462 research outputs found
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
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Debiasing training transfers to improve decision making in the field
The primary objection to debiasing training interventions is a lack of evidence that they transfer to improve decision making in field settings, where reminders of bias are absent. We gave graduate students in three professional programs (N = 290) a one-shot training intervention that reduces confirmation bias in laboratory experiments. Natural variance in the training schedule assigned participants to receive training before or after solving an unannounced business case modeled on the decision to launch the Space Shuttle Challenger. We used case solutions to surreptitiously measure their susceptibility to confirmation bias. Trained participants were 29% less likely to choose the inferior hypothesis-confirming solution than untrained participants. Analysis of case write-ups suggests that a reduction in confirmatory hypothesis testing accounts for their improved decision making in the case. The results provide promising evidence that debiasing training effects transfer to field settings and can improve consequential decisions in professional and private life
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Unexpected benefits of deciding by mind wandering
The mind wanders, even when people are attempting to make complex decisions. We suggest that mind wanderingâallowing one's thoughts to wander until the âcorrectâ choice comes to mindâcan positively impact people's feelings about their decisions. We compare post-choice satisfaction from choices made by mind wandering to reason-based choices and randomly assigned outcomes. Participants chose a poster by mind wandering or deliberating, or were randomly assigned a poster. Whereas forecasters predicted that participants who chose by mind wandering would evaluate their outcome as inferior to participants who deliberated (Experiment 1), participants who used mind wandering as a decision strategy evaluated their choice just as positively as did participants who used deliberation (Experiment 2). In some cases, it appears that people can spare themselves the effort of deliberation and instead âdecide by wind wandering,â yet experience no decrease in satisfaction
The least likely act: Overweighting atypical past behavior in behavioral predictions.
Abstract When people predict the future behavior of a person, thinking of that target as an individual decreases the accuracy of their predictions. The present research examined one potential source of this bias, whether and why predictors overweight the atypical past behavior of individuals. The results suggest that predictors do indeed overweight the atypical past behavior of an individual. Atypical past behavior is more cognitively accessible than typical past behavior, which leads it to be overweighted in the impressions that serve as the basis for their predictions. Predictions for group members appear less susceptible to this bias, presumably because predictors are less likely to form a coherent impression of a group than an individual before making their predictions
Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Becksted (2020)
In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstedâs (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims
and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do.Published versio
Interference of the End: Why Recency Bias in Memory Determines When a Food Is Consumed Again
The results of three experiments reveal that memory for enjoyment of the end rather than the beginning of a gustatory experience determines how soon people desire to repeat that experience because memory for end moments, when one is most satiated, interferes with memory for initial moments
Which social comparisons influence happiness with unequal pay?
We examine which social comparisons most affect happiness with pay that is unequally distributed (e.g., salaries and bonuses). We find that ensemble representation-attention to statistical properties of distributions such as their range and mean-makes the proximal extreme (i.e., the maximum or minimum) and distribution mean salient social comparison standards. Happiness with a salary or bonus is more affected by how it compares to the distribution mean and proximal extreme than by exemplar-based properties of the payment, like its comparison to the nearest payment or its distribution rank. This holds for randomly assigned and performance-based payments. Process studies demonstrate that ensemble representations lead people to spontaneously select these statistical properties of pay distributions as comparison standards. Exogenously increasing the salience of less extreme exemplars moderates the influence of the maximum on happiness with pay, but exogenously increasing the salience of the distribution maximum does not. As with other social comparison standards, top-down information moderates their selection. Happiness with a bonus payment is influenced by the largest payment made to others who solve the same math problems, for instance, but not by the largest payment made to others who solve different verbal problems. Our findings yield theoretical and practical insights about which members of groups are selected as social comparison standards, effects of relative income on happiness, and the attentional processes involved in ensemble representation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Accepted manuscrip
Evolution of consumption: a psychological ownership framework
Technological innovations are creating new products, services, and markets that satisfy enduring consumer needs. These technological innovations create value for consumers and firms in many ways, but they also disrupt psychological ownershipââthe feeling that a thing is âMINE.â The authors describe two key dimensions of this technology-driven evolution of consumption pertaining to psychological ownership: (1) replacing legal ownership of private goods with legal access rights to goods and services owned and used by others and (2) replacing âsolidâ material goods with âliquidâ experiential goods. They propose that these consumption changes can have three effects on psychological ownership: they can threaten it, cause it to transfer to other targets, and create new opportunities to preserve it. These changes and their effects are organized in a framework and examined across three macro trends in marketing: (1) growth of the sharing economy, (2) digitization of goods and services, and (3) expansion of personal data. This psychological ownership framework generates future research opportunities and actionable marketing strategies for firms aiming to preserve the positive consequences of psychological ownership and navigate cases for which it is a liability.Accepted manuscrip
More Intense Experiences, Less Intense Forecasts: Why People Overweight Probability Specifications in Affective Forecasts
We propose that affective forecasters overestimate the extent to which experienced hedonic responses to an outcome are influenced by the probability of its occurrence. The experience of an outcome (e.g., winning a gamble) is typically more affectively intense than the simulation of that outcome (e.g., imagining winning a gamble) upon which the affective forecast for it is based. We suggest that, as a result, experiencers allocate a larger share of their attention toward the outcome (e.g., winning the gamble) and less to its probability specifications than do affective forecasters. Consequently, hedonic responses to an outcome are less sensitive to its probability specifications than are affective forecasts for that outcome. The results of 6 experiments provide support for our theory. Affective forecasters overestimated how sensitive experiencers would be to the probability of positive and negative outcomes (Experiments 1 and 2). Consistent with our attentional account, differences in sensitivity to probability specifications disappeared when the attention of forecasters was diverted from probability specifications (Experiment 3) or when the attention of experiencers was drawn toward probability specifications (Experiment 4). Finally, differences in sensitivity to probability specifications between forecasters and experiencers were diminished when the forecasted outcome was more affectively intense (Experiments 5 and 6)
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