Aspects Of Fairness Research Articles

Analisis Penerapan Prinsip Syariah pada Produk Hasanah Card di Bank Syariah Indonesia KCP Cemara Asri

This study aims to investigate the application of sharia principles in Hasanah Card products at Bank Syariah Indonesia KCP Cemara Asri. The background of the study includes the increasing market demand for banking products that comply with sharia principles. The research questions focus on the extent to which Bank Syariah Indonesia implements sharia principles in Hasanah Card products and how the market responds to its implementation. The research method uses a qualitative approach with interviews and document analysis. The provisional hypothesis shows that the implementation of sharia principles in Hasanah Card includes aspects of transparency, fairness, and sharia compliance. However, the adoption of this product still faces some challenges in terms of public understanding and acceptance of sharia features. The author concludes that while Bank Syariah Indonesia has taken sharia principles into account in the development of the Hasanah Card product, further efforts are needed to improve public understanding and acceptance and strengthen overall sharia compliance.

Towards practical artificial intelligence in Earth sciences

AbstractAlthough Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.

Middle School Students' Conceptualizations and Reasoning about the Fairness of Math Tests

Assessment is central to teaching and learning, and recently there has been a substantive shift from paper-and-pencil assessments towards technology delivered assessments such as computer-adaptive tests. Fairness is an important aspect of the assessment process, including design, administration, test-score interpretation, and data utility. The Universal Design for Learning (UDL) guidelines can inform assessment development to promote fairness; however, it is not explicitly clear how UDL and fairness may be linked through students’ conceptualizations of assessment fairness. This phenomenological study explores how middle grades students conceptualize and reason about the fairness of mathematics tests, including paper-and-pencil and technology-delivered assessments. Findings indicate that (a) students conceptualize fairness through unique notions related to educational opportunities and (b) students’ reason about fairness non-linearly. Implications of this study have potential to inform test developers and users about aspects of test fairness, as well as educators data usage from fixed-form, paper-and-pencil tests, and computer-adaptive, technology-delivered tests.

THE ROLE OF DISTRIBUTIVE JUSTICE COMPENSATION, COMMITMENT AND WORK DISCIPLINE TOWARDS WORK PERFORMANCE

This research aims to analyze the influence of distributive justice compensation, commitment and work discipline towards employee work performance. The research method used is a quantitative method with a survey approach. Data was obtained through a questionnaire distributed to employees in CV. Nursa Prima Mojokerto. Data analysis was carried out using multiple linear regression to see the influence of the independent variables (distributive justice compensation, commitment and work discipline) on the dependent variable (work performance). The research results show that these three independent variables significantly increase work performance, both partially and simultaneously. These findings indicate that the company needs to pay attention to aspects of fairness in providing compensation, improve work discipline, and build employee commitment to improve work performance. It is hoped that this research can contribute to the development of effective human resource management strategies in the company.

Revitalisasi Kemampuan Adaptasi Birokrasi Garis Depan Pada Kebiasaan Baru Penyelenggaraan Pelayanan Publik Di Kota Makassar

Street level bureaucracy is one of the main elements in providing public services in dealing with the impact of the Covid-19 pandemic. This research specifically aims to identify the revitalization of the role of Ballaparang Sub District and Gunung Sari Sub District officials, Rappocini District, Makassar Municipality in providing government administration services and other services to the community during the implementation period of the new normal policy. The type of research used is qualitative with a descriptive approach. The results of the research show that the Ballaparang and Gunung Sari Sub District officials have demonstrated good abilities in responding to and overcoming problems in the delivery of public services in the new normal era and challenges from the aspects of efficiency, effectiveness and fairness in the delivery of public services can be overcome.

The Relationship between Political Image and Voting Behavior in First-time Voters: Demographics of Generation Z Voters in the 2024 Election in Indonesia

