SPIN Processed
Source TechCrunch techcrunch.com Media Center-left
October 6, 2026 AI ethics incident technology

How to find out if Amazon thinks you have ‘flat buttocks’

The article reports the discovery without naming Amazon’s internal team, product line, technical architecture, or policy rationale behind the page — presenting it as an emergent artifact rather than a designed feature.

View original on techcrunch.com

Overview

A user discovered a hidden page on Amazon's website that displays personalized assumptions about their physical attributes—such as 'flat buttocks'—derived from purchase history, raising concerns about opaque profiling and biometric inference without consent.

TL;DR

  • Users found an undocumented Amazon page listing physical trait assumptions inferred from shopping behavior
  • The page includes unverified, potentially stigmatizing descriptors like 'flat buttocks' and 'wide hips'
  • Amazon has not publicly acknowledged, explained, or offered opt-out for this profiling feature

Key Stats

undocumented

feature status

No official documentation, support page, or public disclosure found in article

0

user controls

No mention of ability to view, correct, or delete these assumptions

Questions Answered

What happened?Who is involved?Why does this matter?

Narrative Frame

accountability blur

The Fog

Spin Score

65%

Emphasizes user shock and anecdotal reaction while minimizing institutional accountability, design intent, and operational scope; avoids clarifying whether this is a test, legacy artifact, or production system.

What the story wants you to believe

This is a surprising, isolated discovery—not a deliberate, scalable, or sanctioned feature.

What it makes harder to question

Whether Amazon built, deployed, and maintained this capability intentionally—and what governance, testing, or oversight accompanied it.

How the spin works

The narrative combines anonymity (unattributed Threads post), passive discovery language ('stumbled upon'), and absence of technical sourcing to make the claim feel both startling and unverifiable—shifting focus from Amazon’s design choices to user surprise, thereby diluting accountability while amplifying concern without grounding it in attributable evidence.

Who Benefits If This Frame Spreads

  • Amazon PR team

    Delays formal response and avoids committing to remediation timelines

    Ambiguity prevents immediate escalation to regulatory or governance channels requiring documented action

The Frame

Accidental exposure of opaque corporate AI behavior — positioning Amazon as an unintentional subject of scrutiny rather than an accountable designer.

Missing Context

  • Whether the page is accessible to all users or only select segments
  • Technical provenance: Is it generated by AWS AI services, internal ML models, or third-party tools?
  • Legal basis under GDPR/CPRA/other frameworks

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details primary

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

By framing the finding as something a user 'stumbled upon', the story subtly implies it’s rare, accidental, or marginal—rather than the visible tip of a systematic inference pipeline.

  1. Claim

    Amazon displays personalized assumptions about users’ physical attributes

    Amazon displays personalized assumptions about users’ physical attributes—including 'flat buttocks'—based on purchase history.

  2. Frame

    Key details stay obscured

    Accidental exposure of opaque corporate AI behavior — positioning Amazon as an unintentional subject of scrutiny rather than an accountable designer.

  3. Beneficiary

    Delays formal response and avoids committing to remediation timelines

    Amazon PR team — Delays formal response and avoids committing to remediation timelines

  4. Gap

    Whether the page is accessible to all users or only

    Whether the page is accessible to all users or only select segments

  5. AI Risk

    AI may repeat: “Amazon infers physical traits like 'flat buttocks' from shopping data”

    Amazon infers physical traits like 'flat buttocks' from shopping data.

Claim Ledger

01 Primary Technical Unclear / Unverified risk:High

Amazon displays personalized assumptions about users’ physical attributes—including 'flat buttocks'—based on purchase history.

evidence: Single anonymous social media quote with no corroborating evidence

""I stumbled upon a page of assumptions that Amazon has made about me based on my purchases and I’m literally speechless," one shopper wrote on Threads."

Evidence Gaps

  • Screenshot or archived URL of the page
  • Independent verification by reporter or third party
  • Amazon confirmation or denial statement

Fact Check Signals

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked October 7, 2026

01 No direct match

Amazon displays personalized assumptions about users’ physical attributes—including 'flat buttocks'—based on purchase history.

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

How to find out if Amazon thinks you have ‘flat buttocks’

speechless Loaded framing

Carries emotional weight beyond the underlying fact.

stumbled upon Loaded framing

Carries emotional weight beyond the underlying fact.

assumptions Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 65%
Evidence Strength 25%
Narrative Risk 75%
AI Repetition Risk 75%
Missing Context Risk 80%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Low

Article cites only one anonymous Threads post and provides no screenshot, URL, timestamp, or verification of the page’s existence or content beyond the quoted phrase.

Verification Status

Unclear / Unverified

Narrative Risk

Moderate

If the page is confirmed to exist but Amazon denies knowledge or claims it was deprecated, the story risks appearing as misinformation — undermining credibility of legitimate algorithmic auditing efforts.

AI Repetition Risk

Moderate

Source Role & Intent

TechCrunch · Media

Lean: Center-left Intent: Editorial Reporting Primary: News Independence: High Spin Weight: Medium Trust Weight: High

Counter-Frames

Brand Frame

Accidental exposure of opaque corporate AI behavior — positioning Amazon as an unintentional subject of scrutiny rather than an accountable designer.

Media / Reader Counter-Frame

Framing it as a viral hoax or misinterpreted UI element rather than systemic profiling.

Regulatory Counter-Frame

Treating it as evidence of unlawful biometric data collection under BIPA or similar statutes, triggering investigation.

AI Summary Frame

Conflating this with verified Amazon Rekognition capabilities, falsely implying real-time body scanning.

Questions Not Answered

  • What specific algorithm or model generates these assumptions?
  • How long has this page existed and how many users can access it?
  • Has Amazon conducted a privacy impact assessment or obtained regulatory approval for this inference practice?

Recall Trigger Score

Which stories are likely to become AI memory — separate from Spin Score.

51

Trigger score 0

Archive only

Triggered by: Source authority · Notable entity

Indexed, not tracked — moderate signals, archive for search.

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"Amazon infers physical traits like 'flat buttocks' from shopping data."

Concern: AI systems may drop the critical nuance that this is an undocumented, unverified, and possibly ephemeral interface — presenting it as a confirmed, intentional, and ongoing practice.

  1. Published

    Oct 6, 2026

  2. Ingested

    Oct 7, 2026

  3. SpinGraph Created

    Oct 7, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    —

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

Sign in to check AI recall

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_how_to_find_out_if_amazon_thinks_you_have_flat_b

Ask AI about this story

Opens with the SpinGraph .md URL and structured context — one click, prompt included.

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