Level Of Fairness Research Articles

Analysis on Pricing Strategy of Live Streaming for Heterogeneous Consumers Considering Fairness Concern

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.

Appraisal of high-stake examinations during SARS-CoV-2 emergency with responsible and transparent AI: Evidence of fair and detrimental assessment

In situations like the coronavirus pandemic, colleges and universities are forced to limit their offline and regular academic activities. Extended postponement of high-stakes exams due to health risk hereby reduces productivity and progress in later years. Several countries decided to organize the exams online. Since many other countries with large education boards had an inadequate infrastructure and insufficient resources during the emergency, education policy experts considered a solution to simultaneously protect public health and fully resume high-stakes exams -by canceling offline exam and introducing a uniform assessment process to be followed across the states and education boards. This research proposes a novel system using an AI model to accomplish the complex task of evaluating all students across education boards with maximum level of fairness and analyzes the ability to fairly appraise exam grades in the context of high-stakes examinations during SARS-CoV-2 emergency. Basically, a logistic regression classifier on top of a deep neural network is used to output predictions that are as fair as possible for all learners. The predictions of the proposed grade-awarding system are explained by the SHAP (SHapley Additive exPlanations) framework. SHAP allowed to identify the features of the students' portfolios that contributed most to the predicted grades. In the setting of an empirical analysis in one of the largest education systems in the Global South, 81.85% of learners were assigned fair scores while 3.12% of the scores were significantly smaller than the actual grades, which would have had a detrimental effect if it had been applied for real. Furthermore, SHAP allows policy-makers to debug the predictive model by identifying and measuring the importance of the factors involved in the model's final decision and removing those features that should not play a role in the model's “reasoning” process.

FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models.

Graph mining is an essential component of recommender systems and search engines. Outputs of graph mining models typically provide a ranked list sorted by each item's relevance or utility. However, recent research has identified issues of algorithmic bias in such models, and new graph mining algorithms have been proposed to correct for bias. As such, algorithm developers need tools that can help them uncover potential biases in their models while also exploring the impacts of correcting for biases when employing fairness-aware algorithms. In this paper, we present FairRankVis, a visual analytics framework designed to enable the exploration of multi-class bias in graph mining algorithms. We support both group and individual fairness levels of comparison. Our framework is designed to enable model developers to compare multi-class fairness between algorithms (for example, comparing PageRank with a debiased PageRank algorithm) to assess the impacts of algorithmic debiasing with respect to group and individual fairness. We demonstrate our framework through two usage scenarios inspecting algorithmic fairness.

Improved Trickle Algorithm Toward Low Power and Better Route for the RPL Routing Protocol

The IPv6 Routing Protocol for Low Power and Lossy networks (RPL) is a routing protocol standardized by the Internet Engineering Task Force. According to the specification of the protocol, the Trickle algorithm is adopted for the dissemination of route construction information among nodes in such networks. Since the algorithm is originally designed for code propagation and maintenance in Wireless Sensor Networks, when using the algorithm for the route formation of the network there exist some problems, such as the route convergence time, the fairness issue among nodes, and the amount of power consumption. Therefore, the paper proposes an improved Trickle algorithm, namely FI-Trickle, by taking a new approach to simultaneously reduce the unfairness among nodes and the power consumption and to improve the packet delivery ratio of the network. The performance of FI-Trickle is verified via simulation with extensive experiments over various network sizes, interference conditions, and network topologies. For comparison purposes, the extensive experiments are applied not only to FI-Trickle, but also to various Trickle variants such as Trickle, Trickle-F, I-Trickle, and Drizzle. The simulation results show that FI-Trickle can use less power to achieve a similar fairness level as compared to Trickle-F. Also, in terms of the packet delivery ratio, as the network size grows, FI-Trickle can increasingly outperform Trickle-F, Trickle, Drizzle, and I-Trickle by up to 1%, 3%, 3%, and 4%, respectively. This end result implies that FI-Trickle can be a better Trickle candidate for the dissemination of the RPL message for large-scale networks.

