Concept Of Fairness Research Articles

Mathematical and Statistical Tools for Ensuring Fairness in Computational Models

The rising use of computational models in decision-making within healthcare, hiring, and finance underlines that fairness is of pressing concern as a means for ensuring equity, building trust, and promoting accountability. This research systematically investigates mathematical and statistical methods to identify, measure, and reduce biases of the systems. It reviews basic concepts of fairness, such as demographic parity, equality of odds, and individual fairness; methods for detecting and mitigating bias. Techniques discussed range from pre-processing adjustments to in-processing constraints to post-processing optimizations toward trading off between fairness and predictive accuracy. Application cases include the case study on ICU admissions using the MIMIC-III dataset that practically illustrates how effectiveness can be ensured with the help of fairness-aware strategies. Where there was a big gap, it now greatly closes it in demographic parity, reducing disparities in true positive rates; therefore, proving reliability for these models through sensitivity and adversarial testing. This paper bridges the divide between theory and practice by presenting a comprehensive framework towards the consideration of fairness within computational systems. The presented work identifies prospects for enhancing equity while ensuring dependability and supplements a high-growth conversation about ethical practices in AI development.

Fostering Linguistic Justice in Digital Spaces: Using Indigenous Methods in the Study of NLP

Data and data modeling practices in natural language processing (NLP) privilege languages with the greatest levels of representation online, leaving behind speakers of the overwhelming majority of the world's languages from enjoying the full benefits of digital inclusion. These dynamics have been explored in the context of historic colonial relationships, with linguists and language activists noting the colonizing discourses around the adoption of language-related technologies, and highlighting problematic research practices among linguists and computational linguists working in the area of digital corpus development for minoritized and Indigenous languages. Such power differentials raise questions about the source(s) and nature of language technology development bias in Global South contexts. To that end, this paper is divided into three parts. The first addresses the identification and roots of bias in NLP as it is currently understood, emphasizing the particular ways it is expressed in Global South contexts, and Latin America in particular. The second briefly discusses the theoretical frameworks of data colonialism and Indigenous data sovereignty as mechanisms to clarify how bias in NLP operates in these contexts. In the third section, Indigenous methods are proposed to investigate how these frameworks can help illuminate a research ethics around NLP for minoritized languages. Although there is an abundance of literature addressing ethics in NLP, no work to date has utilized an Indigenous methods framework to examine bias in the field, even as it relates specifically to Indigenous language data and technologies. This work argues that Indigenous methods can ultimately yield deeper insights into the concepts of bias and fairness in NLP, as well as the motivations and decisions employed in the development of language technologies, thereby serving as a more effective generator of equitable solutions while advancing theory development.

Believed Gender Differences in Social Preferences

Abstract While there is a vast (and mixed) literature on gender differences in social preferences, little is known about believed gender differences in social preferences. Using data from 15 studies and 8,979 individuals, we find that women are believed to be more generous and more equality-oriented than men. This believed gender gap is robust across a wide range of contexts that vary in terms of strategic considerations, selfish motives, fairness concepts, and payoffs. Yet, this believed gender gap is largely inaccurate. Consistent with models of associative memory, and specifically the role of similarity and interference, the believed gender gap is correlated with recalled prior life experiences from similar contexts and significantly affected by an experience that may interfere with the recall process of prior memories even though this interfering experience should not affect the beliefs of perfect-memory Bayesians. Application studies further reveal that believed gender differences extend to the household (i.e., beliefs about contributions to the home, family, and upbringing of children), the workplace (i.e., beliefs about equal pay) and policy views (i.e., beliefs about redistribution, equal access to education, healthcare, and affordable housing).

