Does the EU AI Act restrict AI apps that infer sensitive attributes or categorize people from facial images, and what do Apple App Store and Google Play policies say about face-based attractiveness rating apps?
10/2/2026, 4:27:27 PM · llm:deepseek:deepseek-v4-flash + heuristic (fallback from llm:mimo:mimo-v2.5) on 1 step
The provided sources do not contain any information about the EU AI Act or its treatment of AI applications that infer sensitive attributes from facial images, so that question cannot be answered here. Likewise, no supplied passage addresses whether the EU AI Act restricts AI applications that categorize people based on facial images. Regarding app store policies, the only source available is an abstract about resolving AMM0000 errors when integrating Google Play Billing with .NET MAUI; it says nothing about Apple App Store policies on face-based attractiveness rating apps, and nothing about Google Play policies on such apps. All four research questions therefore remain unanswered by the supplied material.
Evidence ledger — supporting quotes
Does the EU AI Act restrict AI applications that infer sensitive attributes from facial images?
0%No supporting evidence
Does the EU AI Act restrict AI applications that categorize people based on facial images?
0%No supporting evidence
What do Apple App Store policies say about face-based attractiveness rating apps?
0%No supporting evidence
What do Google Play policies say about face-based attractiveness rating apps?
0%No supporting evidence
Research evidence matrix
Compare research claims with cited sources and inspect recorded excerpts. An empty cell means no inspectable excerpt was recorded; it does not establish whether a claim is true, false, or disputed. Coverage and agent confidence are not measured accuracy.
| Research claim | Inspection status |
|---|---|
| Does the EU AI Act restrict AI applications that infer sensitive attributes from facial images? | No inspectable excerpt recorded |
| Does the EU AI Act restrict AI applications that categorize people based on facial images? | No inspectable excerpt recorded |
| What do Apple App Store policies say about face-based attractiveness rating apps? | No inspectable excerpt recorded |
| What do Google Play policies say about face-based attractiveness rating apps? | No inspectable excerpt recorded |
Decision log · 63 steps
Breaking down: "Does the EU AI Act restrict AI apps that infer sensitive attributes or categorize people from facial images, and what do Apple App Store and Google Play policies say about face-based attractiveness rating apps?"
Identified 4 research target(s) to investigate; these are not established facts
Quick mode: at most 2 paid/cached/public reads, with no marketplace probe or gap-expansion round.
Web search: 2/2 planned queries attempted, 1 succeeded, 9 public page previews, 1 unavailable queries. Snippets are discovery only. Public reads spend no USDC; model and service operating costs remain separate.
Discovered 21 verified creator source(s) and 14 free public reference(s)
Recalled 11 past runs on this subject — how these sources performed when they were available.
ERC-8004 reputation loaded — composite scores on this subject.
Claim-aware portfolio (exhaustive; bounded selection, not a claim of global optimality) selected 1/1 positive proposal(s): 0 free/cache selections + 1 paid fresh selections, predicting 0/4 claim(s) above the evidence floor with $0.002000/$0.050000 fetch USDC reserved.
Free-preview pre-check covers 1/4 sub-claims (25%). The agent may buy only claim-targeted sources and will label the answer provisional if paid evidence stays thin.
Strong topical match on app, google, play, addresses sub-claim 4; worth the 0.002 USDC toll. — selected for the claim-aware evidence portfolio (targets claim 4; $0.002000 fetch USDC, 1 attention slot).
Weak match (only applications); not worth 0 USDC. - free public feed reference; no purchase or creator reward.
Already cached and still relevant (matches based, applications); reuse for free instead of paying again. - free public feed reference; no purchase or creator reward. — free-preview expected value 0.09 is below the 0.12 spend floor, so no toll is authorized.
Weak match (no key terms); not worth 0 USDC. - free public feed reference; no purchase or creator reward.
Already cached and still relevant (matches app, play); reuse for free instead of paying again. - free public feed reference; no purchase or creator reward. — the free-preview coverage check could not connect this source to any sub-claim, so no toll is authorized.
Weak match (no key terms); not worth 0 USDC. - free public feed reference; no purchase or creator reward.
Strong topical match on act, sensitive, people, facial, images, addresses sub-claim 1 & 2; worth the 0 USDC toll. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — cached bytes are free, but this read does not clear the attention gate (EV 0.32, minimum 0.45, with a required claim target).
Strong topical match on act, infer, sensitive, categorize, facial, addresses sub-claim 1 & 2; worth the 0 USDC toll. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — cached bytes are free, but this read does not clear the attention gate (EV 0.27, minimum 0.45, with a required claim target).
Strong topical match on act, infer, sensitive, based, addresses sub-claim 1 & 2; worth the 0 USDC toll. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — cached bytes are free, but this read does not clear the attention gate (EV 0.18, minimum 0.45, with a required claim target).
