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This study revisits Engelmann and Strobel (2004), which tests theoretical models of fairness preferences with simple distribution games in a classroom environment with German students. We attempt to generalize the findings from ES (2004) to a digital context with US subjects by executing three experimental waves on Amazon Mechanical Turk. As in the original study, we find similar results for efficiency concerns and ERC. However, we find that selfishness motives have larger power for rationalizing allocation decisions while maximin preferences do not explain choices in our context. We draw on a plethora of replication criteria that lead up to 80% replication success with a large variation across criteria. We do not find meaningful preference changes during the pandemic using panel data from the first to the second wave. By leveraging a third wave, we show that lower maximin motives may be attributable to cultural differences in fairness considerations while larger selfishness preferences may be driven by the high anonymity in an online relative to a classroom environment. • We attempt to generalize the findings from Engelmann & Strobel (2004). • We conduct three experimental waves with US participants on MTurk. • We show similar results on efficiency and ERC preferences. • Selfishness plays a larger role to explain choices in the digital anonymous context. • Maximin concerns do not explain choices in our US sample. • The pandemic did not seem to alter fairness preferences significantly. [ABSTRACT FROM AUTHOR]

Do beliefs about fairness interact with past experiences of labor market shocks to condition redistributive preferences? In a large-scale survey experiment, we investigate the effect of informing individuals that growth in automation could disrupt labor markets in ways that are (possibly) viewed as unfair. We then exploit the COVID-19-induced labor market shock to test for an interaction between treatments and shock exposure. We find that beliefs interact with shock exposure to increase redistributive preferences and commitments to donate potential prize winnings. Our findings suggest this may be motivated by respondents' concerns about their vulnerability to future labor market shocks. • Fairness and inequality concerns increase demand for redistribution. • Lived experience of job loss amplifies informational treatment effects. • Information about luck resonates with experiences of labor shocks. • Personal shock history informs future economic anxiety. [ABSTRACT FROM AUTHOR]

Coercion perceived by psychiatric inpatients is not exclusively determined by formal measures such as involuntary admissions, seclusion or restraint, but is also associated with patients' characteristics and professionals' attitude. This study examined how inpatients' involvement in the decision making process, the respect of their decision making preference, and their feeling of having been treated fairly mediate the relationship between involuntary hospitalisation and perceived coercion both at admission and during hospital stay. Mediation analysis were performed in order to study the relationship between involuntary hospitalisation and perceived coercion among 230 patients, voluntarily and involuntarily admitted in six psychiatric hospitals. 32.2% of the participants were involuntarily hospitalised. Taken individually, stronger participants' involvement in decision making process, better respect for their decision making preference and higher level of perceived fairness partially mediated the relationship between involuntary hospitalisation and perceived coercion by reducing the level of the latter both at admission and during the hospitalisation. In multiple mediator models, only involvement and respect played an important role at admission. During the hospitalisation, perceived fairness was the most relevant mediator, followed by involvement in decision making. During psychiatric hospitalisation patients' involvement in decision making, respect of their decision making preference and perceived fairness determined the relationship between involuntary hospitalisation and perceived coercion, but not in the same way at admission and during the stay. Involving patients in decision making and treating them fairly may be more relevant than taking account of their decision making preference in order to reduce perceived coercion. [ABSTRACT FROM AUTHOR]

This paper characterizes the Top Trading Cycles (TTC) mechanism for the school choice problem where schools may have multiple available seats to be assigned to students. We first define weaker forms of fairness, consistency, and resource monotonicity. We show that the TTC mechanism is the unique Pareto efficient and strategy-proof mechanism that satisfies these weaker forms of fairness, consistency and resource monotonicity. We also show that in a well-defined sense TTC is the "most stable" Pareto efficient mechanism. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical framework to think about employees' effort choices, and applies this framework to assess the ability of existing experimental designs to identify the effect of pay inequality on worker effort. The analysis shows that failure to control for a number of confounds—such as reciprocity towards the employer in multi-lateral gift-exchange games (vertical fairness), or the incentive to increase effort when feeling underpaid under piece rates (income targeting)—may lead to inaccurate interpretation of evidence of treatment effects. In light of these findings, the paper provides a set of recommendations on how to improve identification in the design of controlled experiments in the future. • We develop a model to study employees' effort choices in experiments. • We formally assess the design of experiments on the effect of pay inequality on effort. • We find a number of confounds that may impair identification of treatment effects. • Confounds include vertical fairness, income targeting, repeated interactions, procedural fairness. • A set of recommendations to improve identification in experiments is provided. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

Guaranteeing the monotonicity of a learned model is crucial to address concerns such as fairness, interpretability, and generalization. This paper develops a new monotonic neural network named Deep Isotonic Embedding Network (DIEN), which uses different modules to deal with monotonic and non-monotonic features respectively, and then combine outputs of these modules linearly to obtain the prediction result. A new embedding tool called Isotonic Embedding Unit is developed to process monotonic features and turn each one into an isotonic embedding vector. By converting non-monotonic features into a series of non-negative weight vectors and then combining them with isotonic embedding vectors that have special properties, we enable DIEN to guarantee monotonicity. Besides, we also introduce a module named Monotonic Feature Learning Network to capture complex dependencies between monotonic features. This module is a monotonic feedforward neural network with non-negative weights and can handle scenarios where there are few non-monotonic features or only monotonic features. In comparison to existing methods, DIEN does not require intricate structures like lattices or the use of additional verification techniques to ensure monotonicity. Additionally, the relationship between DIEN's inputs and outputs is obvious and intuitive. Results from experiments on both synthetic and real-world datasets demonstrate DIEN's superiority over existing methodologies. • A monotonic neural network named Deep Isotonic Embedding Network (DIEN) is proposed. • DIEN employs different modules to deal with monotonic and non-monotonic features. • Outputs of all modules are combined linearly to obtain the prediction result. • A module that captures complex dependencies among monotonic features is introduced. [ABSTRACT FROM AUTHOR]

