Concept Of Fairness Research Articles

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.

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.

Interpretation of Fairness Concept and Economic Impact Analysis of Carbon Quota Allocation between Industries in Indonesia

Abstract Quota allocation is critical in implementing supporting policies for carbon reduction efforts, such as the carbon cap and trade mechanism. Depending on the allocation rules, certain company types may benefit more economically. Inequality conditions can happen and reduce the willingness to participate in sustainability efforts. This study aims to propose fairness interpretations in the context of company-level carbon quota allocation. Four industries in Indonesia with inherently different emission and financial profiles were selected as case studies: energy, oil palm, basic materials, and finance. Fairness principles were analyzed to represent the interests of companies based on their financial and environmental performance. Indicators were selected to quantify the equality principles and aggregated into a Comprehensive Index (CI) for quota allocation, where the companies’ quota surplus/deficit is quantified into monetary value to estimate the economic impact with varying reduction targets. According to our analysis, the sectoral advantages/disadvantages would depend on the viewpoint of fairness principles, and the oil palm and financial sectors be less impacted than the basic materials and energy sectors with increased emission reduction targets. The sensitivity analysis suggests that the vertical, horizontal, polluter pays, and historical responsibility principles are more sensitive to weighting than the merit and basic needs principles.

CAN AUTOMATIC POETRY GENERATION INFUSE VALUES? UNVEILING INSIGHTS THROUGH CONTENT ANALYSIS OF GENERATED POETRY

This research examines the value underlying poetry created by artificial intelligence (AI), aiming to uncover the complex interaction of religious, philosophical, ethical, and aesthetic values presented in poetry. Eighteen poems generated by three different AI platforms were analyzed using the content analysis method to identify their social values. The results of this research demonstrate the presence of (1) religious values by revealing the AI’s ability to explore traditional and cultural barriers, highlighting themes of spirituality and faith, (2) philosophical values through the exploration of questions of human existence and the nature of reality, showing the AI's capacity to engage with deep existential themes, (3) ethical values by promoting concepts of unity, fairness, and justice, reflecting deeper humanistic perspectives and societal ideals, and (4) aesthetic values by showcasing an interest in the influence of technology and a profound admiration for the natural world's wonders, integrating modern innovation with timeless beauty. In conclusion, AI-generated poetry successfully captures a broad spectrum of human values, enriching literary traditions and intellectual discussions. This intersection of AI and poetry not only highlights technological advancements in creative fields but also opens new avenues for exploring how machines can contribute to cultural and artistic expressions, fostering a deeper understanding of the human condition and societal values.

Fairness in Large Language Models: A Taxonomic Survey

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a comprehensive overview of recent advances in the existing literature concerning fair LLMs. Specifically, a brief introduction to LLMs is provided, followed by an analysis of factors contributing to bias in LLMs. Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness. Furthermore, resources for evaluating bias in LLMs, including toolkits and datasets, are summarized. Finally, existing research challenges and open questions are discussed.

Japanese health and physical education teachers’ positioning in teaching Japanese language learners in high school physical education