The principles of civil proceedings in Ancient Rome: a historical and legal review

In the article, the authors analyze the genesis of the principles of national civil procedure based on the analysis of Roman law. It is noted that the principles of civil procedure of Ancient Rome laid the foundation for most modern legal systems around the world. The Roman legal tradition, with its complex structures and convincing philosophy, is an important source of knowledge not only for scholars and higher education students, but also for practicing lawyers. The principles of civil procedure in ancient Rome stem from a comprehensive understanding of how the legal system functioned. At the heart of these principles is a complex system designed to uphold justice, promote fairness, and ensure access to the courts for all citizens. In ancient Rome, judicial proceedings were governed by a set of principles that were known primarily from customary practice and later codified in sources of law such as the Corpus Juris Civilis. These principles were intended to balance the scales of justice among the competing interests of various stakeholders, including individual litigants, the state, and society as a whole. Each principle functioned not only as a rule, but also as a reflection of broader societal values encompassing the concepts of fairness, accountability and integrity of the judicial process. In this article, the authors analyze the rule of law, the principle of dispositivity, the principle of competition, the principle of publicity, the principle of transparency, and the principle of justice. Based on a critical analysis of the views of scholars presented in legal science, the authors conclude that it was the Roman civil procedure that became the foundation for the formation of the principles of civil procedure of many modern legal systems, which provide invaluable insights into the nature of justice, formalism and the role of law interpretation. Roman law remains an important topic in legal research, providing fundamental knowledge about the evolution of legal institutions and procedures. It allows modern lawyers to understand the reasons for many modern legal innovations. As legal systems continue to evolve, the principles of civil justice originating in ancient Rome will undoubtedly influence current discussions about justice, equality, and the rule of law.

Consumer willingness to pay for fair and sustainable foods: who profits in the agri-food chain?

Going through an era where sustainability and definitions of fairness have been extended and integrated into the agri-food chain, there is a need to understand, on a multi-dimensional and multinational level, the structure of agri-food value chain revenues and consumers’ intentions regarding necessity foods. The study analyzed 1,020 questionnaires from Algeria, Germany, Greece, Italy, and Tunisia revealing that taste prioritizes brand and packaging. Social networks, including family and friends, significantly influence the purchase of fair products. Furthermore, a choice experiment revealed the consumer preferences around attributes of the olive oil case as local, traditional, or organic, from a family or farmer association, in a glass bottle, purchased in small local shops/markets, typical and/or extensive nutritional labeling and health claims, non-relevant branding, and finally a fair price reflecting the reasonable quality of the olive oil product. Regarding the agri-food value chain, the results highlight the revenue distribution among stakeholders as unequal and unfair from consumer perceptions, with an imperative need for transparency. The study investigates in-depth the multifaceted dimensions of the fairness concept in the food market from a consumer’s perception, showing their willingness to pay for necessities based on fair pricing and sustainable practices.

Fair Collaborative Learning (FairCL): A Method to Improve Fairness amid Personalization

Model personalization has attracted widespread attention in recent years. In an ideal situation, if individuals’ data are sufficient, model personalization can be realized by building models separately for different individuals using their own data. But, in reality, individuals often have data sets of varying sizes and qualities. To overcome this disparity, collaborative learning has emerged as a generic strategy for model personalization, but there is no mechanism to ensure fairness in this framework. In this paper, we develop fair collaborative learning (FairCL) that could potentially integrate a variety of fairness concepts. We further focus on two specific fairness metrics, the bounded individual loss and individual fairness, and develop a self-adaptive algorithm for FairCL and conduct both simulated and real-world case studies. Our study reveals that model fairness and accuracy could be improved simultaneously in the context of model personalization. Funding: This work was supported by the Breakthrough T1D Award [Grant 2-SRA-2022-1259-S-B]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2024.0029 .

