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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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.

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.

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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

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.

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset. A novel evaluation approach is introduced in this research based on k-fold cross-validation and statistical t-tests to assess the fairness of ML algorithms. This approach was exercised across five benchmark datasets using six classical ML algorithms. Considering four fair ML definitions guided by the current literature, our analysis showed that the same dataset generates a fair outcome for one ML algorithm but an unfair result for another. Such an observation reveals complex, context-dependent fairness issues in ML, complicated further by the varied operational mechanisms of the underlying ML models. Our proposed approach enables researchers to check whether deploying any ML algorithms against a protected attribute within datasets is fair. We also discuss the broader implications of the proposed approach, highlighting a notable variability in its fairness outcomes. Our discussion underscores the need for adaptable fairness definitions and the exploration of methods to enhance the fairness of ensemble approaches, aiming to advance fair ML practices and ensure equitable AI deployment across societal sectors.