The decision made by machine learning is mostly based on historical data that is used to train them. It raises the awareness that discrimination in machine learning should be eliminated because it may contain societal bias. The financial industry uses credit scoring as a reference to reflect the customer risk profile. To achieve fairness in the model, this paper tries to: (1) assess bias and (2) improve fairness in machine learning models with three bias mitigation methodologies. This study depicts that there is a trade-off between improving fairness and preserving performance. Implementing post-processing methods, for example, Grid Search performs best. [ABSTRACT FROM AUTHOR]

A legislative dispute between the city of St. Louis and the Missouri state government presents a unique opportunity to measure downward wage rigidity in the low-skill labor market. The dispute caused the minimum wage in St. Louis to fall in August 2017 after having risen by 30 percent four months earlier. In this first study of a nominal minimum wage decrease, I evaluate whether firms have a tendency to maintain existing pay levels even when they likely exceed the competitive rate. Analyzing wage trajectories in St. Louis after the minimum wage cut using synthetic controls and a regression analysis, I find that about 3.6 percentage points more workers in the area than we would otherwise expect earn above the statewide minimum wage a year later, and the 5th percentile of wages is about 80 cents higher. Anecdotal evidence is consistent with the interpretation that norms regarding fairness motivated firms to avoid wage cuts. Although I do not observe a decline in hours worked for teenagers or individuals with less than a high school degree, there is some evidence that teenage employment declines. Firms with dynamic expectations regarding a future minimum wage increase may have been particularly willing to maintain higher wages in the near term to avoid paying the costs of taking away a raise they might eventually have to grant anyway. [ABSTRACT FROM AUTHOR]

We use a laboratory experiment to investigate the extent to which leaders—faced with opportunistic incentives—employ monitoring to improve team production. Participants are assigned to teams, with one person appointed as the leader. The leader has the power to commit to a monitoring option, which replaces the default equal sharing rule with one that distributes team revenue in proportion to individual investments. Additionally, the leader can announce a claim to a portion of the team revenue, which is paid before shares are distributed. The resulting game possesses multiple equilibria involving monitoring and full investment, characterized by the largest claim the non-leaders are willing to tolerate by the leader. We appeal to behavioral considerations of fairness that help to form sharper predictions that pivot around the notion that non-leaders limit the leader to a 'fair claim'. In the experiments, leaders are only moderately successful at increasing team production as they claim too much or forgo the monitoring option too often, especially when it is costly to monitor. Still, when there is no cost of monitoring, nearly half of the leaders successfully increase team production towards full investment, by relying on constant monitoring and resisting the temptation to issue unfair claims. These results highlight the potential for opportunistic incentives to undermine efficiency-enhancing leadership, even when the leader can commit to her decisions. [ABSTRACT FROM AUTHOR]

We consider the fundamental problem of fairly allocating indivisible items when agents have strict ordinal preferences over individual items. We focus on the well-studied fairness criterion of necessary envy-freeness. For a constant number of agents, the computational complexity of the deciding whether there exists an allocation that satisfies necessary envy-freeness has been open for several years. We settle this question by showing that the problem is NP -complete even for three agents. Considering that the problem is polynomial-time solvable for the case of two agents, we provide a clear understanding of the complexity of the problem with respect to the number of agents. • Envy-free allocation of indivisible items to a few agents is computationally hard. • Finding a necessarily envy-free allocation is NP-hard already for 3 agents. • For any n ≥ 3 , finding a necessarily envy-free allocation for n agents is NP-hard. [ABSTRACT FROM AUTHOR]

The Rentier State Hypothesis states that taxation promotes government accountability. The argument is that citizens demand more accountability for spending of tax revenue than for spending of windfall revenue (e.g., natural resource revenue and aid). This paper presents causal evidence from a between-subject experiment that tests the effect of taxation on demand for accountability. To investigate the underlying mechanisms of the effect, the design focuses on two main features that distinguish tax from windfall revenue: Tax revenue is produced by citizens' work and has been in their possession before it is collected as tax. The main finding is that taxation causes a higher demand for accountability when both features of taxation are present, consistent with the Rentier State Hypothesis. The paper sheds light on the political economy of government revenues, and contributes to our understanding of how features of the tax system shape behavior. [ABSTRACT FROM AUTHOR]

Facial analysis models are increasingly applied in real-world applications that significantly impact people's lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior concerning protected groups, potentially negatively impacting individuals and society. It is, therefore, critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, this work introduces a novel learning method combining subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated datasets, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. We next show how much each individual model, each trained on a different set of annotations, contributes to the final ensemble. Finally, we empirically show that, by incorporating label diversity in the model pipeline, our method successfully mitigates unintended biases while maintaining significant accuracy on the downstream tasks. Our method is generic and can be applied to different types of facial tasks. [Display omitted] • Central hypothesis: Introducing unbiased math concepts in labels reduces biases. • We included mathematical and unbiased notions to improve facial analysis. • This is the first time a debiasing method combines objective and subjective annotations. • We generate new "objective" annotations to the dataset CelebA. • Subjective annotations are the human-based annotations to the dataset. • We show that our model achieves a better fairness metric compared to the literature. • We improved the fairness metric and kept the accuracy levels. [ABSTRACT FROM AUTHOR]