Strong topical match on act, facial, images, based, addresses sub-claim 1 & 2; worth the 0 USDC toll. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — cached bytes are free, but this read does not clear the attention gate (EV 0.18, minimum 0.45, with a required claim target).
Weak match (only facial); not worth 0 USDC. - free public original-page READ selection (not a cache hit); no purchase or creator reward.
Weak match (only act, images); not worth 0 USDC. - free public original-page READ selection (not a cache hit); no purchase or creator reward.
Weak match (only act, infer); not worth 0 USDC. - free public original-page READ selection (not a cache hit); no purchase or creator reward.
Strong topical match on act, infer, facial, images, based, addresses sub-claim 1 & 2; worth the 0 USDC toll. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — cached bytes are free, but this read does not clear the attention gate (EV 0.23, minimum 0.45, with a required claim target).
Weak match (only act, based); not worth 0 USDC. - free public original-page READ selection (not a cache hit); no purchase or creator reward.
Weak match (no key terms); not worth 0.003 USDC.
Weak match (no key terms); not worth 0.004 USDC.
Weak match (no key terms); not worth 0.005 USDC.
Weak match (no key terms); not worth 0.003 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Already cached and still relevant (matches apps, apple, app, store); reuse for free instead of paying again. — cached bytes are free, but this read does not clear the attention gate (EV 0.18, minimum 0.45, with a required claim target).
Already cached and still relevant (matches apps, app); reuse for free instead of paying again. — free-preview expected value 0.09 is below the 0.12 spend floor, so no toll is authorized.
Weak match (only google); not worth 0.003 USDC.
Weak match (only face); not worth 0.003 USDC.
Weak match (only google); not worth 0.004 USDC.
Weak match (only act); not worth 0.003 USDC.
Already cached and still relevant (matches apple, google); reuse for free instead of paying again. — the free-preview coverage check could not connect this source to any sub-claim, so no toll is authorized.
Weak match (only app); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.003 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Weak match (no key terms); not worth 0.002 USDC.
Agent spend wallet ready: 0x29028Fe1122E17Fe7863A22701e863FE4DaE1aFB (balance sufficient)
Paying $0.002 toll to read Conzit Labs — Resolving AMM0000 Errors in .NET MAUI with Google Play Billing…
Paid $0.002 to Conzit Labs — Resolving AMM0000 Errors in .NET MAUI with Google Play Billing (settled 09ab0e57-b…) — S1
Sub-claim "Does the EU AI Act restrict AI applications that infer sensi…": 0% covered
Sub-claim "Does the EU AI Act restrict AI applications that categorize …": 0% covered
Sub-claim "What do Apple App Store policies say about face-based attrac…": 0% covered
Sub-claim "What do Google Play policies say about face-based attractive…": 0% covered
The only gathered source (S1) is an abstract about resolving AMM0000 errors in .NET MAUI with Google Play Billing. It does not address the EU AI Act, facial-image inference of sensitive attributes, facial-image categorization of people, or Apple App Store / Google Play policies on face-based attractiveness rating apps. No sub-claim has a supported answer, and all requested parts remain missing. The assessment does not establish a complete supported answer for every requested part.
Final check — "Does the EU AI Act restrict AI applications that infer sensi…": 0% assessed
Final check — "Does the EU AI Act restrict AI applications that categorize …": 0% assessed
Final check — "What do Apple App Store policies say about face-based attrac…": 0% assessed
Final check — "What do Google Play policies say about face-based attractive…": 0% assessed
Final coverage assessment — The only gathered source (S1) is an abstract about resolving AMM0000 errors in .NET MAUI with Google Play Billing. It contains no information about the EU AI Act, facial-image inference of sensitive attributes, facial-image-based categorization of people, Apple App Store policies, or Google Play policies on face-based attractiveness rating apps. Therefore none of the four sub-claims are answered by the supplied text. The assessment does not establish a complete supported answer for every requested part.
Synthesizing a grounded answer from 1 source(s)…
Rejected 0 invalid evidence span(s) and 1 unsupported citation marker(s); rejected markers cannot receive citation rewards.
No citation passed the evidence gate — the $0.050000 citation pool stays unspent; settled access tolls still stand.
Drafted answer citing 0 source(s)
Confidence: Low — no citation passed the evidence gate.
Done. Spent $0.002 across 1 confirmed/simulated payment(s) to creators.
Portable research receipt
Take the evidence trail with you
One deterministic JSON bundle binds the answer, visible decisions, exact article versions, claim evidence and a Circle-settlement snapshot under SHA-256. Retain the digest to detect later changes; the self-check is not a publisher or Keryx signature.
Carries this dispatch’s question as context — never its answer. The next dispatch is read from sources bought for it.