ABSTRACT Background In recent years, the number of Japanese Language Learner (JLL) students, or children who speak a language other than Japanese and require Japanese language instruction, has been rapidly increasing in Japanese schools. Successful inclusion of students who use Japanese as a second language in the classroom creates opportunities for all students to engage with multiple languages and learn new cultures, backgrounds, history, and aspects of diversity that help them strengthen their overall cognitive abilities [Sato, T., and S. R. Hodge. 2009. “Asian International Doctoral Students’ Experiences at Two American University: Assimilation, Accommodation, and Resistance.” Journal of Diversity in Higher Education 2(3): 136-148]. In particular, physical education (PE) is considered an academic subject that can be a powerful tool to promote peace, tolerance, and understanding by bringing people together across boundaries, cultures, and religions by promoting the concepts of teamwork, fairness, discipline, and respect for the opponent in a game or activity [Beutler, I. 2008. “Sport Serving Development and Peace: Achieving the Goals of the United Nations Through Sport.” Sport in Society 11 (4): 359–369]. The purpose of this study is to describe and explain Japanese high school health and physical education (HPE) teachers’ positioning of themselves and others, as shown through a storyline, when they teach and assist Japanese Language Learner (JLL) students in academic and social contexts. Participants and setting Seven HPE teachers participated in this study by sharing their thoughts, opinions, and experiences related to teaching JLL students in PE. The teachers had between 3 and 23 years of experience teaching PE, and 2–9 years of experience teaching JLL students in high school PE. Research design This study utilized a descriptive-qualitative design using an in-depth, semi-structured interview approach [Seidman, I. 2019. Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences. 5th ed. New York: Teacher College Press]. A constant comparative analysis method was used to analyze the data. In this study, the process of constantly comparing data from several sources led to tentative categories that were compared to each other and to other data in order to construct categories. In this study, meaningful narrative phrases were those related to PE pedagogical content and teacher development. Findings Explainable by concepts in positioning theory [Harré, R., and L. van Langenhove. 1999. “The Dynamics of Social Episodes.” In Positioning Theory: Moral Contexts of Intentional Action, edited by R. Harré, and L. van Langenhove, 1–13. Oxford: Blackwell], three major interrelated and complex themes emerged from the data analysis: (a) teachers’ positioning in securing a safe learning environment for JLL students in PE, (b) teachers’ dilemma in assessing JLL students’ learning outcomes based on performance assessments and written exams in PE, and (c) teachers’ positioning in their responsibilities as both teacher and host parent. To enhance the quality of PE classes, school districts need clear and focused goals related to assessment and evaluation, instructional planning, and responsibilities involved in teaching JLL students.

What is fair? Proxy discrimination vs. demographic disparities in insurance pricing

Discrimination and fairness are major concerns in algorithmic models. This is particularly true in insurance, where protected policyholder attributes are not allowed to be used for insurance pricing. Simply disregarding protected policyholder attributes is not an appropriate solution as this still allows for the possibility of inferring protected attributes from non-protected covariates, leading to the phenomenon of proxy discrimination. Although proxy discrimination is qualitatively different from the group fairness concepts discussed in the machine learning and actuarial literature, group fairness criteria have been proposed to control the impact of protected attributes on the calculation of insurance prices. The purpose of this paper is to discuss the relationship between direct and proxy discrimination in insurance and the most popular group fairness axioms. We provide a technical definition of proxy discrimination and derive incompatibility results, showing that avoiding proxy discrimination does not imply satisfying group fairness and vice versa. This shows that the two concepts are materially different. Furthermore, we discuss input data pre-processing and model post-processing methods that achieve group fairness in the sense of demographic parity. As these methods induce transformations that explicitly depend on policyholders' protected attributes, it becomes ambiguous whether direct and proxy discrimination is, in fact, avoided.

Best of Both Worlds: Agents with Entitlements

Fair division of indivisible goods is a central challenge in artificial intelligence. For many prominent fairness criteria including envy-freeness (EF) or proportionality (PROP), no allocations satisfying these criteria might exist. Two popular remedies to this problem are randomization or relaxation of fairness concepts. A timely research direction is to combine the advantages of both, commonly referred to as Best of Both Worlds (BoBW). We consider fair division with entitlements, which allows to adjust notions of fairness to heterogeneous priorities among agents. This is an important generalization to standard fair division models and is not well-understood in terms of BoBW results. Our main result is a lottery for additive valuations and different entitlements that is ex-ante weighted envy-free (WEF), as well as ex-post weighted proportional up to one good (WPROP1) and weighted transfer envy-free up to one good (WEF(1, 1)). We show that this result is tight – ex-ante WEF is incompatible with any stronger ex-post WEF relaxation. In addition, we extend BoBW results on group fairness to entitlements and explore generalizations of our results to instances with more expressive valuation functions.

The Dimension of Sincerity According To The Qur’an And Sunnah: The Key To Accepting Deeds

