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Costs and policy-specific beliefs, such as effectiveness and fairness, are central factors for supporting environmental taxes. Less is known about how much each of these factors is limiting support. Across four experiments, I investigate to which extent high costs, ineffectiveness, and unfairness constrain support for environmental taxes. Results consistently demonstrate that perceived unfairness poses a greater barrier to support than extensive costs or ineffectiveness. These findings were robust across three environmental taxes (meat tax, plastic tax, and carbon tax), across three cultures (USA, UK, and India), and were replicated using a representative US sample. Furthermore, delving deeper into the consequences of perceived unfairness, results showed that distributional unfairness was a stronger barrier to support than procedural unfairness. Beyond limiting support, being presented with an unfair tax proposal led participants to perceive the tax as less effective and to express lower trust in policymakers than when receiving a fairer tax proposal.

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BACKGROUND: In Switzerland, the scholastic aptitude test for medical-school selection takes place in three languages. This study examined the effects of two quasi-experimental interventions that aimed to reduce existing differences in test results between the French- and German-speaking language candidates. METHODS: Between 2018 and 2023, the population of applicants to Swiss medical schools consisted of N = 18'824 German- and French-speaking individuals. Based on a quasi-experimental design, we examined the effects of two interventions regarding preparatory material, in these cohorts. The first intervention (2022) consisting of practice trials in baccalaureate schools in the canton of Fribourg enabled French-speaking candidates primarily from the canton of Fribourg to prepare more intensively with official tasks. Practice trials enable future candidates to complete a published test version under original conditions and thus prepare how to approach the real test. The second intervention (2023) released new preparatory material in all languages for one group of tasks for which differences between the language groups were more pronounced than in the other tasks. The test provider offered this material for free download together with existing preparation materials and thereby enabled more intensive preparation. RESULTS: After the first intervention, the initially small to medium-sized mean differences in z-transformed test scores between French-speaking candidates from Fribourg and German-speaking candidates were nearly eliminated (from 0.39 to 0.05). Also for French-speaking candidates from outside of the canton of Fribourg, the mean differences were smaller than before the intervention (0.48 before, 0.39 after first intervention). After the second intervention, particularly the mean differences in test scores between German-speaking and French-speaking candidates from outside of Fribourg were further reduced (to 0.24). CONCLUSIONS: The two interventions regarding material for preparing to participate in the aptitude test affected candidates' test scores. They reduced the gap between German- and French-speaking candidates showing that the additional benefits of commercial offers for test preparation are limited. Hence, offering comparable official preparation material to all language groups enhances test fairness.

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Third-party punishment (TPP) plays an irreplaceable role in maintaining social fairness. Punishment power is a significant area of study within economic games. However, the impact of whether or not the second-party possesses punishment power on TPP remains unexplored. The present study utilizes the high temporal resolution of EEG and time-frequency analysis, intra-barin functional connectivity analysis, inter-brain synchronization (IBS) analysis, and granger causality analysis(GCA) to comprehensively explore the neural mechanism of TPP from the perspective of third-party individual's decision-making and IBS in the real-time social interaction. Time-frequency results found that, the absence of the punishment power activated more theta-band and alpha-band power compare to when second-party has punishment power. When second-party has no punishment power, functional connection results observed stronger functional connectivity in theta band for medium unfair offers between rTPJ and PFC. Dual-brain analysis revealed that when the second-party has no punishment power, there is a significantly higher IBS in the alpha band between the frontal and frontal-central lobes of the second-party and the parietal and parietal occipital lobes of the third-party. GCA results further showed that the direction of IBS from third-party to second-party was significantly stronger than from second-party to third-party. This study demonstrates that the absence of the second-party's punishment power promote TPP, and similar cognitive process of thinking on how to maintain social fairness enhances IBS. The current study emphasizes the influence of punishment power on TPP, broadens the research perspective and contributes crucial insights into maintain social fairness.

