In the past decade, policymakers and commentators across the world have called for the introduction of copyright reform based on the fair use model in the United States. Thus far, Israel, Liberia, Malaysia, the Philippines, Singapore, South Korea, Sr...
Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to...
The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensurin...
The success of a trade fair depends on the close collaboration between organizers and exhibitors with potential visitors. While the literature has invested a great deal of interest in the role of the exhibitor and, to a lesser extent, to the visitor,...
Wherever there is group life, there has been a social division of labor and resource allocation, since ancient times. Examples include ant colonies, bee colonies, and wolf colonies. Different roles are responsible for different tasks. The same is tru...
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlock...
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of tr...
Ensemble clustering has become a widely used technique for improving robustness and accuracy by combining multiple clustering results. However, traditional ensemble clustering methods often fail to provide fair treatment between groups defined by sen...
Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many pri...
An interesting and challenging problem in mathematics is how to construct fair dice pairs. In this paper, by means of decomposing polynomials in a residue class ring and applying the Discrete Fourier Transformation, we present all the 2000 fair dice...
This article examines the role of fairness in software across diverse application contexts, with a particular emphasis on healthcare, and introduces the concept of algorithmic (individual) meta-fairness. We argue that attaining a high degree of fairn...
Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group fo...
Almost all academic literature about the causes and consequences of fairness of elections and referenda is based on retrospective evaluations. One of the strongest findings of such studies is that nonvoting is higher among citizens who retrospectivel...
This study aims to investigate the effects of geographical indication (GI) and fair trade knowledge (FTK) on U.S. female consumers’ perceived brand equity and the purchase intention of fair trade apparel handicrafts. Employing a web-based exper...
The rise of machine learning-driven decision-making has sparked a growing emphasis on algorithmic fairness. Within the realm of clustering, the notion of balance is utilized as a criterion for attaining fairness, which characterizes a clustering mech...
Connectivity is one of the essential needs in today’s standards in many aspects of life, starting with personal relationships, education, and remote work and ending with the security and economy of countries. However, connectivity is susceptibl...
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–the...
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The...
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian proc...
The aim of this study is to examine the level of understanding of the principle of fair play by secondary school pupils (n = 827) from the Polish province of Wielkopolska, physical education (PE) students from Poznan University of Physical Education...
18 October 2023
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing b...
Optimistic Fair Exchange protocol was designed for two parties to exchange in a fair way where an arbitrator always remains offline and will be referred only if any dispute happens. There are various optimistic fair exchange protocols with different...
Atmospheric electricity measurements are very sensitive to weather conditions. Fair weather for atmospheric electricity in Kamchatka (Russia) was determined by the method of expert assessment at an observatory. After the transition to automated digit...
Despite considerable effort and analysis over the last two to three decades, no integrated scenario yet exists for data quality frameworks. Currently, the choice is between several frameworks dependent upon the type and use of data. While the framewo...
Environmental sustainability and fairness in auction systems are becoming increasingly important as systems evolve with the integration of digital technologies. This paper introduces a novel demand-supply matchmaking (DSM) framework designed to impro...
13 June 2023
Aircraft noise causes a variety of negative health consequences, and annoyance is a central factor mediating stress-related health risks. Non-acoustic factors play an important role in the experience of annoyance where the aspect of fairness is assum...
Background and Objectives: Healthcare systems are supported by the European ideology to develop their egalitarian concerns and to encourage the correct and fair behavior of medical staff. By integrating fair healthcare practices into sustainability,...
The ever-increasing amount of data generated from experiments and simulations in engineering sciences is relying more and more on data science applications to generate new knowledge. Comprehensive metadata descriptions and a suitable research data in...
In recent years, graph neural networks (GNNs) have shown powerful performance in processing non-Euclidean data. However, similar to other machine-learning algorithms, GNNs can amplify data bias in high-risk decision-making systems, which can easily l...
In this paper, we address the problem of fair sharing of the total value of a crowd-sourced network system between major participants (founders) and minor participants (crowd) using cooperative game theory. We use the framework of a Shapley allocatio...
Historically, assessments of human intelligence have been virtually synonymous with practices that contributed to forms of inequality and injustice. As such, modern considerations for assessing human intelligence must focus on equity and fairness. Fi...
Multipath transport protocols are aimed at increasing the throughput of data flows as well as maintaining fairness between users, which are both crucial factors to maximize user satisfaction. In this paper, a mixed (non)linear programming (MINLP) sol...
Customer churn prediction has become crucial for businesses, yet it poses significant challenges regarding privacy preservation and prediction accuracy. In this paper, we address two fundamental questions: (1) How can customer churn be effectively pr...
Corona Virus Disease 2019 (COVID-19) is now treating the health of millions of people worldwide. The Chinese government now applies nucleic acid testing as a tool to detect patients from healthy people to control the spread of COVID-19. However, peop...
In an incomplete information setting, we analyze the sealed bid auction proposed by Knaster (cf. Steinhaus (1948)). This procedure was designed to efficiently and fairly allocate multiple indivisible items when participants report their valuations tr...
The standard protocol of near field communication (NFC) has concentrated primarily on the speed of communication while ignoring security properties. Message between an NFC-enabled smartphone and a point of sale are exchanged over the air (OTA), which...
Deceivingly simple and often colloquial, the Fair Go is pervasive to the Australian lexicon and a feature of the projected national identity. The Fair Go has historically been used as a plea for fairness in contexts where there is perceived injustice...
Message exchange for acknowledgment of receipt (e.g., for services that provide recipients with certain documents, such as sanctions, summonses, or requirements, which are often sent by public administrations) has traditionally been provided through...
Cooperative communication techniques have recently enabled wireless technologies to overcome their challenges. The main objective of these techniques is to improve resource allocation. In this paper, we propose a new protocol in medium access control...
Drawing on organizational justice theory, this study examines how workers’ perceptions of fair treatment influence job satisfaction following the transition from temporary agency employment at subcontracting firms to regular employment with cli...
Hospitals must allocate beds and staff effectively under volatile arrival patterns, where scheduling errors can cause preventable harm. This study introduces a fairness-aware, forecast-informed reinforcement learning framework for hospital scheduling...
Fairness-aware classification with respect to sensitive attributes, such as gender and race, is one of the most important topics in machine learning. Although numerous studies have made outstanding progress through various approaches, one key limitat...
Sharing bottleneck bandwidth among TCP flows with diverse round-trip times (RTTs) remains a persistent challenge. This study investigates RTT unfairness and evaluates the behavior of two widely deployed congestion control algorithms, TCP Cubic and TC...
A virtual energy storage system (VESS) logically shares a physical energy storage system among multiple units. In resource sharing, the distribution of benefits is a critical problem. As a resolution, this study proposes a fair VESS operation method...
The high pay packages of U.S. CEOs have raised serious concerns about what would constitute a fair pay. Since the present economic models do not adequately address this fundamental question, we propose a new theory based on statistical mechanics and...
14 December 2018
We argue that a Nash bargaining model with behavioral factors (i.e., fairness concern and risk aversion) should be introduced to the price strategizing process in the context of a closed-loop supply chain. We consider three different pricing models:...
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Her...
Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost...
Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Object...
With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensor...
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