Perilaku Penyiaran Jurnalistik Serta Standar Program Siaran Dalam Bentuk Hidden Camera dan Doorstoping di Era 4.0

Every action taken by the government must have a strong legal basis so as not to conflict with the legislation. This research was conducted with the aim of knowing how the form of legal arrangements in the implementation of television broadcast content according to laws and regulations and how the responsibility of broadcast organizers in organizing television broadcast content in accordance with laws and regulations. The research method used is library research, which is a series of activities related to library data collection methods. The findings in this article are related to the concepts of fairness, accuracy and impartiality, which are very important in upholding the law and truth. Reporting events of panic, riots and conflicts is crucial to ensuring public safety and addressing their impact, even though there is no specific law governing such matters. Finally, the use of hidden cameras has legal and ethical implications that must be considered in accordance with the Electronic Information and Transaction Law and its amendments to prevent legal violations and protect public privacy.

Exploring the Question of Bias in AI through a Gender Performative Approach

The objective of this paper is to examine how artificial intelligence systems (AI) can reproduce phenomena of social discrimination and to develop an ethical strategy for preventing such occurrences. A substantial body of scholarship has demonstrated how AI has the potential to erode the rights of women and LGBT+ individuals, as it is capable of amplifying forms of discrimination that are already pervasive in society. This paper examines the principal approaches that have been put forth to contrast the emergence of biases in AI systems, namely causal, counterfactual reasoning, and constructivist methodology. This analysis demonstrates the necessity of considering the sociopolitical context in which AI systems are developed when evaluating their ethical implications. To investigate this conjunction, we apply the theory of gender performativity as theorized by Judith Butler and Karen Barad. This illustrates how AI functions within the social fabric, manifesting patriarchal configurations of gender through an analysis of the notorious case of the COMPAS system for predictive justice. In conclusion, we demonstrate how reframing of gender performativity theory, when applied to AI ethics, permits us to consider the social context within which these technologies will operate. This approach enables an expansion of the interpretation of the concept of fairness, thereby reflecting the complex dynamics of gender production. In the context of AI ethics, the concept of "fairness" pertains to the capacity of an algorithm to generate results dealing with sensitive categories, such as gender, ethnicity, religion, sexual orientation, and disability, in a manner that does not engender forms of discrimination and prejudice. The gender dimension needs to be reconsidered not as an individual feature but as a performative process. Moreover, it enables the identification of pivotal issues that must be addressed during the development, testing, and evaluation phases of AI systems.

From Tradition to Reform: A Comparative Study of Personal Laws in India

The legal system of India is characterized by its diverse structure, where individual laws concerning marriage, divorce, inheritance, and adoption are influenced by a person's religious and communal affiliations. The rules, originating from religious traditions, have continuously adapted to mirror shifting social contexts and emerging concepts of fairness and impartiality. This paper provides a comparative analysis of Hindu, Muslim, Christian, and Parsi personal laws, tracing their historical foundations, core tenets, and the complex challenges they face within India’s contemporary legal landscape. The genesis of these personal laws can be traced to religious scriptures and practices, with formal codification occurring under British colonial rule. Reforms aimed at advancing gender equality have transformed Hindu law, particularly through the 2005 inheritance amendments, yet issues persist, notably within Muslim law, where doctrines related to divorce and inheritance often favor male beneficiaries. Christian and Parsi laws are more egalitarian by comparison, though they still bear vestiges of colonial legal structures The discussion surrounding the implementation of a Uniform Civil Code (UCC), as outlined in Article 44 of the Indian Constitution, is gaining momentum, portraying the UCC as a tool to harmonize personal laws in pursuit of gender equality and social justice. However, the UCC remains challenging, with many communities concerned that it may destroy cultural autonomy. This paper critically explores the evolution and current state of India's personal laws, evaluating the possibilities and implications of a UCC. It believes that, while a path to legal uniformity is conceivable, it necessitates a nuanced approach that protects religious plurality while advancing individual rights and gender parity.