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

Customer fairness perceptions in augmented reality-based online services

PurposeAn increasing number of retailers is trying to stimulate customers by embedding augmented reality (AR) features such as video try-on into the online shopping experience. As such AR-based online services require customers to actively participate in the service provision, this paper aims at investigating fairness perceptions and customer responses associated with AR-enabled customer participation.Design/methodology/approachThe conceptual framework of this study is based on equity theory. To compare customer responses after an in-store service encounter as opposed to AR-enabled customer participation involving video try-on, this study contains a between-subject online experiment. The effective sample comprises N = 215 participants.FindingsThe data analysis demonstrates that AR-enabled customer participation leads to significantly lower levels of distributive, procedural and price fairness as well as lower engagement intentions than in-store service encounters. Simultaneously, participants in the video try-on scenario report higher negative word-of-mouth (WOM) intentions than in the in-store scenario.Research limitations/implicationsThe extra mile customers go when using AR-based online services is reflected in less favorable fairness evaluations.Practical implicationsService managers should design AR applications in a manner that requires minimum customer participation.Originality/valueThis study contributes to service research by linking AR-enabled customer participation to evaluations of distributive, procedural and price fairness and their outcomes. This is vital to fully exploit the potential of AR in services.

Exploring the Governance and Fairness in the Milk Value Chain: A Case Study in Vietnam

Governance and fairness in the food value chain have gained considerable attention from both policymakers and scholars, especially in developing countries. This study analysed the milk value chain, exploring its governance and fairness, and assessed the regulatory interventions across the milk value chain in Vietnam using a qualitative framework and the global value chain governance model. The results show that Vietnam’s milk production and dairy market have developed notably since the reforms. The value chain is structured according to three governance models, i.e., relational, captive, and hierarchy models. Vietnam’s milk value chain has progressed through three building phases, expanding in breadth, and undergoing in-depth development, and the governance models have adjusted accordingly. However, Vietnamese dairy farms have been exposed to a low level of fairness across the supply chain. Although dairy farmers in the relational model may benefit from more power and fairness in the short term, farmers in the captive model may gain benefits and potential fairness in the long term. Vietnam has diverse regulatory interventions to enhance farmers’ fairness and welfare, and the results are notable. However, not all farmers have benefitted from these policies, and measures regarding fairness and welfare should be diverse, gradual, and inclusive.

Eliciting Fairness in N-Player Network Games through Degree-Based Role Assignment

From social contracts to climate agreements, individuals engage in groups that must collectively reach decisions with varying levels of equality and fairness. These dilemmas also pervade distributed artificial intelligence, in domains such as automated negotiation, conflict resolution, or resource allocation, which aim to engineer self-organized group behaviors. As evidenced by the well-known Ultimatum Game, where a Proposer has to divide a resource with a Responder, payoff-maximizing outcomes are frequently at odds with fairness. Eliciting equality in populations of self-regarding agents requires judicious interventions. Here, we use knowledge about agents’ social networks to implement fairness mechanisms, in the context of Multiplayer Ultimatum Games. We focus on network-based role assignment and show that attributing the role of Proposer to low-connected nodes increases the fairness levels in a population. We evaluate the effectiveness of low-degree Proposer assignment considering networks with different average connectivities, group sizes, and group voting rules when accepting proposals (e.g., majority or unanimity). We further show that low-degree Proposer assignment is efficient, in optimizing not only individuals’ offers but also the average payoff level in the population. Finally, we show that stricter voting rules (i.e., imposing an accepting consensus as a requirement for collectives to accept a proposal) attenuate the unfairness that results from situations where high-degree nodes (hubs) play as Proposers. Our results suggest new routes to use role assignment and voting mechanisms to prevent unfair behaviors from spreading on complex networks.

Game Theory-Based Power Allocation Strategy for NOMA in 5G Cooperative Beamforming

Non-orthogonal multiple access (NOMA) is a Fifth Generation (5G) technique that allows many users to simultaneously access the same time–frequency separating channels via successive interference cancellation (SIC) receiver. Cooperative NOMA (CNOMA) is an effective tool to prevent performance degradation of far users by allocating minimal power to users with good channel conditions. In this paper, we proposed a fair power and channel allocation scheme based on the Nash bargaining solution (NBS) game solution in full-duplex, cooperative beamforming (BF) for multicarrier (MC) NOMA. The proposed NBS scheme assigns optimal power and channel allocation according to channel conditions while maintaining a fair rate amongst cooperative users. NBS provides a fair and optimum approach for maximizing the total rate of CNOMA. The signal-to-leakage (SLR) ratio precoding technique is considered as a design performance criterion for beamforming vector towards achieving power domain CNOMA players. Simulation results show that at BER = $${10}^{-5}$$ , the NBS power allocation (proposed scheme) improved by 2 dB in terms of Signal-to-Noise Ratio (SNR), compared with the non-cooperative scheme, and 3 dB compared with the multiple-input multiple-output NOMA (MIMO-NOMA). Both improvements were as a result of interference reduction and information sharing in the network. In terms of fairness, the proposed NBS scheme shown a high level of fairness at 0.8401, compared to the other similar approaches in the literature.