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Background: Achieving universal health insurance coverage has become a fundamental policy for improving the accessibility and equity of healthcare services. China's Urban-Rural Resident Basic Medical Insurance (URRBMI) is a crucial component of the social security system, aimed at promoting social equity and enhancing public welfare. However, the effectiveness of this policy in improving rural residents' social fairness perceptions (SFP) remains to be tested. Objective: To examine the impact of the urban-rural resident basic medical insurance (URRBMI) on rural residents' social fairness perception (SFP) in China. Methods and samples: The study utilizes city-level and national micro-survey (CGSS) datasets, applying a time-varying difference-in-difference (DID) approach to analyze the equity effects of URRBMI. Excluding urban samples, the final dataset consists of 20,800 rural respondents from 2010, 2011, 2013, and 2015, covering 89 cities. Results: Key findings reveal that URRBMI has a significant negative effect on SFP. The impact varies depending on the integration model and intensifies over time. Additionally, the negative effect shows heterogeneity based on income, age, health, and region. Conclusion: This study highlights the complexities and impacts of integrating China's urban and rural healthcare systems. It provides a detailed understanding of the role of URRBMI in rural China, emphasizing the need for targeted approaches to improve rural residents' perceptions of social fairness. The research offers specific policy recommendations, such as establishing differentiated contribution standards, implementing welfare policies favoring rural residents, and adopting varied reimbursement rates for different diseases.

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It is expected that machine learning algorithms will enable better diagnosis, prognosis, and treatment in psychiatry. A central argument for deploying algorithmic methods in clinical decision-making in psychiatry is that they may enable not only faster and more accurate clinical judgments but also that they may provide a more objective foundation for clinical decisions. This article argues that the outputs of algorithms are never objective in the sense of being unaffected by human values and possibly biased choices. And it suggests that the best way to approach this is to ensure awareness of and transparency about the ethical trade-offs that must be made when developing an algorithm for mental health.

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In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.

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Introduction: Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities. Methods: We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance. Results: The precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race. Conclusions: Racial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it.

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Background: The development of a stable society is closely linked to a prevalent sense of social fairness. Participating in physical activities, which are inherently social, plays a crucial role in fostering mental stability within social contexts. Objective: This study aims to examine how physical exercise influences the sense of social fairness among college students, focusing on the potential mediating effects of perceived social support and life satisfaction. Methods: The study surveyed 496 Chinese college students using several scales: the Physical Activity Rating Scale-3 (PARS-3), Perceived Social Support Scale (PSSS), Satisfaction with Life Scale (SWLS), and Social Justice Scale (SJS). Results: (1) A positive correlation was found between physical exercise and sense of social fairness (r = 0.151, p < 0.01). A significant direct effect of physical exercise on sense of social fairness was also observed (ß = 0.151, t = 3.971, p < 0.01). (2) Physical exercise was a positive predictor of perceived social support (ß = 0.113, t = 4.062, p < 0.01), which in turn positively influenced both life satisfaction (ß = 0.333, t = 18.047, p < 0.01) and sense of social fairness (ß = 0.485, t = 6.931, p < 0.01). Additionally, life satisfaction had a positive effect on sense of social fairness (ß = 0.431, t = 3.247, p < 0.01). (3) Both perceived social support and life satisfaction significantly mediated the relationship between physical exercise and sense of social fairness through two pathways: physical exercise → perceived social support → sense of social fairness (mediating effect: 0.055); and physical exercise → perceived social support → life satisfaction → sense of social fairness (mediating effect: 0.016). Conclusion: (1) Physical exercise enhances both perceived social support and the sense of social fairness among college students, suggesting that it not only directly contributes to an enhanced sense of social fairness but also fosters supportive social relationships. (2) The influence of physical exercise on the sense of social fairness operates both directly and indirectly through the mediating roles of perceived social support and, sequentially, life satisfaction.