Purpose: This research aims to determine the relationship between voting behavior and political image as well as the demographic characteristics of the voting behavior tendencies of Generation Z in the 2024 Election in Indonesia. Methods: Data collection uses a questionnaire that has good validity and reliability, the questionnaire contains a measurement scale for voting behavior and political image. The measurement of voting behavior variables uses a scale that includes aspects of knowledge, attitudes and actions. Meanwhile, the measurement of the political image variable uses a scale that includes aspects of competence, benevolence, integrity, fairness and reliability. Respondents are given alternative answers to choose one of the four answers provided starting from the options Strongly Agree to Strongly Disagree. Scoring for each answer choice for each variable is 1-4. Apart from that, the questionnaire also contains descriptive questions regarding respondents’ voting tendencies. The number of samples that met the criteria for first-time voter respondents was 453 respondents taken using a purposive sampling technique. The data analysis technique uses the Pearson Product Moment Correlation Test (r) which aims to determine the level of closeness of the relationship between variables which is expressed by the correlation coefficient (r). Results: The research results show a significant positive correlation between political image and voting behavior. The findings also show that the categorization of respondents’ political image is medium and the categorization of voting behavior is in the high category. The majority of respondents chose candidates based on their track record, ethnicity and religion, having a nationalist and religious image, and looking cool and trendy. Conclusion: The political image and voting behavior of Generation Z can be concluded to be correlated with each other. This research also shows how the candidate’s political image is desired by first-time voters.

Between risk perceptions and adaptation policies: centering climate justice and social innovation in heat vulnerable cities

ABSTRACT This study leverages the roles of climate justice and social innovation in the perceptions of climate risk and climate adaptation policies within the local heat-vulnerable areas of seven Korean metropolitan cities. Using survey and document-based analyses, this work examines residents’ perceptions of climate risks and evaluates local climate adaptation plans in the context of climate adaptation policies. Results demonstrate that economic and social fairness aspects and collaborative learning activities played positive roles in enhancing adaptation policies. Further, higher participation as an aspect of social innovation led to heightened climate risk perceptions. Findings imply that social innovation and climate justice features can contribute to triggering risk perceptions as an initial stage for enabling subsequent steps in support of climate adaptation policies.

Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning

With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning-based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine learning setting where the clients provide only the machine learning model updates to the server rather than the actual data for decision making. However, the distributed nature of federated learning raises specific challenges related to fairness in a heterogeneous setting. This motivates the focus of our article, on the heterogeneity of client devices having different computational capabilities and their impact on fairness in federated learning. Furthermore, our aim is to achieve fairness in heterogeneity while ensuring privacy. As far as we are aware there are no existing works that address all three aspects of fairness, device heterogeneity, and privacy simultaneously in federated learning. In this article, we propose a novel federated learning algorithm with personalization in the context of heterogeneous devices while maintaining compatibility with the gradient privacy preservation techniques of secure aggregation. We analyze the proposed federated learning algorithm under different environments with different datasets and show that it achieves performance close to or greater than the state-of-the-art in heterogeneous device personalized federated learning. We also provide theoretical proofs for the fairness and convergence properties of our proposed algorithm.

FairSNA: Algorithmic Fairness in Social Network Analysis

In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, in social network analysis (SNA), designing fairness-aware methods for various research problems by considering structural bias and inequalities of large-scale social networks has not received much attention. In this work, we highlight how the structural bias of social networks impacts the fairness of different SNA methods. We further discuss fairness aspects that should be considered while proposing network structure-based solutions for different SNA problems, such as link prediction, influence maximization, centrality ranking, and community detection. This survey-cum-vision clearly highlights that very few works have considered fairness and bias while proposing solutions; even these works are mainly focused on some research topics, such as link prediction, influence maximization, and PageRank. However, fairness has not yet been addressed for other research topics, such as influence blocking and community detection. We review the state of the art for different research topics in SNA, including the considered fairness constraints, their limitations, and our vision. This survey also covers evaluation metrics, available datasets and synthetic network generating models used in such studies. Finally, we highlight various open research directions that require researchers’ attention to bridge the gap between fairness and SNA.

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process (DGP) that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

Tax Compliance in Slovenia: An Empirical Assessment of Tax Knowledge and Fairness Perception

Complex tax systems can result in tax evasion, which further impacts the revenues necessary to achieve sustainable development goals. Enhancing taxpayer education, tax knowledge, and tax fairness perception is essential for boosting revenues to support societal sustainability. The aim of this study was to assess the levels of tax knowledge and tax fairness perception within the Slovene taxpayer population, with a specific focus on the differences related to gender and settlement size. Further, the connections between tax knowledge and various aspects of tax fairness were explored. The Kruskal–Wallis test was used to assess the statistical significance of gender and settlement size differences and the Kendall’s coefficient of rank to determine the association between the tax knowledge and fairness perception dimensions. The results provide evidence that highlights disparities in tax knowledge between male and female taxpayers (p-value = 0.0116). Additionally, this study demonstrates that settlement size does not significantly impact tax knowledge perception among Slovene taxpayers (p-value = 0.2067). However, tax fairness encompasses various dimensions, and our research reveals no disparities based on gender (p-value = 0.7263) or settlement size (p-value = 0.2786). When assessing the correlation between tax knowledge and tax fairness perception, the results indicate statistically significant but weak correlations in both directions, depending on the specific fairness dimension.

