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"If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations

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Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anikó Hannák, Gerasimos Spanakis, Konrad Kollnig

Computers and Society · Submitted 16 Sep 2026 · 84 references

Pith's verdict: Well-executed audit with honest limitations; the central claim that APIs don't proxy consumer interfaces holds up despite minor uncertainties about model versioning.

T0 · AI review

How this review was made: An AI referee (deepseek/deepseek-v4-flash) read the paper on September 17, 2026 and answered a fixed list of questions about it. T0 is the starting level every paper gets. The levels describe how deeply Pith has checked the paper, never the authors.

  1. T0 AI review An AI read the paper and answered a fixed list of questions.
  2. T1 Audit Automatic checks that the references and files the paper relies on exist and match.
  3. T2 Compute The paper's code or calculations re-run with the same results.
  4. T3 Formal statement The claims written out in a formal math language a computer can read.
  5. T4 Proof Those claims proved, and the proof checked by computer.

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arXiv ID2609.18729 (version 1)
Pith ID3VRDDBIO, Pith's permanent record of this paper and its review
CitationAnik\'o Hann\'ak, Bram Rijsbosch, Gerasimos Spanakis, Gijs van Dijck, Giovanni Astante, Konrad Kollnig, Lucas G. Uberti-Bona Marin, Thales Bertaglia. ""If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations." arXiv:2609.18729 (2026). https://pith.science/paper/3VRDDBIO
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The paper

The paper claims

AI product recommendations are not stable answers to a shopping query: different systems recommend different products with different framing, and provider APIs do not reproduce what consumers actually see.

The reading

This paper tries to establish that AI-generated product recommendations are not a stable answer to a shopping question: the same real query asked of ChatGPT, Google Gemini, and Google's AI Overviews produces different products, different framing, and different cited sources, and even repeating the identical query to one system can change the result. That instability matters because consumers increasingly treat chatbots as buying advisors, both OpenAI and Google are monetising those answers with advertising, and the European Commission has just designated ChatGPT as a Very Large Online Search Engine under the Digital Services Act, making independent auditability of such advice a regulatory requirement. Using 2,528 real user queries (the ConsumerQ dataset), the paper submits 117 physical-product queries three times through five audit conditions, namely the ChatGPT and Gemini consumer interfaces, their APIs, and AI Overviews, and measures how often the resulting recommendations and sources agree. The central negative result is that provider APIs are not a proxy for the consumer-facing product: identical queries submitted moments apart share only 12.0% of displayed domains between the ChatGPT interface and its API and 14.8% for Gemini. The paper's conclusion is methodological: neither isolated responses nor API observations can be assumed to represent the commercial advice consumers actually encounter, so audits must sample repeated responses, observe consumer-facing conditions, and record which source layer is being measured.

What carries the argument

The load-bearing object is the paired audit condition: each of 117 verbatim consumer queries is submitted to five conditions, namely the logged-out ChatGPT and Gemini consumer interfaces, their developer APIs, and Google Search's AI Overviews, with every interface request paired to an API request a median of 0.17 seconds later, and with three repetitions per condition spread hours apart from a fixed Netherlands location using fresh browser profiles. Agreement is quantified by the Jaccard overlap $J(A,B) = \frac{|A \cap B|}{|A \cup B|}$, the share of the union of two sets that both contain, applied to recommended-product sets and to cited domains and pages. The pairing carries the argument: because interface and API receive identical queries moments apart, their divergence cannot be blamed on query wording or short-term search drift and must be a property of the conditions as configured. A second mechanism is the separation of observable source layers, namely displayed citations, pages returned by web search, and grounding sources, which allows the paper to show that different access methods expose different stages of the same retrieval pipeline.

Extended reading notes

Core claim

The central claim is that AI-mediated commercial advice is a property of the whole system, not of any single response. Across 117 real shopping queries repeated three times, the consumer-facing systems disagreed systematically in how they frame advice: ChatGPT expressed a first-person preference ('my pick is...') in 79% of product-recommending responses and labelled a product as 'best' in 73%, while Gemini did so in 7% and 43% and AI Overviews in 2% and 48%. The recommended products themselves drifted across repetitions, most strongly for ChatGPT (mean Jaccard overlap of 0.178 between repeats), so two answers to the same question differed about as much as answers from two different systems. Displayed sources were shared even less: ChatGPT and Gemini had a mean domain overlap of 5.4% for the same query, with no common domain in 76.7% of comparisons, and repeated queries kept surfacing new domains (ChatGPT's observed domains per query grew from 2.65 after one request to 5.56 after three). The APIs did not reproduce their interfaces: interface and API shared 12.0% of domains for ChatGPT and 14.8% for Gemini, with systematic compositional differences (manufacturer domains dominated in the ChatGPT API; youtube.com and reddit.com in the Gemini API) and with the ChatGPT API exposing a search-return layer of 37.38 pages per observation against only 3.22 pages in the final citations. The paper concludes that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter, and that the unit of analysis for audits must become the system in use rather than the canonical answer.

