator models tested, score metrics such as AUC or Brier score, threshold selection, false positive rate, false negative rate, and whether the test conditions match the buyer's use case.
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DoNotPay robot lawyer claims
The FTC finalized an order against DoNotPay for marketing itself as 'the world's first robot lawyer.' This case shows the evidence burden behind claims that AI can replace qualified professionals.
Risk pattern: Professional-replacement positioning without task scope or qualified review boundary
Evidence gap: Task scope and document types tested, the professional review standard used for comparison, failure handling and escalation path, disclosed non-use cases, and user notice about when to seek qualified legal advice.
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SEC Delphia AI investment claims
The SEC settled with Delphia for making false claims about using AI and machine learning to inform investment decisions. This case shows the evidence burden behind AI performance claims in regulated financial contexts.
Risk pattern: AI-driven performance claim without disclosed model description or independent validation
Evidence gap: Description of the AI or ML model used, training data and sources, how the AI contribution to investment returns was measured and isolated from other factors, independent validation of stated capabilities, and disclosed limitations of the model.
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SEC Global Predictions 'first regulated AI advisor'
The SEC settled with Global Predictions for claiming to be 'the first regulated AI financial advisor.' This case shows the evidence burden behind 'first' and AI-capability claims in regulated financial services.
Risk pattern: Uniqueness claim without defined scope; AI capability claim without disclosed method
Evidence gap: The scope definition used to support the 'first' claim, how the comparison was made and against what alternatives, what the AI component does versus conventional advisory tools, independent validation of AI capabilities, and when the claim was last reviewed for continued accuracy.
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FTC fake reviews and testimonials rule
The FTC issued a final rule banning fake reviews and testimonials, including AI-generated reviews. This source-backed example explains what the rule requires and what evidence buyers should look for when a vendor cites testimonials.
Risk pattern: Testimonials or review counts used as social proof without disclosure of AI generation, compensation, or collection method
Evidence gap: Whether testimonials are from independent users, whether reviewers received any compensation or incentive, whether any testimonial content was generated or enhanced by AI tools, when reviews were collected relative to product use, and how the collection method is disclosed.
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Sitejabber review collection timing
The FTC issued a consent order against Sitejabber for collecting reviews before consumers received their products, then displaying them as post-purchase reviews. This case shows what buyers should ask about review collection timing and context.
Risk pattern: Review ratings displayed without disclosure of collection timing or reviewer experience context
Evidence gap: When reviews were collected relative to product delivery or service completion, what specific experience reviewers were evaluating at the time of the survey, how the collection method and timing are disclosed, and whether displayed ratings reflect post-delivery experience.
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Rytr AI-generated testimonial service
The FTC issued a final order against Rytr in December 2024 for providing AI tools to generate testimonials. In December 2025, the FTC reopened and set aside that order. This case shows why enforcement status and checked date matter when evaluating AI claim risk.
Risk pattern: AI-generated testimonial or review content presented as genuine user experience without disclosure
Evidence gap: Whether any testimonials produced by the tool were presented as genuine consumer feedback without AI-generation disclosure, what disclosure requirements apply to AI-generated review content, and how buyers can verify review authenticity on platforms that permit AI writing assistance.
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ASA/CAP AI as a marketing term
The UK's Advertising Standards Authority and Committee of Advertising Practice published a report on how 'AI' is used as a marketing descriptor without adequate substantiation. This official report describes the evidence burden behind vague AI-powered claims.
Risk pattern: AI used as a marketing descriptor without explaining function, input, output, or user-visible benefit
Evidence gap: What AI specifically does in the product, what input the AI processes, what output it produces, how performance compares to a non-AI version of the same function, what human oversight is involved, and whether users experience a meaningful AI-attributable benefit.
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Rite Aid facial recognition safeguards
The FTC said Rite Aid deployed AI-based facial recognition without reasonable safeguards, leading to false-positive matches and consumer harm. This case shows the evidence burden behind AI safety and biometric surveillance claims.