A structured discussion of the fairness of GCSE and A level grades in England in summer 2020 and 2021

ABSTRACT In England, examinations for general qualifications (GCSE, AS and A level) were cancelled in summer 2020 and summer 2021 in response to the COVID-19 pandemic and replaced with contingency measures involving teacher judgements. For summer 2020, the intention was to calculate grades using rankings provided by centres, prior attainment data for the current learners and performance data for centres’ candidates in previous years. This approach was later abandoned, and 'centre assessment grades' were issued instead (except where calculated grades were higher). For summer 2021, 'teacher-assessed grades' were awarded to learners. These drastically different to usual approaches to the assessment of high-stakes qualifications raised debate about fairness. This article discusses evidence that can inform judgements about the fairness of these grades, using an existing framework of different ‘senses’ of fairness to provide a structure. In this way, we judged how fair grades were in each sense compared to prior years for each of the approaches used (or nearly used) in summer 2020 and 2021. The analysis highlights tensions in the prioritisation of different aspects of fairness in the unusual circumstances of the pandemic.

A Novel Virtual Arrival Optimization Method for Traffic Organization Scenarios

The International Maritime Organization (IMO) has been progressively implementing stricter regulations on ship carbon emissions, leading to many vessels adopting the virtual arrival (VA) method to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through virtual arrival while simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

The Implementation of Actio Pauliana Creditor Law Bankruptcy Boedel Dispute Process to Achieve Substantive Justice

This Actio pauliana is actually a lawsuit that substantively attempts to confirm that the debtor's legal actions are declared legally invalid if they harm the creditor. In bankruptcy law, actio pauliana is an important instrument so that substantive justice, especially for creditors, can be implemented optimally. This research aims to analyze the substantive justice aspects of actio pauliana in the field of bankruptcy law. This research is normative legal research by prioritizing conceptual and statutory approaches. The research results confirm that the essence of actio pauliana in bankruptcy disputes is viewed from the aspect of substantive justice, namely that it is aimed at protecting creditor rights, especially when there are debtor legal actions that can harm creditors. Apart from that, Actio Pauliana also emphasizes the restitutive aspect as per the substantive justice concept, namely being able to declare the debtor's actions to have no legal force if the action is detrimental to the debtor and reduces the number of bankruptcy debts as has been determined. In its development, the actio pauliana lawsuit in bankruptcy actually represents the fairness aspect as a manifestation of substantive justice so that in the future the actio pauliana lawsuit will not only represent factual losses, but also include potential losses that directly harm creditors and can be proven fairly and properly

Fairness Aspect in the Paroan System in the Mangar Society

This research was conducted on the Mangar society who have a system of cooperation in the form of "paroan agreements" which can be categorized as creative in drafting and implementing agreements. The social system of the Mangar society is quite interesting to explore because in the midst of a very exploitative agricultural and livestock management system, the Mangar society remains consistent in upholding justice by placing the position of farm laborers in a balanced way both in terms of roles and the distribution of benefits from managing the farm. The research method used in this research is qualitative research or field research. The results of this study indicate: (1) The paroan agreement form of the Mangar society is an oral agreement; (2) Implementation of the paroan agreement begins with an agreement between the two parties, namely the land owner and the manager. Then the implementation starts from clearing the land to harvesting; (3) The meaning of justice in the paroan agreement can be seen from: first, equal opportunity to help each other and obtain welfare between the parties. Second, there is a balance of rights and obligations. Third, there is appreciation based on the principle of mutual need. Fourth, in the distribution of profits based on mutually agreed agreements.