This article aims to examine the dimension of sincerity (sincerity) in deeds according to the Qur'an and Sunnah, emphasizing the purity of intention, consistency in doing good, and independence from human praise. This study uses a qualitative method with textual analysis of relevant Qur'anic verses and hadiths. The findings show that sincerity encompasses relationships with both Allah and fellow humans, manifested through the concept of tawakkal (reliance on Allah) and fairness in social interactions. Sincerity in human relations creates an environment full of mutual understanding and compassion. In Islam, sincerity, derived from the Arabic word "khalasa" meaning purification, is about performing worship solely for Allah without seeking human praise. In the Qur'an, sincerity encompasses choice, purity from flaws, exclusivity, and the oneness of Allah. Sincerity is a prerequisite for the acceptance of deeds, prayers, and repentance, and it brings great benefits such as the acceptance of deeds, great rewards, and protection from satanic interference. Hadiths from Umar ibn al-Khattab, Abu Hurairah, and Abu Sa'id al-Khudri emphasize the importance of pure intentions. Sincerity helps in forming honest character, enhancing the quality of worship, social harmony, spiritual resilience, and work ethic. Understanding and applying sincerity supports individuals and communities to be responsible and have high integrity, hence the need to intensify education on sincerity for broad positive impacts.

A Review of Resilience and Fairness Concepts and Modeling in Humanitarian Logistics Using a Meta-analysis Approach

A Review of Resilience and Fairness Concepts and Modeling in Humanitarian Logistics Using a Meta-analysis Approach

Double Dutch: Illumina/GRAIL, Article 22 and the General Court

This article explores the jurisdictional issues underlying the decision of the European Commission to review Illumina’s acquisition of GRAIL. It marked the first the time the Commission investigated an acquisition where the target had no activities in the EU; nor indeed any nexus to any individual Member State. The decision to review a concentration where the target had no EU revenues was premised on a re-interpretation of Article 22(1) of the EU Merger Regulation, which allows Member States to request the Commission to examine a concentration. This interpretation is intended to expand the jurisdictional reach of the regulation to capture transactions involving companies with low turnover, but high competitive potential in the internal market, in cases where neither EU nor any Member State merger control thresholds are met. The General Court upheld the Commission’s position on Article 22 jurisdiction, but the judgment is on appeal to the Court of Justice. This article argues that the Court should overturn the earlier ruling on the basis that the GC was mistaken in its approach to Article 22, in view of its history and purpose. It also considers that flaws in the conduct of the procedure which deprived the merging parties of due process warranted an annulment of the decision to open an investigation. Moreover, this article calls out the incompatibility of the Commission’s application of Article 22 in Illumina/GRAIL with international law concepts of comity, fairness and sovereignty.

Pengaruh Politik Minoritas di Indonesia dalam Pilpress 2024 (Relevansi Konsep Fairness John Rawls)

The focus of this research examines the relevance and implications of John Rawls' concept of fairness in the context of minority politics in Indonesia, especially in the 2024 presidential election. John Rawls, in his A Theory of Justice, proposes the concept of justice as fairness, which is based on two principles: the same basic freedoms for all citizens and fair equality of opportunity. This study analyzes how this concept can be applied to Indonesia's political situation, where ethnic and religious minorities are often marginalized in the political and policy-making process. Through literature analysis and public opinion survey methods, this study evaluates how the 2024 presidential election reflects Rawls' principles of justice, particularly in terms of fair treatment, distribution of opportunities, and inclusion. The findings show that the Prabowo-Gibran presidential ticket received widespread support from various religious groups, including minorities, indicating the potential for inclusivity and acceptance in the context of this election. However, the study also underlines the risk of polarization and discrimination in political campaigns, emphasizing the need to maintain plurality

Fairness and Diversity in Recommender Systems: A Survey

Recommender systems (RS) are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware RS. While most existing studies explore fairness and diversity independently, we identify strong connections between these two domains. In this survey, we first discuss each of them individually and then dive into their connections. Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level. With this expanded perspective on user and item-level diversity, we re-interpret fairness studies from the viewpoint of diversity. This fresh perspective enhances our understanding of fairness-related work and paves the way for potential future research directions. Papers discussed in this survey along with public code links are available at: https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems .

Causal fairness assessment of treatment allocation with electronic health records

Objective:Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. Methods:We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. Results:Our analysis reveals notable disparities in coronary artery bypass grafting (CABG) allocation among different patient groups. Women were found to be 4.4%–7.7% less likely to receive CABG than men in two out of four treatment response strata. Similarly, Black or African American patients were 5.4%–8.7% less likely to receive CABG than others in three out of four response strata. These results were similar when social determinants of health (insurance and area deprivation index) were dropped from the algorithm. These findings highlight the presence of disparities in treatment allocation among similar patients, suggesting potential unfairness in the clinical decision-making process. Conclusion:This study introduces a novel approach for assessing the fairness of treatment allocation in healthcare. By incorporating responses to treatment into fairness framework, our method explores the potential of quantifying fairness from a causal perspective using EHR data. Our research advances the methodological development of fairness assessment in healthcare and highlight the importance of causality in determining treatment fairness.

