Unscrupulous advertisers may try to increase attention to search ads by using offensive ads, which can increase attention and recall to the detriment of individuals and society. Here, we investigate whether offensive ads, when shown to search engine users, have such effects. We developed 12 search scenarios and created 4 versions of the search results page (SERP) for each scenario, where some of the ads were changed to be irrelevant and/or offensive. Crowdsourced judges found a strong correlation ( \(\geq 0.63\) ) between the reported number of annoying ads and the actual number of offensive and irrelevant ads, suggesting people conflate these attributes. Furthermore, we found that judges who assessed the SERPs for themselves reported lower positive affect and higher negative affect than judges asked to imagine the results were provided to someone else. In the latter case offensive ads also lead to slightly lower positive ( \(-4\%\) ) and higher negative affect ( \(+61\%\) ). Finally, in a recall test, only 6% of judges reported seeing an offensive ad when using search engines. Our work should further detract advertisers from using offensive ads since, in addition to previously documented adverse effects, such ads have a small but statistically significant negative effect on people’s emotional experience.

People increasingly rely on Artificial Intelligence (AI) based systems to aid decision-making in various domains and often face a choice between alternative systems. We explored the effects of users' perception of AI systems' warmth (perceived intent) and competence (perceived ability) on their choices. In a series of studies, we manipulated AI systems' warmth and competence levels. We show that, similar to the judgments of other people, there is often primacy for warmth over competence. Specifically, when faced with a choice between a high-competence system and a high-warmth system, more participants preferred the high-warmth system. Moreover, the precedence of warmth persisted even when the high-warmth system was overtly deficient in its competence compared to an alternative high competence-low warmth system. The current research proposes that it may be vital for AI systems designers to consider and communicate the system's warmth characteristics to its potential users.