Risk pattern: AI biometric surveillance deployment without documented accuracy testing, false-positive monitoring, or harm controls
Evidence gap: Pre-deployment accuracy testing, false-positive tracking, demographic impact monitoring, image-quality controls, employee training, vendor oversight, consumer notice, complaint handling, deletion rules for biometric data, and a clear stop rule when the system cannot control consumer harm.
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ASA PixVideo AI video maker ad
The ASA upheld a ruling against a PixVideo AI Video Maker ad whose wording and visuals implied harmful image-editing uses. This case shows the evidence burden behind AI creative-tool safety and boundary claims.
Risk pattern: AI creative-tool ad implying unrestricted or harmful image editing without visible safeguards, consent limits, or content restrictions
Evidence gap: Allowed-use policy, prohibited content categories, consent rules for editing people in images, automated detection and blocking methods, human review boundary, ad creative review process, platform targeting controls, and whether the marketing claim reflects the product's actual restrictions.
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ASA Aurai Ai roleplay ad
The ASA upheld a ruling against an Aurai Ai paid ad for an AI roleplay app. This case shows the evidence burden behind AI companion and roleplay marketing that suggests safety, intimacy, or no-judgment interaction.
Risk pattern: AI companion and roleplay positioning without clear ad-safety controls, content boundaries, or audience safeguards
Evidence gap: Ad review process, age and audience targeting controls, prohibited roleplay scenarios, sexual content and violence moderation, prompt and response safeguards, platform policy checks, complaint response process, and documentation showing that marketing does not encourage unsafe or harmful interaction patterns.
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Evolv AI security screening claims
The FTC alleged Evolv made unsupported claims about what its AI-powered security screening system could detect, ignore, and reduce compared with traditional metal detectors. This case shows the evidence burden behind safety-sensitive AI detection claims.
Risk pattern: Safety-sensitive detection claim without item-level detection rates, false-alarm rates, or field-condition limits
Evidence gap: Detection rates by item type, false positive and false negative rates, sensitivity settings, field test conditions, school or venue deployment data, staffing assumptions, comparison method against metal detectors, and documented cases where items were missed or harmless items were flagged.
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IntelliVision facial recognition claims
The FTC finalized an order against IntelliVision over claims that its AI-powered facial recognition software had high accuracy, zero gender or racial bias, and anti-spoofing capability. This case shows the evidence burden behind demographic performance and safety claims.
Risk pattern: Bias-free and anti-spoofing AI claim without subgroup testing, training-data support, or attack-scenario limits
Evidence gap: Subgroup performance by gender, ethnicity, and skin-tone groups; sample size and training-data documentation; benchmark design; error rates by deployment setting; anti-spoofing test methods; attack types tested; and limits for image quality, lighting, angle, and camera conditions.
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NGL AI content moderation claims
The FTC and Los Angeles District Attorney's Office alleged NGL falsely claimed its AI content moderation filtered cyberbullying and other harmful messages. This case shows the evidence burden behind AI safety claims for apps used by children or teens.
Risk pattern: AI harm-filtering claim without disclosed detection scope, error rates, or child-safety limits
Evidence gap: Harm category definitions, training data and evaluation set, false negative and false positive rates, teen-user test coverage, language and slang coverage, escalation process, human review boundary, recurrence handling for repeat abuse, and update process for new harmful-message patterns.
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Cox Active Listening AI ad targeting
The FTC alleged Cox Media Group and two marketing firms falsely claimed an AI-powered service could target ads from conversations captured by smart devices and that consumers had opted into that targeting. This case shows the evidence burden behind vague AI-powered data claims.
Risk pattern: AI-powered targeting claim without evidence of data source, consent basis, algorithm function, or geographic accuracy
Evidence gap: Actual data sources used, whether voice data is collected or not, opt-in method and consent record, algorithm function, geographic targeting validation, data-broker involvement, customer disclosures, and whether the claim is updated when the product workflow changes.