The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavouring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy. We D rop the unfair E dges and, simultaneously, we A dapt the model's parameters to those modifications, DEA in short. We introduce two covariance-based constraints designed explicitly for the link prediction task. We use these constraints to guide the optimization process responsible for learning the new 'fair' adjacency matrix. One novelty of DEA is that we can use a discrete yet learnable adjacency matrix in our fine-tuning. We demonstrate the effectiveness of our approach on five real-world datasets and show that we can improve both the accuracy and the fairness of the link prediction tasks. In addition, we present an in-depth ablation study demonstrating that our training algorithm for the adjacency matrix can be used to improve link prediction performances during training. Finally, we compute the relevance of each component of our framework to show that the combination of both the constraints and the training of the adjacency matrix leads to optimal performances. [ABSTRACT FROM AUTHOR]

• Computational thinking integration into a moral reasoning context. • Epistemic overlap between computational thinking, moral reasoning, and modeling. • Empathy as a resource for computational thinking. • Considerations for integration in socio-emotional learning settings in K12. In this work, we argue for expanding the scope of K-12 computational thinking (CT) integration contexts to include everyday scenarios involving moral reasoning. Epistemic overlap between computational thinking practices and moral reasoning suggest that these contexts are potentially rich sites to see "seeds of CT" in children's reasoning and can provide rich educational pathways for children into CT. Taking a case-study approach, we examine the reasoning of a second-grader, Ollie, on a task involving fair allocation of resources to victims of a natural disaster. Our analysis finds that Ollie's reasoning was rich with seeds of CT (e.g., problem formulation, abstraction, complex-systems thinking) and that empathy served as an important supporting role to the CT. This work has implications for curricular design, suggesting that fairness and resource allocation scenarios with built-in opportunities for empathy might provide rich sandboxes for CT integration. [ABSTRACT FROM AUTHOR]

Friendship is an important aspect of children's social lives. However, little is known about how it influences children's fairness behavior towards their peers. We tested (N = 183) pairs of children between 7 and 9 years of age from rural communities in India, Peru and Canada that are known to have divergent norms of fairness. Participants were paired with either a close friend or an acquaintance and could accept or reject different allocations of valuable resources. We experimentally compared children's responses to disadvantageous allocations (less for self) and advantageous allocations (more for self). Results showed that across the three societies children were more likely to reject disadvantageous allocations when they were paired with a friend relative to an acquaintance. When they stood to gain a relative advantage, children in Canada and to some extent Peru were more likely to reject advantageous allocations with friends, yet children in India rejected these offers rarely regardless of who they were paired with. These findings suggest that friendship may shape the expression of fairness concerns in young children, though its influence varies across societies. • We explored how friendship influences children's fairness behavior in three diverse cultural groups. • In samples from Peru and Canada, children were averse to having more resources than a friend. • In India, friendship did not affect children's tolerance of having more resources. • Across societies, children were averse to having fewer resources than a peer, especially when the peer was their friend. [ABSTRACT FROM AUTHOR]

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

Both private sector organizations and governmental health agencies increasingly use illness severity measures to adjust willingness-to-pay thresholds. Three widely discussed methods—absolute shortfall (AS), proportional shortfall (PS), and fair innings (FI)—all use ad hoc adjustments to cost-effectiveness analysis methods and "stair-step" brackets to link illness severity with willingness-to-pay adjustments. We assess how these methods compare with microeconomic expected utility theory-based methods to value health gains. We describe standard cost-effectiveness analysis methods, the basis from which AS, PS, and FI make severity adjustments. We then develop how the Generalized Risk Adjusted Cost Effectiveness (GRACE) model assesses value for differing illness and disability severity. We compare AS, PS, and FI against value as defined by GRACE. AS, PS, and FI have major and unresolved differences between them in how they value various medical interventions. Compared with GRACE, they fail to properly incorporate illness severity or disability. They conflate gains in health-related quality of life and life expectancy incorrectly and confuse the magnitude of treatment gains with value per quality-adjusted life-year. Stair-step methods also introduce important ethical concerns. AS, PS, and FI disagree with each other in major ways, demonstrating that at most, one correctly describes patients' preferences. GRACE offers a coherent alternative, based on neoclassical expected utility microeconomic theory, and can be readily implemented in future analyses. Other approaches that depend on ad hoc ethical statements have yet to be justified using sound axiomatic approaches. • Absolute severity, proportional severity, and fair innings have increasing use, but without clearly developed economic foundations. • The Generalized Risk Adjusted Cost-Effectiveness method provides a theoretically based benchmark, using preferences of representative utility-maximizing individuals, against which to evaluate these alternative severity adjustment methods. • Absolute severity, proportional severity, and fair innings all have major deficits, compared against Generalized Risk Adjusted Cost-Effectiveness methods, including possible overstatement and understatements of value, conflation of total gains with marginal value, and overstatement of life expectancy gains. Stairstep methods risk violating horizontal and vertical equity criteria. Ethical support for these methods remains unclear and incomplete. [ABSTRACT FROM AUTHOR]

