consumer protection agencies

Abstract: The infamous privacy paradox refers to the apparent inconsistency between people's stated concern for privacy and their readiness to disclose personal information. This phenomenon has sparked two largely disconnected literatures: one offering experimental evidence of inconsistent behavior, and another providing qualitative accounts and defending the importance of privacy. The Article presents an online field experiment that bridges those literatures and shows that the so-called paradox arises from a mischaracterization of the underlying behavior. The Article finds that it is structural uncertainty about risk that drives seemingly paradoxical privacy decisions. It does so by isolating discounting mechanisms and empirically testing whether observed privacy choices reflect temptation or rational responses to uncertainty. The results suggest that privacy behavior is not paradoxical but, rather, consistent with choices shaped by incomplete information. The Article then discusses the policy implications of this reframing. As privacy decisions stem from structural uncertainty, which operates as a market failure, regulation should aim to reduce that uncertainty. This supports regulation that prioritizes transparency (for people to assess the risks of data collection) and flexibility mechanisms that accommodate evolving contexts. Such reframing provides a new argument for the right to be forgotten, which allows people to revisit prior disclosures as new risks become apparent. By shifting the focus from individual inconsistency to structural uncertainty, the findings call for privacy law to better reflect the reality of people's decision-making environments.

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.