A machine learning-based approach to identify unlawful practices in online terms of service: analysis, implementation and evaluation

Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests. Secondly, we introduce a novel machine learning-based approach to classify ToS clauses, represented by using sentence embedding, in different categories classes and fairness levels. Results of a test involving well-known machine learning models show that Support Vector Machine is able to classify clauses into categories with a F1-score of 86% outperforming state-of-the-art methods, while Random Forest is able to classify clauses into fairness levels with a F1-score of 81%. With the final goal of making terms of service more readable and understandable, we embedded this approach into ToSware, a prototype of a Google Chrome extension. An evaluation study was performed to measure ToSware effectiveness, efficiency, and the overall users’ satisfaction when interacting with it.

Fairness and behavioral intentions in discrete B2B transactions: a study of small business firms

Purpose While studies about business-to-business (B2B) relationships have mainly addressed buyer–supplier long-term exchanges, focusing on social outcomes such as trust, commitment and cooperation, there is little research that explores the social outcomes which stem from short-term B2B transactions. The purpose of this paper is to explain buyers’ intention to renew a contract after discrete and time-delimited transactions by suggesting a model that complements social exchange theory with theories of fairness. In detail, this study aims to determine how evaluations of economic and social outcomes are complemented by both procedural fairness and distributive fairness. Design/methodology/approach The hypotheses are tested in the social couponing industry with a survey of a sample of 199 firms purchasing advertising services from daily deal websites. Data are analyzed using covariance-based structural equation modeling (CB-SEM). Findings The findings reveal direct effects of procedural fairness on social outcomes (satisfaction) and distributive fairness on the intention to renew a contract, negative moderating effect of procedural fairness on the relationship between economic outcomes (campaign effectiveness) and social outcomes (satisfaction). Research limitations/implications In discrete, time-delimited transactions, high levels of procedural fairness may partially compensate for low levels of economic outcomes and prevent a reduction in social outcomes. Hence, when economic outcomes are influenced largely by external, uncontrollable conditions, the buyer seems to appreciate the supplier’s efforts to behave fairly. Practical implications Social outcomes matter even in discrete transactions and considerations of fairness should be integrated in the management of discrete transactions. Sharing economic outcomes fairly is not sufficient to secure the buyer’s intention to renew the contract. Originality/value This study proposes and tests a model that complements social exchange theory with theories of fairness and explains contract renewal in discrete, time-delimited transactions, encompassing both economic outcomes and social outcomes.

Ethical and unethical behaviour of built environment professionals in the Ghanaian construction industry

PurposeThe construction industry is an enabler of economic growth in developing countries, but its performance is governed by the professional behaviour of construction professionals. Unethical behaviour (UB) breaches codes of practice and undermines economic performance hence, ubiquitous academic attention has been given to understanding this phenomenon. This paper aims to contribute to the ensuing discourse by reporting upon the most critical ethical behaviours (EBs and UBs) of professionals in the Ghanaian construction industry (GCI).Design/methodology/approachThe study compounded identified factors into a closed-ended questionnaire in a quantitative research strategy. Data analysis was conducted using the relative importance index and one samplet-test. To measure the reliability of the scale, Cronbach’s alpha was used, which indicated that all measured items were reliable for further analysis.FindingsThe study confirmed that professionals within the GCI are aware of the existence of UBs and revealed that the most prevalent ethical conducts exhibited, namely, level of accuracy, accountability, honesty, reliability, fairness and respect for colleagues. Common unethical conducts exhibited included: favouritism, bribery and corruption, professional negligence, falsification, fraud and overbilling.Research limitations/implicationsThe study reported on the dominant ethical conduct among built environment professionals. The claims put forward in the analysis are, thus, affected by Ghana’s social, economic and political environments, which could restrict the generalization of the findings.Practical implicationsIncipient findings presented from this research will guide stakeholders to develop and device strategies that will aid alleviate persistent ethical issues within the built environment.Social implicationsThe study highlights individuals’ perspectives on ethical issues persistent in the built environment. The findings suggest individuals adhere to ethical practices in a project environment by the evidence presented.Originality/valueThis pioneering study is a novel assessment on EBs and UBs of built environment professionals in the GCI. The study supplementary adds value to the literature on ethical and unethical practices. By identifying these practices, construction firms have a competitive edge in combating UB and promoting EB among built environment professionals in the GCI.