• iFselect : An individually fair method of member selection for unsupervised outlier detection ensemble. • iFscore : Measures the extent of individual fairness in the output score of an outlier detector. • Maintains an overall balance of performance-fairness in the constructed ensemble. Outlier detection ensembles usually outperform individual detectors. However, the result of the ensemble could still be biased toward a group or individual data. To omit such biases, the notion of fairness was introduced. Individual fairness, in particular, aims at treating similar instances similarly. This paper proposes an individual fairness-based member selection approach (iFselect) for an outlier detection ensemble. We represent individual fairness with iFscore , which considers the relative similarity of the instances in the dataset with their outlier scores. Our method picks up members according to their iFscore from a pool of diverse detectors with varying parameters, giving a balance for performance-fairness trade-off. This enhances its suitability for the unsupervised problem of outlier detection. Moreover, our method avoids the selection of sensitive attributes which otherwise have an associated cost. Experiments on benchmark datasets suggest the superiority of iFselect in fairness, with overall performance being relatively comparable to its state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

A growing body of theoretical and empirical work suggests that our sense of fairness implies a preference for divisions of rewards that are proportional to contributions. However, there has been no study testing whether people distribute effort costs according to the expected reward distribution. We hypothesized that when people expect to share the reward of the joint task equally, they will ensure fairness by calibrating their effort investment such as to reduce inequity with respect to joint action partners' effort investment. We developed a task in which participants traded off effort costs against reward. Before making their decision, they observed as their partner performed an effort task. We examined how the perception of the partner's effort modulated effort-based decision-making in joint action, depending on whether participants were in a joint or separate reward structure, and on whether the available reward was known or unknown. Across two lab-based, pre-registered experiments (N = 57), we found support for our hypothesis, and we controlled for other candidate explanations for the observed effort matching effect such as a preference for acting jointly, learning about the value of opportunities afforded by the environment, learning the value of effort and competition. [ABSTRACT FROM AUTHOR]

People tend to dislike and punish unfair behaviors in social interactions, and this disposition may be moderated by the characteristics of their interaction partner. We used a modified ultimatum game (UG) to investigate players' responses to fair and unfair offers from proposers described as having performed either a moral transgression or a neutral behavior, and recorded an electroencephalogram. The participants' behavior in the UG suggests that people quickly demand more fairness from proposers who have committed moral transgressions rather than neutral behavior. Event-related potentials (ERPs) revealed a significant effect of offer type and of proposer type on P300 activity. The prestimulus α-oscillation power in the neutral behavior condition was significantly lower than that in the moral transgression condition. The post-stimulus β-event-related synchronization (β-ERS) was larger for the moral transgression condition than the neutral behavior condition in response to the least fair offers, and larger for neutral behavior than the moral transgression condition in response to the fairest offers. In summary, β-ERS was influenced by both proposer type and offer type, which revealed different neural responses to the offer from either a morally transgressive or a neutral behavior proposer. • People reject offers from the moral violator more often and more quickly. • The offers from the moral violators induced larger P300. • Proposer's moral behaviors induced significantly larger prestimulus α-oscillation. • Proposer's behaviors influenced β-ERS when offers are the fairest or unfairest. [ABSTRACT FROM AUTHOR]

• A new utility model of RCOCCL market incremental income distribution in China under equity preference was established. • Mathematical and numerical simulation techniques were applied in research on the level of fairness preference. • Policy suggestions for improving the distribution strategy of RCOCCL incremental benefits in China. This study explains how fairness preferences affect the allocation of income based on the practices of rural collectively-owned commercial construction land (RCOCCL) entering the market in contemporary China. The theoretical model for RCOCCL's market income allocation is extended based on Fehr and Schmidt (1999), and a fairness preference model is developed for analysis. The study makes the following conclusions. First, the stakeholders are bounded rational people with fair preferences, making them eager to narrow the income gap between themselves and others. Second, the income to local governments should be increased. Third, the more willing farmers accept the allocation scheme, the higher the benefits. Overall, this study provides new perspectives on the fair allocation of income and references for subsequent research on rural land income allocation in other developing countries. [ABSTRACT FROM AUTHOR]

Humans are not the only species that cares about inequality; individuals in some other species also respond negatively when a social partner receives a better outcome than they do and there is (limited) evidence of apes even responding when they receive more than their partner. The distribution of this reaction across species and individual's responses in experimental cooperation tasks suggests that the response to inequity evolved in the context of cooperation, potentially to help individuals recognize – and thereby avoid – partners who take more than their share. Even though this might cost individuals an absolute gain in the short term, they ultimately benefit by not being relatively disadvantaged. Thus, while clearly there are cultural influences on humans' responses to inequity, the basic response is biologically rooted, and comparative work will help us better understand why and in what contexts the responses evolved so that we can better understand how our moral and legal systems evolved in the way in which they did and thereby work to design institutions and outcomes that better benefit everyone. [ABSTRACT FROM AUTHOR]

• We study the impact of voice characteristics on fairness in speaker recognition. • There are significant relationships between gender and other speech covariates. • Age and language do not relate significantly with any speech covariates. • Vocal frequency covariates explain the most the disparate security estimates. • The spoken language has the strongest impact on the security of system. Automated speaker recognition is enabling personalized interactions with the voice-based interfaces and assistants part of the modern cyber-physical-social systems. Prior studies have unfortunately uncovered disparate impacts across demographic groups on the outcomes of speaker recognition systems and consequently proposed a range of countermeasures. Understanding why a speaker recognition system may lead to this disparate performance for different (groups of) individuals, going beyond mere data imbalance reasons and black-box countermeasures, is an essential yet under-explored perspective. In this paper, we propose an explanatory framework that aims to provide a better understanding of how speaker recognition models perform as the underlying voice characteristics on which they are tested change. With our framework, we evaluate two state-of-the-art speaker recognition models, comparing their fairness in terms of security, through a systematic analysis of the impact of more than twenty voice characteristics. Our findings include important takeaways to enable voice controlled cyber-physical-social systems for everyone. Source code and data are available at https://bit.ly/EA-PRLETTERS. [ABSTRACT FROM AUTHOR]