When fairness is an abstraction: equity and AI in Swedish compulsory education

ABSTRACT The paper emphasises the need to consider the social, political, and economic contexts within which educational systems operate and use Artificial Intelligence (AI). Focused on Swedish compulsory education, this study explores whether the use of AI envisioned by national authorities and educational technology companies contributes to unfairness. Through qualitative content analysis of Swedish policy documents and educational company reports, the study employs the concept of Relevant Social Groups to assess how diverse stakeholders perceive the risks and benefits of using AI in education regarding fairness. Three distinct groups are identified, each prioritising different forms of “efficiency” as a key value—economic, pedagogical, and accessibility-related. In conclusion, this study sheds light on the intricate interplay between fairness and the use of AI in the Swedish educational system. It also questions the concept of fairness revolving around formal equality of opportunities that separates fairness from the broader context of social justice.

Disambiguating Concepts of Fairness in Stormwater Management: A Review of Economic Efficiency and Equity

AbstractTo confront the converging challenges of failing infrastructure, climate change, degraded water quality, and fewer undeveloped lands, many municipalities are establishing stormwater utilities (SWUs) to allocate a dedicated funding source to stormwater management (SWM) services. As a public service, creating a “fair” SWU by defining collective values that reflect community and municipal needs is crucial. However, the prevalent professional discourse surrounding SWUs often conflates “equity” with “economic efficiency” when they are two theoretically separate concepts, which obscure concerns raised by service beneficiaries. This paper unpacks ideas of fairness based on a systematic literature review that frames SWM financing in terms of economic efficiency and SWM services in terms of distributional, procedural, and structural equity. The distinction of which is important to set appropriate expectations between SWUs and beneficiaries.

Envy-free relaxations for goods, chores, and mixed items

In fair division problems, we are given a set S of m items and a set N of n agents with individual preferences, and the goal is to find an allocation of items among agents so that each agent finds the allocation fair. There are several established fairness concepts and envy-freeness is one of the most extensively studied ones. However envy-free allocations do not always exist when items are indivisible and this has motivated relaxations of envy-freeness: envy-freeness up to one item (EF1) and envy-freeness up to any item (EFX) are two well-studied relaxations. We consider the problem of finding EF1 and EFX allocations for utility functions that are not necessarily monotone, and propose four possible extensions of different strength to this setting.In particular, we present a polynomial time algorithm for finding an EF1 allocation for two agents with arbitrary utility functions. An example is given showing that EFX allocations need not exist for two agents with non-monotone, non-additive, identical utility functions. However, when all agents have monotone (not necessarily additive) identical utility functions, we give a pseudo-polynomial time algorithm that always finds an EFX allocation of chores. As a step toward understanding the general case, we discuss two subclasses of utility functions: Boolean utilities that are {0,+1}-valued functions, and negative Boolean utilities that are {0,−1}-valued functions. For the latter, we give a polynomial time algorithm that finds an EFX allocation when the utility functions are identical.

Decentralized Dreams - Crafting a Secure and Fair Blockchain Gaming Environment

This ambitions to discover the potential of blockchain generation in revolutionizing the gaming enterprise through developing a secure and honest gaming environment. This studies assignment delves into the concepts of decentralization, security, and fairness inside blockchain-based gaming systems, focusing on the improvement and essential evaluation of a prototype platform. Keywords: blockchain era, gaming industry, decentralization, safety, equity, prototype platform. Through an intensive literature overview, methodological technique, implementation, and vital analysis, this task investigates the challenges and opportunities in crafting a blockchain-based gaming environment that prioritizes consumer safety, asset possession, and honest gameplay. The results and findings from the implementation phase are analyzed to assess the platform's performance, scalability, and person revel in, supplying valuable insights for future improvements and advancements in blockchain gaming structures. Keywords—Blockchain Technology, Decentralized Gaming, Ownership, In-Game Assets, Smart Contracts, Security Measures, Fairness Mechanisms, User Experience, Scalability, Interoperability, Market Research, Regulatory Compliance, Innovation, Player Engagement, Future Improvements.