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Air AI business growth claims
The FTC alleged Air AI made deceptive claims about business growth, earnings potential, refund promises, and a conversational AI feature. This case shows the evidence burden behind AI-linked outcome and earnings claims.
Risk pattern: AI-linked earnings claim without customer outcome distribution, cost baseline, or refund-promise substantiation
Evidence gap: Customer outcome distribution, total startup and operating costs, baseline comparison, time period, refund eligibility and payout history, cancellation terms, customer-selection criteria, evidence that the conversational AI feature worked as described, and evidence separating that feature from coaching, sales, ads, labor, inventory, or market conditions.
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Automators AI ecommerce earnings
The FTC case page says Automators claimed to use AI to support success and profitability for ecommerce storefront customers. This case shows the evidence burden behind AI-boosted income and passive-investment claims.
Risk pattern: AI-boosted earnings claim without customer outcome distribution, workload disclosure, or cost basis
Evidence gap: Customer sample size, median profit and loss, total investment required, time to break even, passive-investor versus self-managed-store outcomes, customer workload, inventory and ad costs, ecommerce platform fees, refund or chargeback data, and a method for attributing any outcome to AI-powered tools rather than coaching or store operations.
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FBA Machine AI storefront income
The FTC case page says FBA Machine and Passive Scaling were alleged to have promised income through online storefronts using AI-powered software. This case shows what evidence is needed before relying on AI-powered income claims.
Risk pattern: Income promise tied to AI-powered software without full cost, customer sample, and failure-rate disclosure
Evidence gap: Customer result distribution, total investment range, inventory and platform fees, ad spend, fulfillment and labor costs, refund request outcomes, unsuccessful customer percentage, time to break even, evidence that the AI-powered software was active in customer stores, and a documented method for isolating the effect of repricing or storefront tools.
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Ecommerce Empire Builders AI income
The FTC case page says Ecommerce Empire Builders was charged over claims about an AI-powered ecommerce business opportunity and potential customer earnings. This case shows the evidence burden behind AI-powered business outcome claims.
Risk pattern: AI-powered business opportunity claim without typical-results evidence, cost disclosure, or customer-loss context
Evidence gap: Typical customer result data by offer type, median net income, unsuccessful customer share, total cost for training versus done-for-you storefronts, refund rate, time period, store-operation workload, product and supplier assumptions, and evidence showing that AI-powered workflows materially changed results.
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SEC Rimar AI trading platform
The SEC charged Rimar Capital entities and related individuals over statements about the firm's purported use of AI to perform automated trading for client accounts. This case shows the evidence burden behind AI-driven trading and asset-management claims.
Risk pattern: AI-driven investment platform claim without evidence of model ownership, live use, trading workflow, or investor disclosure support
Evidence gap: Model description, ownership and vendor involvement, live-deployment evidence, trading workflow role, client-account coverage, risk controls, human oversight, performance attribution, backtest limits, disclosure documents, and records showing the AI capability existed when the claim was made.
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SEC Presto AI voice automation
The SEC announced settled charges against Presto Automation over statements about critical aspects of its AI voice product, including ownership of AI technology and human intervention in drive-thru ordering. This case shows the evidence burden behind automation claims in public company statements.
Risk pattern: AI automation claim without disclosure of third-party technology, human intervention rate, or completion metric definition
Evidence gap: Technology ownership and vendor role, deployed-unit coverage, human intervention rate, order-completion metric definition, exception categories, customer deployment data, quality control process, and disclosure showing how AI-assisted work differs from human-assisted work.
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FTC Content at Scale AI detector
The FTC legal-library page for Content at Scale AI identifies Workado, LLC, formerly known as Content at Scale AI. This case explains the record-keeping burden behind numeric accuracy claims in AI detection products.
Risk pattern: Headline accuracy number without disclosed benchmark, test corpus, or error rate records
Evidence gap: Benchmark design and test corpus description, content types and categories included, AI model versions covered at time of test, sample size, false positive rate, false negative rate, test date, and a record the vendor retains that could be produced if the claim is questioned.
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