The impacts and implications of climate change – such as floods, droughts, heavy rainfall and increased regulation – are affecting dairy farming practices in the lower South Island (Te Waipaounamu) of Aotearoa-New Zealand. Adapting to these changes, in an equitable and transformational manner, is dependent on understanding the underlying root causes of vulnerability alongside local knowledge and values. We apply an intersectional values-based and contextual analysis to describe how past and present processes of agrarian change interact across different farmer identities to influence adaptive pathways. Local knowledge, place-based experience, values and perceptions of fairness intersect with different facets of a farmer's identity – such as financial capacity, land ownership status, debt arrangements, age and gendered participation – to enable or constrain adaptive action. Notably, notions of fairness, whether real or perceived, vary across farmer groups, and influence the kinds of adaptation activities that dairy farmers are willing, or potentially able, to engage in. The results call for more contextualised engagement with farming communities, and highlight the need to build a shared understanding of the complex historical, social, economic, cultural and environmental drivers of past, present and future change, in this highly productive, yet risky, agricultural landscape. • Intersectional values-based and contextual analyses help identify the barriers and enablers to transformational climate change adaptation in dairy farming contexts. • Dairy farmers' adaptive capacity is socially and economically differentiated, and influenced by experience, values and perceptions of fairness. • Greater contextualised engagement with dairy farmers is needed, without undermining environmental and greenhouse gas reforms. [ABSTRACT FROM AUTHOR]

• We disclosed that only balancing the sample size cannot ensure fairness. • We propose a resampling method that dynamically minimizes the loss discrepancy between groups. • We introduce a new metric to evaluate the trade-off performance between fairness and accuracy. • It achieves the best trade-off performances between fairness and accuracy in various settings. Existing classification models often output discriminatory results since they learn the target attribute without addressing data imbalance with respect to the protected attributes (e.g., gender). The models tend to focus on learning toward demographic groups containing the larger number of training samples, which consequently leads to training loss discrepancy between the groups. Our work focuses on addressing the occurrence of training loss discrepancy between the groups to improve the model's fairness. To this end, we firstly define the target-protected group using the target and protected attribute labels and observe the group-wise training loss in terms of previous fairness approaches. From the observation, we figure out that balancing the total loss across all the groups allows to mitigate fairness issue significantly, and meanwhile, only considering the sample size of each group to obtain a balanced mini-batch is not enough for mitigating fairness. Motivated by the observations, we propose a fairness-aware batch sampling scheme that adaptively updates batch sampling probability (BSP) and constructs a fairness-aware mini-batch from the model's point of view. Our key idea is to balance the training losses via training with fairness-aware mini-batch. Through extensive experiments on two facial attribute benchmark datasets and one tabular dataset, our simple and effective sampling strategy achieves superior improvement in terms of two standard fairness metrics. We validate our algorithm with various experimental settings (e.g, multi-attribute classification, binary classification with multiple protected attributes). Moreover, we introduce a new metric for measuring the trade-off between fairness and classification performance. On this metric, our algorithm also achieves the best trade-off performance. [ABSTRACT FROM AUTHOR]

We study worst-case fairness in resource allocation and cooperative games with transferable utility, the stable division most dissimilar to a normative standard of fairness. Motivated by welfare economics, similarity is quantified using information-theoretic divergences. Worst-case fairness aims to parallel the spirit of the price of anarchy from noncooperative game theory, quantifying how much unfairness is compatible with coalitional rationality. Computing worst-case fairness is tractable in weighted voting games and classes of coalitional skill games, but NP-hard in highway allocation, induced-subgraph games and some task-count coalitional skill games. In these cases we show approximation algorithms that yield constant approximations. We also upper bound the performance of a Reverse Greedy algorithm on general convex games in terms of two game-specific constants. • We study worst-case fairness in resource allocation and cooperative games with transferable utility. • We consider the imputation in the core with the largest divergence with a fixed imputation, a "standard of fairness". • We study the problem for several classes of cooperative games with transferable utility. • In cases when worst-case fairness is intractable we give algorithms that produce an approximately worst-case fair imputation. [ABSTRACT FROM AUTHOR]

We study the problem of assigning objects. There may be multiple copies of each object. Each agent is assigned at most one. Monetary transfer is not allowed. We require a rule to be fair and efficient. We introduce an axiom that is a natural weakening of no-envy. We call it "bounded no-envy". It states that for each pair of agents who receive objects of the same rank in their respective preferences, each of them should find his assignment at least as desirable as the other's. Bounded no-envy is compatible with Pareto efficiency. In particular, the immediate acceptance rules (a.k.a. the "Boston mechanisms") satisfy both. Our main result is that the immediate acceptance rules are the only rules satisfying weak non-wastefulness, bounded no-envy, rank-respecting invariance, resource monotonicity , and bilateral consistency. • We study the problem of assigning objects. • We introduce a fairness axiom called "bounded no-envy". • Bounded no-envy is compatible with Pareto efficiency. • We study the logical relations between bounded no-envy and favoring higher ranks. • Our main result is a characterization of the immediate acceptance rules. [ABSTRACT FROM AUTHOR]

