People see more of their biases in algorithms.

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.

Why do people eat the same breakfast every day? Goals and circadian rhythms of variety seeking in meals.

People exhibit a circadian rhythm in the variety of foods they eat. Many people happily eat the same foods for breakfast day after day, yet seek more variety in the foods they eat for lunch and dinner. We identify psychological goals as a driver of this diurnal pattern of variety seeking, complementing other biological and cultural drivers. People are more likely to pursue hedonic goals for meals as the day progresses, which leads them to seek more variety for dinners and lunches than breakfasts. We find evidentiary support for our theory in studies with French and American participants (N = 4481) using diary data, event reconstruction methods, and experiments. Both endogenously and exogenously induced variation in hedonic goal activation modulates variety seeking in meals across days. Hedonic goal activation predicts variety seeking for meals when controlling for factors including time devoted to meal preparation and eating, the presence or absence of other people, and whether people ate a meal inside or outside their home. Goal activation also explain differences in time spent on meals, whereas increasing time spent on meals does not increase variety seeking. Finally, we observed that a similar increase in hedonic goal activation enacts a larger increase in variety seeking at breakfast than at lunch than at dinner, suggesting a diminishing marginal effect of hedonic goal activation on variety seeking.

Understanding, explaining, and utilizing medical artificial intelligence.

Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a 'black box') and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1-3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).

Unpalatable food for thought: Let marketing research guide effective public obesity interventions.

The prevalence of obesity is growing unabatedly despite the considerable efforts directed at the problem. Although abundant research has contributed to our understanding of the multifactorial causes of obesity, there is less attention to research that is relevant for guiding social marketers, public health professionals and policymakers in delivering public health interventions for countering and/or preventing the problem of obesity. This review offers six points for identifying and developing research relevant for guiding community-wide obesity interventions based on the idea that an applied marketing research perspective offers a better model for identifying effective interventions than more theoretical academic research. Specifically, the research guiding public health and social marketing interventions needs to (1) provide information on ultimate outcomes (weight, health and unintended consequences) more than intermediate outcomes (beliefs, attitudes and behaviour), (2) report on observations collected over the longer term, (3) use natural settings (even at a cost of internal validity), (4) endeavour to overcome observer-effects, (5) report effect sizes (rather than statistical significance) and (6) use moderator analyses to capture variation in how a population responds to interventions.

The impact of health claims and food deprivation levels on health risk perceptions of fast-food restaurants.

OBJECTIVE AND PROCEDURE: We examined the effect of health claims and food deprivation levels on the health risk perceptions of fast-food restaurants. Consistent with previous research, we used a within-subjects experimental design to manipulate the health claims of fast-food restaurants using real brands: Subway, expressing strong health claims vs. McDonald's, expressing weak health claims. Participants who did not have access to nutrition information were asked to estimate the health risk associated with food items that were slightly more caloric for Subway than McDonald's (640 kcal vs. 600 kcal). We collected data through a web survey with a sample consisting of 414 American adults. Based on the USDA Food Insufficiency Indicator, participants were classified into two categorical food deprivation levels: food sufficiency and food insufficiency. RESULTS AND CONCLUSIONS: We find that risk perceptions for obesity, diabetes and cardiac illnesses are lower (higher) for the restaurant with stronger (lower) health claims, i.e., Subway (McDonald's). Moreover, we also find that food deprivation levels moderate this effect, such that health risk underestimation is aggravated for individuals who suffer from food insufficiency. More precisely, we find that food insufficient individuals are more responsive to health claims, such that they perceive less health risk than food sufficient individuals for the restaurant with stronger health claims (Subway). Exploring the underlying mechanism of the latter effect, we found that dietary involvement mediates the relationship between food deprivation levels and health risk perceptions for the restaurant with stronger health claims (Subway). These results provide an interdisciplinary contribution in consumer psychology and public health.