Effects of asymmetric role assignation and fitness heterogeneity based on the background payoff on fairness

Most previous studies on ultimatum game assume that players assign roles symmetrically, and the fitness of players comes entirely from the payoffs of the game. However, asymmetry is common in real life, due to the fact that everyone's social status and family resources are different, that is, each player has a different background payoff. To investigate the impacts of role assignation and fitness on fairness, we study the asymmetric role assignation and fitness heterogeneity based on background payoff. The results show that asymmetric role assignation and fitness heterogeneity are of great importance on fairness. Especially, when players with small background payoff have more opportunities to act as proposer, fairness level can be significantly promoted, and the greater the weight of background payoff in fitness, the fairer the strategy. The results provide us with better insights into understanding fairness behaviors in the population.

Trend Improving Soccer Professional Referees Performance in West Java Using Psychological Skill Training and Physical Fitness

The referee has an important role in football matches. The referee determines the level of fairness in the match so they must be mentally and physically fit. The purpose of this study was to examine the application of psychological skills training and the level of physical fitness to the performance of the referee. Psychological skill exercises used are relaxation, imagery, and relaxation-imagery combination that exist in conditions of high and low physical fitness. This research is quantitative with a quasi-experimental method. The research sample consisted of 48 people who were taken using purposive sampling based on the criteria that the referee had a provincial level referee license (Code C2) and has led a provincial level competition. Data collection techniques are through pre-test, post-test and treatment during the study. The data analysis technique used two-way ANOVA analysis followed by the Tukey test. The findings showed that psychological skill training, both relaxation, imagery, or a combination of imagery-relaxation and physical fitness, had a positive effect on the performance of the referee in leading the match. These results have implications for the concept of psychological training for football referees in West Java, especially during the Covid-19 pandemic, who had limited access to training in the field. However, this psychological skill training makes it easier to train the referee mentally.

Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China

Abstract Urban vitality is a spatial phenomenon and a public service. Previous studies often did not measure the fairness of the accessibility of urban vitality. This research analyzed the spatial distribution of urban vitality in Wuhan as a case study area using big data from multiple sources. The study used the two-step floating catchment area method (2SFCA) to measure the accessibility level of each residential district to the vibrant zones (VZs). Furthermore, the inequality in the level of accessibility of residential areas with different housing prices was assessed on the basis of the Gini coefficient. The main conclusions are as follows: (1) the Gini coefficient of reaching the accessibility level of high-grade VZs is 0.426, and the corresponding sub-high-grade Gini coefficient is 0.274. This shows that the inequality in accessibility of different residential areas is more obvious as the level of vitality is higher and (2) residential areas with high housing prices have greater accessibility than those areas with low housing prices. It was also noticed that the level of accessibility and fairness in vitality is generally higher in central urban areas than in suburban areas. The Gini coefficient of high-grade vitality is generally higher than that of sub-high-grade. The results of this study provide a reference for researching the accessibility level of urban vitality, which considers the needs of population, and can also provide guidelines in urban planning regarding the allocation of services and resources.

Court fairness and firm level export decisions

This paper is the first micro-level study in the international trade literature to examine the causal relationship between court fairness and the firm-level export decisions (propensity and intensity). The paper uses firm-level data from South Asian countries collected and maintained by the World Bank for empirical analysis. Methodologically, the study uses IVprobit and 2SLS models to analyze the pooled cross-sectional data. I estimate various specifications to show that court fairness increases the export share as well as the likelihood that a firm becomes an exporter. Results are robust for both intensive and extensive margins of trade.