In the post epidemic era, the mode of live streaming was recognized by the market. This paper research in the impact of the different levels of fairness concerns of heterogeneous consumers on the pricing strategy of live streaming. The study finds that differential pricing is conducive to improving the profits of the supply chain, but the price of consumer identification services provided by e-commerce platforms is the key factor that determines whether online celebrity anchors accept differentiated services of platform consumers. The platform (such as JD platform) with a higher level of fairness concern for ordinary consumers and a lower level of fairness concern for consumers with a preference for live broadcasting distorts the pricing of consumers with a preference for live broadcasting. The platform (such as Pinduoduo platform) with a lower level of fair concern for ordinary consumers and a higher level of fair concern for consumers with a preference for live broadcasting distorts the pricing of live broadcasting with goods to unified pricing. [ABSTRACT FROM AUTHOR]

Driven by digital technologies, high-tech product management personnel can ensure the product reliability by means of monitored working cycles. In this paper, a manufacturer's random warranty is designed to ensure the reliability during the warranty stage and a consumer's random maintenance policy is modeled to ensure the reliability during the post-warranty stage. The designed random warranty is named a two-stage two-dimensional free repair warranty (2DFRW), where the product is minimally repaired at each failure and the regions of the second stage warranty are different in order to remove all inequities resulting from the first stage warranty. [ABSTRACT FROM AUTHOR]

Using machine learning algorithms in social environments and systems requires stricter and more detailed control. More specifically, the cost of error in such systems is much higher. Therefore, one should ensure that important decisions, such as whether to convict a person or not based on the previous criminal record, are by the legal requirements and not biased toward a group of people. One can find many many papers in the literature aimed at mitigating or eliminating unwanted bias in machine learning models. A significant part of these efforts add fairness constraint to the mathematical model or adds a regularization term to the loss function. In this paper, we show that optimizing the loss function given the fairness constraint or regularization for unfairness can surprisingly yield unfair solutions. This is due to the linear relaxation of the fairness function. By analyzing the gap between the true value of fairness and the one obtained using linear relaxation, we found that the gap can be as high as around 21% for the COMPAS dataset, and around 35% for the Adult dataset. In addition, we show that the fairness gap is consistent regardless of the strength of the fairness constraint or regularization. [ABSTRACT FROM AUTHOR]

We study the parameterized complexity of finding shortest s - t -paths with an additional fairness requirement. The task is to compute a shortest path in a vertex-colored graph where each color appears (roughly) equally often in the solution. We provide an almost complete picture of the parameterized complexity landscape of the problem with respect to structural parameters by showing a tetrachotomy including polynomial kernels, fixed-parameter tractability, XP-time algorithms (and W [ 1 ] -hardness), and para-NP-hardness. [ABSTRACT FROM AUTHOR]

We show that compensation measures aimed at improving the fairness of a crisis policy response can unintendedly nudge compliance with emergency rules. We combine information on the distribution of relief funds across Italian municipalities during the novel coronavirus pandemic with data tracking citizens' movements through mobile devices and navigation systems. To assess the impact of transfers on compliance, we exploit a sharp kink schedule in the allocation of funds. The empirical analysis provides evidence that compliance increased with transfers, suggesting that the observance of emergency rules also depends on the fairness of the pandemic policy response. [ABSTRACT FROM AUTHOR]

As they take a crucial role in social decision makings, AI algorithms based on ML models should be not only accurate but also fair. Among many algorithms for fair AI, learning a prediction ML model by minimizing the empirical risk (e.g., cross-entropy) subject to a given fairness constraint has received much attention. To avoid computational difficulty, however, a given fairness constraint is replaced by a surrogate fairness constraint as the 0–1 loss is replaced by a convex surrogate loss for classification problems. In this paper, we investigate the validity of existing surrogate fairness constraints and propose a new surrogate fairness constraint called SLIDE, which is computationally feasible and asymptotically valid in the sense that the learned model satisfies the fairness constraint asymptotically and achieves a fast convergence rate. Numerical experiments confirm that the SLIDE works well for various benchmark datasets. [ABSTRACT FROM AUTHOR]

The merchant-regulatory mechanism represents a promising tool that combines the benefits of merchant investment and regulated investment, thereby providing efficient incentives for merchant Transmission Companies (Transcos) subject to regulatory compliance. Taking the H-R-G-V mechanism as a foundational example of this approach, it permits Transcos to receive the total surplus increase from investments, and the profit-maximizing Transco will perform social welfare maximum investment under this mechanism. However, one drawback of this mechanism is that it allows the Transco to receive the whole benefit created by the Transco, while excluding consumers and generators from the resultant economic benefits. To address this issue, we propose an incentive tuning parameter, which is incorporated into the calculation of the incentive fee for the Transco. Accordingly, the regulatory framework can effectively manage the Transco's profit and allow market participants to access economic benefits, thus ensuring a fair distribution of economic advantages among the stakeholders, while the impact on overall social welfare remains relatively modest. The results on the case study demonstrate that this careful balancing act maintains the essence of the H-R-G-V mechanism while addressing its critical gap—the equitable sharing of economic gains. • Regulatory incentives drive private transmission company (Transco) investments. • The H-R-G-V incentive mechanism favor the Transco, leaving market participants with no economic benefits. • Introducing an incentive tuning parameter for fair balance in public, market, and private interests. • Choosing the tuning parameter for increased market participant benefits without compromising social welfare. [ABSTRACT FROM AUTHOR]