Load-bearing premise

All comparisons rest on 117 physical-product queries that were filtered from real user logs by excluding anything with a price figure, a named product, a location, a date, or more than one constraint; if real shopping questions are messier than this filtered set, the measured differences may not describe what consumers actually ask.

Editorial extensions

If this is right

  • Audits of AI commercial advice should sample repeated responses and report the variance, because a single response captures only part of the source set a consumer may encounter: ChatGPT's observed domains per query grew from 2.65 after one request to 5.56 after three.
  • Regulatory and legal evidence gathering should rely on consumer-facing observations rather than API calls, since the APIs diverge from the interfaces both in which domains appear and in how sources are composed within a response.
  • The framing of advice is itself part of the product: the same query arrives as a personal endorsement from ChatGPT, a list of alternatives from Gemini, or a source-panel summary from AI Overviews, and this framing affects how consumers read the certainty of buying advice.
  • Because 76.7% of ChatGPT-Gemini comparisons share no domain at all, source disagreement across providers is structural rather than incidental, implying that retrieval and licensing choices shape the advice consumers see as much as the underlying product facts.
  • The appropriate unit for evaluating these systems shifts from 'is this answer right?' to 'have we characterised the distribution of answers, framings, and sources a consumer could receive?'

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the reported instability extends to the higher-stakes purchase categories in the paper's own codebook, namely health, safety, and financial commitments, the practical cost of a variable recommendation rises sharply, strengthening the case for treating these outputs as regulated commercial advice rather than mere information.
  • A testable extension the authors did not run: repeating the audit from several EU jurisdictions or with language-localised queries would show whether recommendations and source sets shift by country, which is precisely the evidence regulators enforcing the Digital Services Act would need.
  • The provider-specific source profiles (techradar.com and tomsguide.com for ChatGPT; reddit.com and youtube.com for Gemini) look consistent with the content-licensing and data-access deals the paper mentions, which predicts that source sets will stay stable within a provider over time even as the exact cited pages change, a distinction future audits could test directly.
  • As advertising expands inside these assistants, a before-and-after comparison of first-person framing and source composition around each ad rollout would test whether monetisation measurably changes recommendation behaviour; the paper's method of paired, repeated observations is ready-made for that study.
Original abstract

Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. First, we curate a dataset of 2,528 real commercial-advice queries (ConsumerQ). Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed.

The compiled record

The paper's own sentences, arranged, kept beside the review and separate from it. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 7,645 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.68 · human participants 0.15 · controlled comparison 0.17 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.58 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. First, we curate a dataset of 2,528 real commercial-advice queries (ConsumerQ). Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Consumers increasingly use AI chatbots for advice on what to buy. background 1.00 0.04 0.01 asserted 1.00
s1 With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. background 0.96 0.04 0.02 asserted 0.76
s2 In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. contribution 0.95 0.93 0.03 asserted 0.90
s3 First, we curate a dataset of 2,528 real commercial-advice queries (ConsumerQ). method 0.99 0.96 0.02 asserted 1.00
s4 Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). method 0.98 0.93 0.02 asserted 0.95
s5 We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. result 1.00 0.97 0.14 asserted 1.00
s6 Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. result 1.00 0.95 0.05 asserted 1.00
s7 APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. result 1.00 0.95 0.05 asserted 1.00
s8 Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. interpretation 0.95 0.84 0.63 asserted 0.99
s9 Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed. future work 0.79 0.25 0.77 asserted 0.87

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

Figures
Figure 1. From user chats to product recommendations. We highlight how C
Figure 2. ChatGPT more often highlights options as the best and/or its pick/recommendation. Product recommendation framing
Figure 4. Recommended products are inconsistent; the dif
Figure 5. When the interface and API disagree on sources, they usually disagree on the domain itself. Pages from domains
Figure 6. The API systematically surfaces different domains
Figure 7. Interfaces and APIs differ in both the types and diversity of sources they display.

Showing 6 of the paper's 7 figures. See all figures in the PDF ↗ Figures reproduced from arXiv:2609.18729 by the authors.

References

Discussion

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