Optimizing energy consumption for blockchain adoption through renewable energy sources

The adoption of blockchain technology across various industries and systems has garnered significant attention due to its myriad benefits, leading to widespread popularity today. However, the energy-intensive nature of blockchain, attributed to extensive computations and data mining, poses substantial operational and environmental challenges, hindering its widespread acceptance. To mitigate these limitations, leveraging renewable energy sources emerges as a viable and crucial solution. These options are assessed across various dimensions including sustainable energy transfer, physical attributes, legal regulations, energy supply costs, technological infrastructure, and climatic constraints. To achieve this, we present four optimization models. Initially, three optimization models, rooted in risk aversion, fairness, and weighted sum principles, are meticulously solved. Subsequently, leveraging the insights garnered from these models, a multi-objective optimization model is developed based on Percentage Multi-Choice Goal Programming (PMCGP) method. This framework facilitates the scoring and ranking of renewable energy sources, culminating in informed decision-making. Our investigation, anchored by a case study, underscores the significant potential of utilizing blockchain technology in conjunction with wind energy. In the initial step, our models grounded in risk, optimization, and fairness concepts establish targets for the subsequent stage. Consequently, the proposed methodology offers diverse analytical capabilities tailored for supply chain managers and decision-makers.

Introducing fairness in network visualization

Motivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence, stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for orthogonal and for straight-line drawings of graphs, two foundational paradigms in the field. We investigate the following research questions: (i) What is the price, in terms of global readability, of incorporating fairness constraints in graph drawings? (ii) How unfair is a graph drawing that does not optimize fairness as a primary objective? We present both theoretical and empirical results. In particular, we design and implement two optimization algorithms for multi-objective functions, one based on an ILP model for orthogonal drawings, and one based on gradient descent for straight-line drawings. In a nutshell, we experimentally show that it is possible to significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced global readability. Also, we present a use case in which we qualitatively evaluate our approach on a practical scenario.

The Decentralisation of the DMA’s Enforcement System

Abstract The harmonisation of the internal market is a fundamental aspect of the EU acquis, and the Digital Markets Act (DMA) is no exception. It aims to harmonise digital markets across the Union on the basis of the concepts of contestability and fairness. The DMA is enforced through a quasi-centralised enforcement system, with the European Commission as its sole enforcer and national competition authorities (NCAs) playing a secondary and supporting role in monitoring. The EC’s centralisation of enforcement powers is key to ensuring harmonisation. This paper highlights the potential risks at the national level due to NCA involvement in the enforcement and monitoring of the DMA. While the Regulation does not need to be transposed into national law to be effective, some Member States have introduced legislation specifying which authorities are responsible for enforcement and how they should apply their powers in line with the DMA. The paper examines these legislative developments, revealing potential deviations that undermine the effective enforcement of the DMA and disrupting its current centralised enforcement system.

Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set’s membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of considering fairness in vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game framework is developed under the waiting response time cost (WRTC) sharing mechanism. Simulation results show that with fairness concerns in vehicle-cargo matching, all stakeholders are better off: The platform achieves positive revenue growth, and shippers and carriers receive positive subsidy. This study offers both theoretical insights and practical guidance for the long-term and stable operation of the on-line freight stowage industry.

A General Concave Fairness Framework for Influence Maximization Based on Poverty Reward

Influence maximization (IM) aims to find a group of influential nodes as initial spreaders to maximize the influence spread over a network. Yet, traditional IM algorithms have not been designed with fairness in mind, resulting in discrimination against some groups, like LGBTQ communities and racial minorities, etc. This issue has spurred research on Fair Influence Maximization (FIM). However, existing FIM studies come with some drawbacks. Firstly, most proposed notions of fairness for FIM cannot adjust the trade-off between fairness level and influence spread. Secondly, though a few specific notions of fairness allow such balancing, they are limited to a few specific concave functions, which may not be suitable for various real-world scenarios. Furthermore, none of them have studied the deep relations between the features of concave functions and the level of fairness. Thirdly, existing fairness metrics are limited to their corresponding concepts of fairness. Comparing the level of fairness across different algorithms using existing metrics can be challenging. To tackle the above problems, this paper first proposes a novel fairness notion named Poverty Reward (PR), which achieves fairness by rewarding the enrichment of groups with low utility. Based on PR, we further propose an algorithmic framework called Concave Fairness Framework (CFF) that allows any concave function that satisfies specific requirements. We also systematically clarify how fairness is improved by applying concave functions and provide an in-depth quantitative analysis of how to select appropriate concave functions for different utility distributions. Moreover, we propose the Reward of Fairness (RoF) metric that evaluates the disparity between groups. Based on RoF, an evaluation system is built to uniformly compare FIM algorithms from different fairness notions. Experiments in real-world datasets have demonstrated the validity of the CFF, as well as the proposed fairness notion.