Moderator Chatbot for Deliberative Discussion

Online chat functions as a discussion channel for diverse social issues. However, deliberative discussion and consensus-reaching can be difficult in online chats in part because of the lack of structure. To explore the feasibility of a conversational agent that enables deliberative discussion, we designed and developed DebateBot, a chatbot that structures discussion and encourages reticent participants to contribute. We conducted a 2 (discussion structure: unstructured vs. structured) × 2 (discussant facilitation: unfacilitated vs. facilitated) between-subjects experiment (N = 64, 12 groups). Our findings are as follows: (1) Structured discussion positively affects discussion quality by generating diverse opinions within a group and resulting in a high level of perceived deliberative quality. (2) Facilitation drives a high level of opinion alignment between group consensus and independent individual opinions, resulting in authentic consensus reaching. Facilitation also drives more even contribution and a higher level of task cohesion and communication fairness. Our results suggest that a chatbot agent could partially substitute for a human moderator in deliberative discussions.

Conceptualizing blockchain‐based value co‐creation: A service science perspective

AbstractCo‐creation and open innovation have become an important source of competitive advantage. Over the past decades, huge amounts of information have become accessible to the external stakeholders of the organizations, changing the traditional value creation processes. However, co‐creation comes with its challenges, including the complex engagement of stakeholders, difficulties in opening up value chains and issues of trust and intellectual property. The arrival of blockchain and cryptographic asset‐driven decentralized technologies has brought to the world an entirely new spectrum of possibilities regarding social, financial and technological innovations. Now it is possible to achieve superior levels of transparency, security and fairness to the public, especially when compared with the rather limited business models that dominate Web 2.0 commercial products. While the first applications of blockchain technology were for digital currency transactions, that same technology is now being applied to the development of the blockchain‐based decentralized platforms. This calls for research to integrate other relevant fields such as organizational management, public policy, community engagement and marketing. This qualitative preliminary study aims to build an integrative and transdisciplinary framework of blockchain service ecosystems based on theoretical premises of Service Science through which blockchain‐based value co‐creation can be analysed. The systemic approach presented in this paper provides much‐needed guidance for developers of decentralized organizations on how to create sustainable platforms and to maintain them profitable in the long run. The focus of the proposed conceptual model is to initiate a scholarly curiosity in understanding possible solutions and potential impact of blockchain technology in the way we collaborate as a society.

Initial Exchange Offerings: The Next Evolution in Cryptocurrencies

As the initial coin offering (ICO) market waned in 2018, a new format for crowdfunding arose: initial exchange offerings (IEOs). Although the downturn of the ICO market undoubtedly contributed to the development of IEOs, this is also part of the natural evolution of raising capital through cryptocurrencies. Scams, regulatory issues, manipulation, and random speculation have all been associated with ICOs. Conversely, IEOs put the good name and reputation of a cryptocurrency exchange behind the token offering, providing a level of fairness, integrity, and safety not always offered through the ICO market. This article demonstrates that the additional level of due diligence conducted in an IEO increases token fundraising success. TOPICS:Currency, exchanges/markets/clearinghouses, risk management Key Findings ▪ Initial exchange offerings (IEOs) allow companies to raise capital by selling utility tokens on an exchange. Because the cryptocurrency exchange performs due diligence on the offering to protect its customers and its reputation, IEOs are less risky investments than initial coin offerings (ICOs). ▪ Utility tokens are one type of cryptocurrency that usually provides the holder preferential access to the sponsoring firm’s services and does not represent ownership in the sponsoring firm raising funds and are not considered securities. Nevertheless, utility tokens sold on a cryptocurrency exchange (IEOs) provide several advantages to both firms seeking to raise funds and their investors relative to those that are not (ICOs). ▪ Empirical results demonstrate that firms offering utility tokens are more successful in raising money if they do so through IEOs, rather than using an ICO approach. Results indicate that whether or not firms sell tokens on a cryptocurrency exchange is more important than traditional ICO success factors.

The Challenges of Machine Learning and Their Economic Implications.

The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These issues can have substantial economic implications because they may hinder the development and mass adoption of machine learning. In light of this, the purpose of this paper was to determine, from a positive economics point of view, whether the free use of machine learning models maximizes aggregate social welfare or, alternatively, regulations are required. In cases in which restrictions should be enacted, policies are proposed. The adaptation of current tort and anti-discrimination laws is found to guarantee an optimal level of interpretability and fairness. Additionally, existing market solutions appear to incentivize machine learning operators to equip models with a degree of security and privacy that maximizes aggregate social welfare. These findings are expected to be valuable to inform the design of efficient public policies.