Auditing AI-Washing in German Startups: A Mixed-Methods Study of Marketing Claims and Perceptions

arXiv:2610.11788v1 Announce Type: new Abstract: AI-washing is the use of 'AI' as a marketing term where products' claims are exaggerated, likely to capture the hype from investors and consumers. In this paper, we examine how startups market AI and how people perceive those claims. We audited the German startup market, applying an annotation codebook of seven dimensions to 100 startups and conducting 63 expert interviews. Startups exaggerated AI capability in three forms: claims without inspectable evidence, performance numbers without methodology, and claims without acknowledged limits. The capabilities they claimed rarely matched the underlying system, and consumers who had encountered exaggerated claims before withdrew trust from later AI marketing. AI-washing has also been linked to the marketing of unethical products: 38% of the sample were flagged for moderate or serious ethical concern, in domains such as surveillance and automated decisions in regulated fields. Together these findings show how AI-washing happens: founders exaggerate capability to attract investors who reward AI branding they cannot verify, and consumers in unfamiliar domains take the marketing at face value. We recommend that startup AI claims be brought under disclosure requirements, with the codebook offered to regulators, investors, and consumer-protection bodies for routine audits.

AI-washing leads to consumer distrust and unethical product marketing

This is a rigorous empirical audit documenting systemic deception in AI marketing and its link to ethically concerning products, which has real negative implications for trust and accountability. It is measured evidence, not speculation, though the sample is one national market.

The study found significant exaggeration of AI capabilities by startups, leading to consumer mistrust and ethical concerns. This misrepresentation can harm societal trust in AI.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   6687ae7f-0573-43eb-9290-89cf29f22070
           https://arxiv.org/abs/2610.11788
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EVIDENCE   3 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      -77  (Strongly adverse)
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CONFIDENCE 85%