Implementasi Good Hospital Governance di Rumah Sakit Prima Husada Cipta Medan

<span>Good Hospital Governance is a governance system in implementing good management and <em>can assist in efforts to improve company performance and service quality, especially hospital companies that provide health services to the community. improve service and management innovation</em></span><em><span lang="EN-US">. </span><span>The purpose of this study is to determine and analyze the implementation of aspects of Good Hospital Governance such as aspects of transparency, accountability, responsibility, independence, fairness and obstacles faced, this research is a qualitative study that aims to prove the phenomena in research problems. Research informants as many as 7 people including the board of commissioners, director, head of finance, sdm, a quality committee, head of nursing and nurses</span><span lang="EN-US">. </span><span>The results showed that the implementation of Good Hospital Governance at the Prima Husada Cipta hospital Medan on the transparency aspect had been running but had not been carried out as a whole because many aspects were hospital privacy, in the implementation of hospital accountability it had been carried out in accordance with applicable regulations and standards. the implementation of the responsibility aspect that the hospital has carried out such as health human resources that have carried out their duties and functions in accordance with their respective professions, on the independence aspect that the hospital can carry out everything independently, in the aspect of fairness that the hospital has implemented and treated all employees fairly and fair.</span><span>Suggestions in this study are that it is necessary to implement aspects of transparency, accountability, responsibility, independence and fairness that can be carried out better and overall and can be used as targets in the internal audit of Prima Husada Cipta Hospital Medan</span></em>

Development and Validation of the Aircraft Noise related Fairness Inventory (fAIR-In)

Annoyance caused by aircraft noise is largely influenced by non-acoustic factors, such as the attitude towards the noise source, perceived control over the noise and the predictability of noise occurrence. Underlying many of these non-acoustic factors is perceived fairness, e.g., regarding information provision by the noise source or opportunities for noise-affected residents to participate in noise-related decision-making. A validated instrument to assess a variety of fairness aspects in surveys has been lacking. Therefore, the Aircraft Noise related Fairness Inventory (fAIR-In) has been developed, a multifaceted psychometric instrument. With 32 items, the fAIR-In assesses aspects of distributive, procedural, informational and interpersonal fairness of airport management. The development of the fAIR-In as well its validation by means of data from 1,367 residents living around three German airports are presented. Results reveal a high degree of predictive validity with regards to annoyance, airport and air travel acceptance, as well as protest behavior. Our findings indicate that the fAIR-In is a useful tool for capturing existing community perceptions of the airport, as well as facilitating the design, monitoring and evaluation process of interventions aimed at improving the relationship between the airport and its residents.

IMPLEMENTATION OF LAND ACQUISITION FOR PUBLIC INTEREST INFRASTRUCTURE DEVELOPMENT IN NORTH LUWU REGENCY REVIEWED FROM THE ASPECT OF JUSTICE

This study aims to explore the implementation of land acquisition processes for development in the public interest in North Luwu District and assess whether they have met the principles of justice. Using a descriptive qualitative approach as the research methodology, data collection was conducted through interviews and literature studies. Based on the discussion and analysis of literature studies and interview results, it can be explained that the land acquisition process involves planning stages, community socialization, land inventory and identification, land valuation, community consultations, and compensation provision. The importance of humanitarian and fairness aspects in land acquisition necessitates the provision of fair and reasonable compensation, taking in to account property value, financial losses, social losses, and non-material losses experienced by affected communities. Involving landowners and communities in the process of valuation and compensation negotiation is key to ensuring justice in the land acquisition process. The conclusion of this study is that land acquisition for development in the public interest in North Luwu District has been conducted effectively and can be considered to have fulfilled the principles of justice.

Key factors of business sustainability: Strengthening leadership, psychology, and fairness aspects from an Islamic-management perspective

Purpose: The aim of this study is to investigate the relationship between factors in HRM management development and organizational strategy using the philosophical values approach of Islamic studies. The assessment of organizational performance characteristics with strategic management values and psychological techniques in human resource management is part of the study's state-of-the-art and distinctive characteristics. 
 Design/Methodology/Approach: The method used in this study is Structural Equation Modeling (SEM) with the selection of analysis using non-parametric Smart-PLS. We analyzed 256 samples of lecturers at several private universities within the LLDIKTI-IX, Sulawesi, Indonesia.
 Findings: 14 hypotheses are reviewed in this study. According to our findings, 13 hypotheses have a positive and significant effect on the dependent variable. The antecedents of Islamic management values, leadership style, religiosity and fairness can increase complex effects on organizational motivation, HRM engagement, performance and organizational sustainability.
 Conclusion: Our findings provide theoretical and managerial reinforcement of various long-term strategic considerations to support the effect of organizational sustainability by reviewing the HRM aspect as an intangible asset.
 Research Implications: This study contributes to broadening the theoretical understanding of strategic management studies developed with a psychological approach as well as Islamic value management.
 Practical Implications: Organisations must pay attention to various crucial variables to boost job satisfaction, motivation and employee engagement such as HR development components and work evaluations carried out and developed on a regular basis.

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. To evaluate fairness in prediction models for development of psychosis and functional outcome. Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.