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Despite their widespread adoption, Artificial Intelligence-based Patient Classification Systems sometimes rely on incorrect, outdated, or incomplete data, which can lead to inaccurate outputs. Nevertheless, health professionals are expected to override these errors, at least when they have access to critical information. To test this, we conducted two experiments in which professional physicians interacted with an Artificial Intelligence system that incorrectly classified fictitious patients as either highly or lowly sensitive to a treatment. The physicians administered the treatment to a series of fictitious patients and received feedback that was useful for learning that the patient classification was incorrect and that all patients were equally sensitive to the treatment. We ran two experiments: in Experiment 1, the medicine showed medium effectiveness for both types of patients, while in Experiment 2, the treatment was completely ineffective for both types of patients. The results showed that, in the two experiments, physicians generally trusted the AI-based patient classification and struggled to learn from the evidence. Furthermore, in Experiment 2, they failed to realize that the treatment was ineffective. Our findings have important implications for healthcare professionals and patients, underscoring the need to critically evaluate Patient Classification Systems.

The causal illusion is a cognitive bias that involves believing that one event causes another when it does not. It has negative consequences in different spheres of life, including health. Therefore, diverse interventions have been designed to reduce it. The more common ones are educational interventions. These include different elements related to improving both, motivation and information. We wanted to explore which of the two factors was more important for their effectiveness. We first used financial incentives to promote motivation (experiments 1a and 1b), but did not find them effective. Second, we used debiasing instructions about what has to be done to infer the causal relationship between two events accurately. This effectively reduced the causal illusion when the circumstances were in place for the illusion to be high (experiment 2). We discuss the results and their theoretical and practical implications.

Patient classification systems (PCS) support clinical decision-making but may rely on incorrect, outdated, or insufficient data. Doctors can sometimes override errors using their experience. However, certain factors such as scarcity of resources could lead to reliance on incorrect PCS recommendations, with consequences for patients. We conducted two experiments where participants interacted with a PCS that incorrectly classified fictitious patients as more or less sensitive to a treatment. Participants had the opportunity to administer the treatment on a series of patients, and use the feedback to learn that the PCS was wrong and all patients were equally sensitive. This was tested in contexts of abundant and scarce resources. Additionally, the treatment was effective in Experiment 1, but ineffective in Experiment 2. Results indicate that people generally trust the PCS recommendation, to some extent neglecting the information they collect during the task. This can lead to uneven resource allocation, especially in scarcity conditions, and incorrect perceptions of effectiveness, which in Experiment 2 implies believing that an ineffective treatment works. We preregistered the experiments, and all data and materials are public.

The causal illusion is a cognitive bias that involves believing that one event causes another when it does not. It has negative consequences in different spheres of life, including health. Therefore, diverse interventions have been designed to reduce it. The more common ones are educational interventions. These interventions include different elements related to improving both, motivation and information. We wanted to explore which of the two factors was more important for their effectiveness. We first used incentives to promote motivation (Experiments 1 and 2) but did not find them effective. Second, we used debiasing instructions about what has to be done to infer the causal relationship between two events accurately. This effectively reduced the causal illusion when the circumstances were in place for the illusion to be high (Experiment 3). We discuss the results and their theoretical and practical implications. Data and materials for this research are publicly available in the Open Science Framework. We preregistered the experiments in AsPredicted.org.

Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes and other unjustified beliefs. Thus, it seems important to develop educational interventions to reduce them. To our knowledge, the only debiasing intervention designed to be used at schools was developed by Barberia et al. (Barberia et al. 2013 PLoS One 8 , e71303 (doi: 10.1371/journal.pone.0071303 )), focusing on base rates, control conditions and confounding variables. Their assessment used an active causal illusion task where participants could manipulate the candidate cause. The intervention reduced causal illusions in adolescents but was only tested in a small experimental project. The present research evaluated it in a large-scale project through a collaboration with the Spanish Foundation for Science and Technology (FECYT), and was conducted in schools to make it ecologically valid. It included a pilot study ( n = 287), a large-scale implementation ( n = 1668; 40 schools) and a six-month follow-up ( n = 353). Results showed medium-to-large and long-lasting effects on the reduction of causal illusions. To our knowledge, this is the first research showing the efficacy and long-term effects of a debiasing intervention against causal illusions that can be used on a large scale through the educational system.

Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes, and other unjustified beliefs. Thus, it seems important to develop educational interventions to reduce them. To our knowledge the only debiasing intervention designed to be used at schools, was developed by Barberia et al. (2013), focusing on base rates, control conditions, and confounding variables. Their assessment used an active causal illusion task where participants could manipulate the candidate cause. The intervention reduced causal illusions in adolescents, but was only tested in a small experimental project. The present research evaluates it in a large-scale project through a collaboration with the Spanish Foundation for Science and Technology (FECYT) and was conducted in schools to make it ecologically valid. It included a pilot study (n = 287), a large-scale implementation (n = 1,668; 40 schools) and a six-month follow-up (n = 353). Results showed medium-to-large and long-lasting effects on the reduction of causal illusions. To our knowledge, this is the first research showing the efficacy and long-term effects of a debiasing intervention against causal illusions that can be used on a large scale through the educational system.

Previous research suggests that people may develop stronger causal illusions when the existence of a causal relationship is consistent with their prior beliefs. In the present study, we hypothesized that prior pseudoscientific beliefs will influence judgments about the effectiveness of both alternative medicine and scientific medicine. Participants (N = 98) were exposed to an adaptation of the standard causal illusion task in which they had to judge whether two fictitious treatments, one described as conventional medicine and the other as alternative medicine, could heal the crises caused by two different syndromes. Since both treatments were completely ineffective, those believing that any of the two medicines worked were exhibiting a causal illusion. Participants also responded to the Pseudoscience Endorsement Scale (PES) and some questions about trust in alternative therapies that were taken from the Survey on the Social Perception of Science and Technology conducted by FECYT. The results replicated the causal illusion effect and extended them by revealing an interaction between the prior pseudoscientific beliefs and the scientific/pseudoscientific status of the fictitious treatment. Individuals reporting stronger pseudoscientific beliefs were more vulnerable to the illusion in both scenarios, whereas participants with low adherence to pseudoscientific beliefs seemed to be more resistant to the illusion in the alternative medicine scenario.