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Patient demand for procedures has increased in the evolving landscape of cosmetic dermatology. This has been fueled, in part, by social media and the growing normalization of cosmetic enhancements; however, this has led some patients to have potentially unrealistic expectations, placing undue pressure on dermatologists to meet these often unrealizable demands. This pressure is further exacerbated by patients who are seen as difficult, demanding, and time-consuming and who may require extensive counseling. Physicians may adopt dynamic or differential pricing strategies to offset the additional time and effort these patients require. We discuss the ethical concerns surrounding these pricing strategies in the cosmetic sphere, highlight the importance of transparency in pricing, and offer suggestions to promote clarity and fairness in cosmetic dermatology practices.

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The purpose of a first-in-human (FIH) clinical trial is to gather information about how the drug or device affects and interacts with the human body: its safety, side effects, and (potential) dosage. As such, the primary goal of a FIH trial is not participant benefit but to gain knowledge of drug or device efficacy, i.e., baseline human safety knowledge. Some FIH clinical trials carry significant foreseeable risk to participants with little to no foreseeable participant benefit. Participation in such trials would be a bad deal for participants, and the research is considered justifiable because of the promise of significant potential social benefit. I argue that there is an ethical tension inherent in risky FIH research and that researchers should fairly compensate risky FIH trial participants. This does not make the risk-benefit outcome more favorable for participants; rather, it amounts to a collective reckoning with the ethical tension inherent in the research.

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The 2030 Sustainable Development Goals (SDG) agenda has committed to 'ensuring that no one is left behind'. Applying the right to health of non-citizens and international migrants is challenging in today's highly polarized political discourse on migration governance and integration. We explore the role of a priority setting approach to help support better, fairer and more transparent policy making in migration health. A priority setting approach must also incorporate migration health for more efficient and fair allocation of scarce resources. Explicitly recognizing the trade-offs as part of strategic planning, would circumvent ad hoc decision-making during crises, not well-suited for fairness. Discussions surrounding decisions about expanding services to migrants or subgroups of migrants, which services and to whom should be transparent and fair. We conclude that a priority setting approach can help better inform policy making by being more closely aligned with the practical challenges policy makers face towards the progressive realization of migration health.

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Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes. Methods: This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one's demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state. Results: Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models' ability to accurately estimate healthcare outcomes for the sensitive groups. Discussion: These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.

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AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid. To show this behavioral shift, we recruited participants to play the ultimatum game, where they were asked to decide whether to accept proposals of monetary splits made by either other human participants or AI. Some participants were informed their choices would be used to train an AI proposer, while others did not receive this information. Across five experiments, we found that people modified their behavior to train AI to make fair proposals, regardless of whether they could directly benefit from the AI training. After completing this task once, participants were invited to complete this task again but were told their responses would not be used for AI training. People who had previously trained AI persisted with this behavioral shift, indicating that the new behavioral routine had become habitual. This work demonstrates that using human behavior as training data has more consequences than previously thought since it can engender AI to perpetuate human biases and cause people to form habits that deviate from how they would normally act. Therefore, this work underscores a problem for AI algorithms that aim to learn unbiased representations of human preferences.

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The goal of this paper was to describe the context within which the PASS theory of intelligence was conceived and the reasons why this theory was used to guide the construction of the Cognitive Assessment System and the several versions of the Cognitive Assessment System, 2nd Edition. We also discuss validity issues such as equitable assessment of intelligence, using PASS scores to examine a pattern of strengths and weaknesses related to academic variability and diagnosis, and the utility of PASS scores for intervention. We provide summaries of the research that informs our suggestions that intelligence testing should be theory-based, not constrained by the seminal work of test developers in the early 1900s, and neurocognitive processes should be measured based on brain function.