Comparing ‘fair’ machine learning models for detecting at-risk online gamblers

ABSTRACT Researchers have worked to develop machine learning models that detect at-risk online gamblers, enabling personalized harm prevention tools. However, existing research has not evaluated these models’ potential to reinforce or amplify sociodemographic biases leading to treatment disparity, a recognized issue in the machine learning field. We sought to develop and compare three examples of potentially fair models using online gambling data. In two large samples of transaction data from a provincially owned Canadian gambling website (N1 = 9,145, N2 = 10,716), we developed three machine learning models based on competing concepts of fairness: fairness via unawareness, classification parity, and outcome calibration. We hypothesized that significant relationships existed between reporting a high risk of past-year gambling problems (the dependent variable) and participants’ age and sex. Further, we hypothesized that the three ‘fair’ models would show differing levels of classification performance both in aggregate and within sociodemographic groups. Significant age and sex effects were found, refuting the fairness via unawareness modeling strategy. Superiority across all performance metrics was not present for either of the remaining models. For the fairest practices in any jurisdiction, classification parity and outcome calibration models should be tested in situ, and incorporate the perspectives and preferences of end users who will be affected.

Accurate preference-based method to obtain the deterministically optimal and satisfactory fairness-efficiency trade-off

The resource allocation problem is a classic multi-objective challenge, particularly when balancing the fairness-efficiency trade-off. To achieve a deterministically optimal and satisfactory solution, researchers frequently employ preference-based methods, including selecting among Pareto solutions based on the decision-maker's a posteriori preference and using deterministic models incorporating a priori preferences. In this study, we address two main challenges—specifically, (1) the limitations in measuring the abstract concepts of fairness and efficiency and (2) finding a deterministically optimal and satisfactory balance between fairness and efficiency. We apply a Gini impurity index derived from the classification and regression tree to calculate fairness, ensuring the Gini index function's differentiability. Additionally, we unify the scales of fairness and efficiency to facilitate calculation. Using accurate preference information, we employ the extended interval goal programming method to solve the model and achieve a deterministically optimal and satisfactory solution. The comparative analysis results demonstrate that our model (1) efficiently addresses the real-world water resource allocation problem concerning the fairness-efficiency trade-off; and (2) generates fewer penalties, with an average improvement ratio of 8% in the case study, using more refined penalty functions that align closer to the decision-maker's real and nonlinear preferences.

Fairness for machine learning software in education: A systematic mapping study

The integration of machine learning (ML) systems into various sectors, notably education, has great potential to transform business workflows and decision-making processes. However, this technological advancement brings forth critical ethical concerns, particularly concerning the fairness of decisions affecting diverse groups of people. Our objective was to systematically map out the landscape of ML fairness research in higher education by exploring seven key research questions. These questions span a range of topics from the types of ML algorithms used in education to the methods of fairness assessment and the results achieved in terms of equity. We included 63 primary studies published between 2002 and 2023. The most common setting for AI Fairness research are: traditional machine learning algorithms (Logistic Regression, Random Forest, Decision Tree), sensitive variables (gender, race, ethnicity), and various definitions of fairness (Group fairness, Demographic parity, Equalized odds). We also identify several future research directions, including fairness assurance for multiple sensitive variables, combining different fairness concepts and metrics, open-source benchmarking tools, and fairness testing for modern ML/AI models.