Federated learning enables training models on distributed, privacy-sensitive medical imaging data. However, data heterogeneity across participating institutions leads to reduced model performance and fairness issues, especially for underrepresented datasets. To address these challenges, we propose leveraging the multi-head attention mechanism in Vision Transformers to align the representations of heterogeneous data across clients. By focusing on the attention mechanism as the alignment objective, our approach aims to improve both the accuracy and fairness of federated learning models in medical imaging applications. We evaluate our method on the IQ-OTH/NCCD Lung Cancer dataset, simulating various levels of data heterogeneity using Latent Dirichlet Allocation (LDA). Our results demonstrate that our approach achieves competitive performance compared to state-of-the-art federated learning methods across different heterogeneity levels and improves the performance of models for underrepresented clients, promoting fairness in the federated learning setting. These findings highlight the potential of leveraging the multi-head attention mechanism to address the challenges of data heterogeneity in medical federated learning. • Vision Transformer-based federated learning improves accuracy in heterogeneous settings. • Multi-head attention alignment enhances fairness for underrepresented clients. • Weighted averaging boosts performance, especially in highly heterogeneous environments. • Vision Transformer approach reduces need for complex multi-level optimization in FL. [ABSTRACT FROM AUTHOR]

Hospitableness within healthcare settings is increasingly recognized in the literature. However, despite the significance of electronic word-of-mouth (eWOM) behaviour, our understanding of how hospitableness influences patient perceptions and behaviours remains limited. This study utilizes fairness heuristic theory as a theoretical framework to assess how hospitableness affects patients' eWOM behaviour and explores the mediating role of perceived overall fairness (POF) and the moderating roles of treatment effectiveness and disease severity. Using data collected from 387 patients, the results reveal that hospitableness significantly enhances patients' eWOM behaviour. POF mediated the relationship between hospitableness and patients' eWOM behaviour. Furthermore, treatment effectiveness and disease severity negatively moderate the influence of hospitableness on POF. This study has both theoretical and practical implications for healthcare service management. • Hospitableness positively impacts patients' eWOM behaviour. • Perceived overall fairness mediates the relationship between hospitableness and patients' eWOM behaviour. • Treatment effectiveness moderates the relationship between hospitableness and patients' perceived overall fairness. • Disease severity moderates the relationship between hospitableness and patients' perceived overall fairness. [ABSTRACT FROM AUTHOR]

The 'just transition' concept has gained popularity in recent years and is being widely applied as a guiding concept within policy frameworks promoting low carbon transitions. Justice is at the heart of much of the policy rhetoric surrounding these transitions, favoured for the emphasis it places on fairness, equity and inclusivity. The use of justice as the guiding virtue for the transition has remained largely unquestioned. This paper examines the concept of just transition and experiments with positioning generosity as a guiding principle. It is argued that this may enable a more transformative, inclusive zero carbon future that prioritises wellbeing over mere survival. This does not seek to position a just transition and a generous transition in direct opposition to one another, instead intending to highlight how the concept of generosity might act as a catalyst for or complement to justice. • Low carbon transitions should consider interconnectedness, fairness and care. • Generosity can guide transformative, inclusive zero carbon initiatives. • Generosity can spark and complement justice. • Generosity-based frameworks can reveal values behind just transitions. • Generosity empowers collective efforts for a sustainable future. [ABSTRACT FROM AUTHOR]

The impact of individuals' fairness predispositions on public support for the welfare state receives less attention than the effect of economic self-interest on this support. Amid growing income differences even in previously egalitarian Nordic countries, predispositions about what is fair in society are rapidly becoming more politically salient. We examine how fairness predispositions towards the rich and the poor are linked to the support for three dimensions regarding how redistribution ought to be organised: access, conditionality and contribution. We then disaggregate these links for different income brackets and between elites who hold leadership positions and the rest of society (citizens). Using data pertaining to Finnish citizens and elites in 2018 and 2020, respectively, we show that the two fairness predispositions are related in various ways to the support for these three dimensions, with differences across income brackets and tentative differences between elites and citizens. These findings underline the importance of considering fairness predispositions even in welfare states emphasising economic equality. [ABSTRACT FROM AUTHOR]

Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses. • Considers a hybrid approach with a mix of different noise mitigation strategies for UAM noise mitigation. • Explores the use of ambient noise data and ambient noise masking to manage UAM's community noise impact. • Formulates a multi-objective optimization model for managing urban air traffic flow and proposes a numerical method to solve the problem. • Conducts efficiency-fairness trade-offs for aspects that involve multiple parties. [ABSTRACT FROM AUTHOR]

Ensuring equitable decision-making and distribution of costs and benefits in conservation and natural resource management is morally right and instrumental to achieving positive social and ecological outcomes. Understanding perceived equity is key; equity is subjective, context-dependent and has implications for legitimacy, cooperation and wellbeing. Since gender, in combination with other social characteristics, influences how people benefit or participate in management, examining perceived fairness from an intersectional perspective is crucial. However, few studies have examined people's perceptions of equity and how those perceptions are related to intersecting identities. Using data from ten villages in Fiji, we assess how perceptions of distributional and procedural equity differ by gender and the intersection between gender and other social identity characteristics (migrant status, age, education, marital status and wealth). We found that the majority of respondents identified the broader community as benefiting the most from management, while women were the most negatively affected. Overall, respondents' perceptions of distributional and procedural fairness were high regardless of gender. The intersection between gender and other social identity characteristics was not significantly related to perceived fairness, except in relation to migrant status; migrant men were less likely to perceive distributional fairness. Our study provides new insights into patterns of perceived (un)fairness in marine management and conservation. It reveals a discrepancy between conservation costs (women are seen as more negatively affected by conservation) and fairness perceptions (women are not more likely to perceive unfairness). Our findings can inform conservation theory and practice aimed at fostering equity in conservation and management. • People generally perceived fair distribution of management costs and benefits and fair decision-making. • High levels of perceived distributional and procedural fairness were related to the distributional criteria equality and public good. • Perceived distributional and procedural fairness did not differ by gender, even though women were more negatively impacted by management. • Little evidence of a relationship between intersectionality and fairness perceptions, except for migrant status and gender. • Migrant men were less likely to perceive distributional fairness. [ABSTRACT FROM AUTHOR]