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BACKGROUND: Sharing and fairness are important prosocial behaviors that help us navigate the social world. However, little is known about how and whether individuals with Williams Syndrome (WS) engage in these behaviors. The unique phenotype of individuals with WS, consisting of high social motivation and limited social cognition, can also offer insight into the role of social motivation in sharing and fairness when compared to typically developing (TD) individuals. The current study used established experimental paradigms to examine sharing and fairness in individuals with WS and TD individuals. METHODS: We compared a sample of patients with WS to TD children (6-year-olds) matched by mental age (MA) on two experimental tasks: the Dictator Game (DG, Experiment 1, N = 17 WS, 20 TD) with adults modeling giving behaviors used to test sharing and the Inequity Game (IG, Experiment 2, N = 14 WS, 17 TD) used to test fairness. RESULTS: Results showed that the WS group behaved similarly to the TD group for baseline giving in the DG and in the IG, rejecting disadvantageous offers but accepting advantageous ones. However, after viewing an adult model giving behavior, the WS group gave more than their baseline, with many individuals giving more than half, while the TD group gave less. Combined these results suggest that social motivation is sufficient for sharing and, in particular, generous sharing, as well as the self-focused form of fairness. Further, individuals with WS appear capable of both learning to be more generous and preventing disadvantageous outcomes, a more complex profile than previously known. CONCLUSIONS: In conclusion, the present study provides a snapshot into sharing and fairness-related behaviors in WS, contributing to our understanding of the intriguing social-behavioral phenotype associated with this developmental disorder.

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To better understand the individual differences in fairness, we used event-related potentials (ERPs) to explore the fairness characteristics of deaf college students through the ultimatum game task. Behaviorally, the significant main effect of the proposal type was found, which meant both deaf and hearing college students showed a lower acceptance rate for the more unfair proposal. Interestingly, we found a significant interaction between group and proposal type in the early stage (N1). Moreover, in the deaf college group, N1 (induced by moderately and very unfair proposals) was significantly larger than that of fair proposals. However, we found that deaf college students had smaller amplitudes on P2 and P3 than hearing college students. These results suggested that deaf college students might pursue more equity strongly so they are more sensitive to unfair information in the early stage. In a word, we should provide more fair allocations for deaf college students in our harmonious society.

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The equitable allocation of resources has long been a central concern for humanity, prompting extensive research into various motivations that drive the pursuit of distributive justice. In contrast to one of the most fundamental motives, inequality aversion, a conflicting motive has been proposed: rank-reversal aversion. However, it remains unclear whether this rank-reversal aversion persists in the presence of self-rank. Here we provide evidence of rank-reversal aversion in the first-party context and explore diverse moral strategies for distribution. In a modified version of the redistribution game involving 55 online-recruited participants, we observed rank-reversal aversion only when one's rank was maintained. When participants' self-rank was altered, they tended to base their behavior on their new ranks. This behavioral tendency varied among individuals, revealing three distinct moral strategies, all incorporating considerations of rank-reversal. Our findings suggest that rank-reversal aversion can indeed influence one's distribution behavior, although the extent of its impact may vary among individuals, especially when self-rank is a factor. These insights can be extended to political and economic domains, contributing to a deeper understanding of the underlying mechanisms of distributive justice.

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Economic decision-making plays a paramount role in both individual and national interests. Individuals have fairness preferences in economic decision-making, but a proposer's moral-related information may affect fairness considerations. In prior ERP studies, researchers have suggested moral identity influences fairness preferences in the Ultimatum Game (UG), but there are discrepancies in the results. Furthermore, whether role models (individuals whom someone else looks to help decide suitable behaviors), who can modulate people's moral standards, can affect fairness concerns in UG is still understudied. To address the questions, we selected the moral-related statements by eliminating those with illegal information and employed the ERP technique to explore whether the interplay of the proposer's role model and moral-related behavior influenced fairness processing in the modified UG and the corresponding neural mechanisms. We mainly found that the aforementioned interaction effect on proposal considerations in UG could be mirrored in both rejection rates and P300 variations. The results demonstrate that the interaction between the proposer's role model and moral behavior can modulate fairness concerns in UG. Our current work provides new avenues for elucidating the time course of the influencing mechanism of fair distributions in complicated social environments.

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