Navigating Labor-Based Grading Contracts

Abstract This article explores the impact of labor-based grading contracts on student attitudes and perceptions within multilingual First-Year Composition (FYC) sections at an R1 university. Data collected qualitatively and quantitatively examined correlations between labor-based grading contracts and shifts in student attitudes toward writing and overall learning experiences. Findings revealed that some students found labor-based grading contracts motivating, leading to improved attitudes toward writing, while others found themselves demotivated or stressed by the absence of traditional grades. The concept of fairness emerged as a key concern, challenging the assumption that labor-based grading contracts universally benefit students. This article underscores the need for nuanced implementation of labor-based grading contracts and encourages a student-centered approach to foster equitable and antiracist writing assessment practices. It acknowledges the potential benefits of labor-based contract grading, but also its associated challenges, and calls for a critical examination of grading contracts within local contexts to ensure they genuinely advance opportunities for underrepresented students.

End of the Pandemic - Beginning of Digitalization in the Public Health Service? The German National Funding Program

The data-related desolate state of the health authorities became evident at the latest with the onset of the Covid-19 pandemic. The lack of interoperability and networking among health authorities quickly developed into blockages in contact tracing and pandemic control. As a basis for the study, key figures and funding volumes from the Pact-Public Health Service (Pakt-ÖGD) were evaluated and analyzed using descriptive parameters. In addition, a literature review on the Pakt-ÖGD and related terms was carried out. The Pakt-ÖGD represents the largest financing offensive for the Public Health Service to date. Despite the digitalization pact and the first distributions of funds, there is no consensus on the goals of digitalization such as the ISCODE, Findability, Accessibility, Interoperability und Reuse (FAIR concept). The RKI is currently presenting a new electronic reporting and information system for health authorities (EMIGA), which is to be introduced parallel to DEMIS as a nationwide core application for infection protection. In analogy to health literacy, the public health service needs increased digital literacy to strengthen competence in order to agree on common goals of ISCODE and the FAIR concept, i.e. what digitalization should create or not. A sustainable public health service requires an open and connectable network that goes beyond infection protection (pandemic prevention, preparedness and response - PPPR) to a global One-Health platform.

Justice & fairness: the relationship and the corruption factor

The article analyzes the concepts of justice and fairness. The highest level of corruption in Ukraine, according to ordinary citizens, scientists, and experts, is precisely in the sphere of justice. The criminogenic basis of widespread corruption in Ukraine is malfeasance in the judicial system of Ukraine. Such actions of judges provide an opportunity for many corrupt persons to avoid punishment for corrupt actions. Since justice naturally includes «justice» as an imperative component, the expediency of using the concept of «fair justice» in scientific investigations should be questioned. At the same time, the formula «justice = justice» does not work in Ukraine. And corruption is the main negative factor of this situation. Therefore, in the Ukrainian information space, we quite often come across the wording «fair justice», which emphasizes the problems of modern justice in Ukraine (for example, the USAID project «Fair Justice»). Recognizing the protection of the rights and freedoms of every citizen of Ukraine by a fair court as an absolute, it should be noted that the concepts «right to a fair trial» and «principle of natural justice» are consistent. Justice is a universal natural idea embodied in the legal system of any social legal state. Justice, by its very nature, means that the violated right, justice, is restored. Therefore, justice during the war is first and foremost a fair punishment for both the Russian occupiers and the traitors of Ukraine, the inevitability of responsibility for those who betrayed their Motherland. At the same time, unfair justice against corrupt officials in state authorities, its selectivity, in the conditions of war and post-war reconstruction of the country, can cause devastating consequences for the state. There must be a systematic approach, the political will of the state leadership, the inevitability of responsibility for everyone without excluding persons involved in corruption. Key words: justice, fairness, right to a fair trial, corruption, war crime, collective victim, court decision, purpose, method, principle, rule of law, legality, access to justice, national security, equality before the law.