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively. [ABSTRACT FROM AUTHOR]

The three-player evolutionary model of support for redistribution is compatible with a fairness motive; however, existing research has found near-zero effects of fairness. Here we propose an egalitarian division fairness motive, solving the problem of reward for collaboration and impacting support for redistribution. Study 1 (N = 403) showed egalitarian division fairness had additional predictive power predicting support for redistribution (β = 0.14), as well as discriminant validity from self-interest, compassion, and envy. Robustness was supported by a replication (N = 402), yielding a significant and larger effect size (β = 0.25) of egalitarian division with support for redistribution. We also examined support for coercive redistribution. In both studies, willingness to use coercive redistribution was predicted by egalitarian division fairness (S1 β = 0.15, S2: β = 0.31) and, independently, by instrumental harm (S1 β = 0.21, S2: β = 0.16). These motives expand the three-player model to include fairness and coercive enforcement, and suggest applications of evolution in developing better political, economic, and ethical knowledge. Evolved motives accounted for ~45 % of support for redistribution. • Tested if fairness impacts support for economic redistribution. • Procedural fairness unrelated to support for redistribution. • Egalitarian fairness drives support for redistribution. • A taste for coercion drives support for redistribution. [ABSTRACT FROM AUTHOR]

Government policies have focused on carbon emissions from the green supply chain (GSC). To discuss how price decision-making in GSC is affected by the manufacturer's fairness concerns, retailer's countervailing power, and carbon emissions reduction, the Stackelberg model assumes that consumers are environmentally sensitive. To explore this problem considering the type of manufacturer's behavior preferences, we built four models that pertain to the following two situations: the retailer has countervailing power, and the retailer has no countervailing power. The results show that (1) the manufacturer's disadvantageous fairness concerns reduce the overall supply chain profits and utility; influenced by countervailing power, the members witness a win-win situation; (2) moderate advantageous fairness concerns benefit GSC members; furthermore, under advantageous fairness concerns, the manufacturer can "give profits" to the retailer; (3) affected by the retailer's countervailing power, no bargaining agreement is made in the GSC under advantageous fairness concerns. Finally, the interplay between member decisions and consumer sensitivity is explored. This study can help manufacturers achieve emission reduction targets in response to consumer environmental awareness and government regulation. • Disadvantageous fairness concerns reduce the supply chain profits and utility. • The members witness a win-win situation in countervailing power. • Moderate advantageous fairness concerns benefit GSC members. • No bargaining agreement is reached in the GSC under advantageous fairness concerns. • In the GSC, market share rises with increased investments in low-carbon products. [ABSTRACT FROM AUTHOR]

Community forest management (CFM) has gained prominence globally and shown great value as a community-based conservation approach to protecting and sustainably managing forest ecosystems while, at the same time, devolving tenure rights to local populations and stimulating local livelihoods and economies. Given CFM's relative successes and socioeconomic goals, it is often assumed to be an inherently just approach. Here, we challenge that assumption. We present a framework rooted in environmental justice to uncover how some initiatives can perpetuate or exacerbate unfairness and thus undermine the core purpose and spirit of CFM. We put forward three questions on the fairness of CFM programs. First, we call to question the imposition of new CFM-related restrictions and rules, considering Indigenous and local communities' legal autonomy and/or long-standing de facto rights. Second, we interrogate the burden of CFM-related economic costs and opportunity costs, in light of communities' poverty conditions and vulnerable livelihoods. Third, we examine the fairness of focusing on the role of local communities in tackling deforestation and forest degradation, given these groups might not be the ones primarily responsible for those problems. Our discussion exposes several contradictions, trade-offs and justice implications of CFM that have remained largely unrecognized. We conclude by providing recommendations for a more just approach that centers a rights-needs-merit rationale. Our analysis is relevant for community-based conservation efforts around the globe. • We challenge the assumption that CFM initiatives are inherently just. • By imposing restrictions, CFM can perpetuate (autonomy) rights violations. • Imposing conservation-economy trade-offs on impoverished groups furthers inequalities. • CFM costs are even more unfair given communities are not the main driver of climate change. • We call for a more just approach to CFM that centers a rights-needs-merit rationale. [ABSTRACT FROM AUTHOR]

Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanilla GNNs, causing biased results toward particular demographic groups (divided by sensitive attributes, such as race and age). There have been efforts to address the fairness issue. However, existing fair techniques generally divide the demographic groups by raw sensitive attributes and assume that are fixed. The biased information correlated with raw sensitive attributes will run through the training process regardless of the implemented fair techniques. It is urgent to resolve this problem for training fair GNNs. To tackle this problem, we propose a brand new framework, FairMigration, which is able to migrate the demographic groups dynamically, instead of keeping that fixed with raw sensitive attributes. FairMigration is composed of two training stages. In the first stage, the GNNs are initially optimized by personalized self-supervised learning, and the demographic groups are adjusted dynamically. In the second stage, the new demographic groups are frozen and supervised learning is carried out under the constraints of new demographic groups and adversarial training. Extensive experiments reveal that FairMigration achieves a high trade-off between model performance and fairness. [ABSTRACT FROM AUTHOR]