Auditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open challenge. This study empirically investigated certification labels as a promising solution. Through interviews (N = 12) and a census-representative survey (N = 302), we investigated end-users’ attitudes toward certification labels and their effectiveness in communicating trustworthiness in low- and high-stakes AI scenarios. Based on the survey results, we demonstrate that labels can significantly increase end-users’ trust and willingness to use AI in both low- and high-stakes scenarios. However, end-users’ preferences for certification labels and their effect on trust and willingness to use AI were more pronounced in high-stake scenarios. Qualitative content analysis of the interviews revealed opportunities and limitations of certification labels, as well as facilitators and inhibitors for the effective use of labels in the context of AI. For example, while certification labels can mitigate data-related concerns expressed by end-users (e.g., privacy and data protection), other concerns (e.g., model performance) are more challenging to address. Our study provides valuable insights and recommendations for designing and implementing certification labels as a promising constituent within the trustworthy AI ecosystem.

Introduction: A conventionally grown kiwi from Spain or an organic pineapple from Ghana? Which is the more environmentally friendly option? Given that the production and distribution of food is responsible for about a quarter of our CO₂e emissions and thus plays a role in climate change the answer to such questions and, accordingly, making the right food product choices is crucial. The problem, however, is that it is difficult for consumers to calculate the CO₂e value of food as it depends on several specifications such as the type of food, origin, etc. Could carbon labeling of food circumvent this problem and help consumers make more environmentally friendly choices?Methods: In an online experiment, 402 participants had to choose 20 food products from a fictitious online shop. The participants were randomly assigned to either one of three food labeling conditions (Star Rating, Green Foot, and Traffic Light Label, short TLL) or the control condition.Results: The labeling conditions resulted in lower overall CO₂e emissions, the purchase of more green food products and fewer red food products than in the control condition. The TLL outperformed the other two labels and was also the most accepted.Discussion: The carbon TLL is therefore a promising intervention to help consumers to not only choose more environmentally friendly foods, but also make a significant contribution to the fight against climate change.

To reduce obesity and thus promote healthy food choices, front-of-pack (FOP) labels have been introduced. Though FOP labels help identify healthy foods, their impact on actual food choices is rather small. A newly developed so-called swipe task was used to investigate whether the type of label used (summary vs. nutrient-specific) had differential effects on different operationalizations of the “healthier choice” measure (e.g., calories and sugar). After learning about the product offerings of a small online store, observers (N = 354) could, by means of a swipe gesture, purchase the products they needed for a weekend with six people. Observers were randomly assigned to one of five conditions, two summary label conditions (Nutri-Score and HFL), two nutrient (sugar)-specific label conditions (manga and comic), or a control condition without a label. Unexpectedly, more products (+7.3 products)—albeit mostly healthy ones—and thus more calories (+1732 kcal) were purchased in the label conditions than in the control condition. Furthermore, the tested labels had different effects with respect to the different operationalizations (e.g., manga reduced sugar purchase). We argue that the additional green-labeled healthy products purchased (in label conditions) “compensate” for the purchase of red-labeled unhealthy products (see averaging bias and licensing effect).

Two types of newly designed pharmaceutical pictograms (with and without context) were compared with an existing type of certified pictograms regarding their search efficiency. Each of the 30 participants had to search a total of 1'090 "fictitious" medical shelves for a certain box defined by the amount and type of medical instructions given (memory size) and presented among a variable number of other boxes (set size). The boxes contained the different types of pictograms mentioned above. Calculated factorial analyses on reaction time data, among others, showed that the two newly designed pictogram types make search more efficient compared to existing types of pictograms (i.e., flatter reaction time x set size slopes). Furthermore, regardless of the type of pictogram, this set size effect became more pronounced with larger memory sizes. Overall, the newly designed pictograms need fewer attentional resources and therefore might help to increase patient adherence.