Fairness Index Research Articles

Falcon: Fair Active Learning Using Multi-Armed Bandits

Biased data can lead to unfair machine learning models, highlighting the importance of embedding fairness at the beginning of data analysis, particularly during dataset curation and labeling. In response, we propose Falcon, a scalable fair active learning framework. Falcon adopts a data-centric approach that improves machine learning model fairness via strategic sample selection. Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e.g., (attribute=female, label=positive)) that are the most informative for improving fairness. However, a challenge arises since these target groups are defined using ground truth labels that are not available during sample selection. To handle this, we propose a novel trial-and-error method, where we postpone using a sample if the predicted label is different from the expected one and falls outside the target group. We also observe the trade-off that selecting more informative samples results in higher likelihood of postponing due to undesired label prediction, and the optimal balance varies per dataset. We capture the trade-off between informativeness and postpone rate as policies and propose to automatically select the best policy using adversarial multi-armed bandit methods, given their computational efficiency and theoretical guarantees. Experiments show that Falcon significantly outperforms existing fair active learning approaches in terms of fairness and accuracy and is more efficient. In particular, only Falcon supports a proper trade-off between accuracy and fairness where its maximum fairness score is 1.8--4.5x higher than the second-best results.

Yesterday and today of the religious fact from the registration of the church to the new context of the metaverse: special constitutional approach from Spain

In a multicultural society of change like the one that happens to us, the religious fact continues to have a transcendental role, an issue that also implies legal considerations that must be studied and considered. From this premise we will address, especially in constitutional terms, two phenomena that will undoubtedly be a space for future decisions that offer fair legal consequences and measures. In the first place, the resolution of the socio-political controversy that has arisen in recent years on the registration of the goods of the Catholic Church in Spain, giving a peripheral vision, offering a study of comparative law in this regard; and secondly, we will offer the legal arguments to scaffold an approach about how an ex novo cybernetic environment should be legally treated by the current system for the religious fact in a virtual reality of massive social interaction such as the metaverse.

Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules

Smart grid operators seeking to extend their wireless network capacity can use unlicensed bands. However, devices in these shared bands must follow rules such as Listen Before Talk (LBT), standardized by ETSI. In this paper, we focus on the performance of the frame-based equipment (FBE) version of LBT channel access. We design, implement, and validate a fully functional FBE channel access simulator. Next, we conduct an extensive performance analysis of the FBE variants encountered in the literature, focusing on channel efficiency and fairness in upper-bound and coexistence scenarios. Our study leads to several conclusions about the operation of FBE-based devices, including the need for proper configuration of channel access parameters to ensure fairness and optimal performance. We also observe generally poor coexistence among FBE variants: the highest Jain’s fairness index was only 0.88, with an average normalized channel efficiency of 0.76. Therefore, we identify several open research areas in the field, such as the need for further development of parameter adaptation algorithms, the deployment of an external controller to update channel access parameters, and new FBE designs with better coexistence qualities.

The Anchoring Effect, Algorithmic Fairness, and the Limits of Information Transparency for Emotion Artificial Intelligence

Emotion artificial intelligence (AI) is shown to vary systematically in its ability to accurately identify emotions, and this variation creates potential biases. In this paper, we conduct an experiment involving three commercially available emotion AI systems and a group of human labelers tasked with identifying emotions from two image data sets. The study focuses on the alignment between facial expressions and the emotion labels assigned by both the AI and humans. Importantly, human labelers are given the AI’s scores and informed about its algorithmic fairness measures. This paper presents several key findings. First, the labelers’ scores are affected by the emotion AI scores, consistent with the anchoring effect. Second, information transparency about the AI’s fairness does not uniformly affect human labeling across different emotions. Moreover, information transparency can even increase human inconsistencies. Plus, significant inconsistencies in the scoring among different emotion AI models cast doubt on their reliability. Overall, the study highlights the limitations of individual decision making and information transparency regarding algorithmic fairness measures in addressing algorithmic fairness. These findings underscore the complexity of integrating emotion AI into practice and emphasize the need for careful policies on emotion AI.

A value sharing method for heterogeneous energy communities archetypes

A novel value sharing (VS) method is proposed that distributes the energy communities (ECs) value based on the individual contribution to the total surplus/deficit. It considers the load-generation profile of each EC member and allocates a higher share to members who contribute to the EC revenue. The lowest share is received by the members with the highest demand that has to be supplied from the shared generation or from the grid, contributing to the EC cost. Several allocation methods are compared using the fairness index (FI), and, for setting the strategy of the EC using a decision model, as the strategy may vary over time, an objective function is defined as a combination between FI and self-sufficiency index using weighting coefficients. The methodology is implemented as an algorithm that automatically calculates and distributes the gain. For the proposed VS method, the FI is between 0.81 and 1.

Routing and scheduling co-design for holistic software-defined deterministic network

As a result of the growing complexity of industrial Internet applications, traditional hardware-based network designs are encountering challenges in terms of programmability and dynamic adaptability as they struggle to meet the real-time, high-reliability transmission requirements for the vast quantities of data generated in industrial environments. This paper proposes a holistic software-defined deterministic network (HSDDN) design solution. This solution uses a centralized controller to implement a comprehensive software definition, ranging from the network layer down to the physical layer. Within the wireless access domain, we decouple the standard radio-frequency modules from baseband processing to realize a software-defined physical layer, which then allows us to adjust the data transmission cycles and tag the trigger rates to meet demand for low-power, high-concurrency transmission. Within the wired network domain, we integrate software-defined networking with time-sensitive networking and propose a coordinated design strategy to address routing and the deterministic scheduling problem. We define a set of constraints to ensure collaborative transmission of the periodic and aperiodic data flows. To guarantee load balancing across all paths and timeslots, we introduce the Jain’s fairness index as the optimization objective and then construct a nondeterministic polynomial-time (NP)-hard joint optimization problem. Furthermore, an algorithm called Tabu search for routing and scheduling with dual-stages (TSRS-DS) is proposed. Simulation experiments demonstrate the effectiveness of the proposed HSDDN architecture.

CoopCon: Cooperative Hybrid Congestion Control Scheme for Named Data Networking

Congestion control is a key technology for guaranteeing quality-of-service (QoS) in Named Data Networking (NDN). Hybrid congestion control has gradually developed into the mainstream method in NDN congestion control, which capitalizes on the advantages of receiver adjusting rate and router diverting traffic to deal with congestion. However, it has to address how to effectively coordinate consumers and routers to prevent transport performance degradation caused by repeated and excessive control. In this paper, a hybrid congestion control scheme named CoopCon is proposed, which fully gives the cooperation between consumers and routers to control the congestion adaptively. Moreover, the optimal path is used resiliently by CoopCon to enhance the robustness of multipath forwarding and multicast data delivery in NDN. The proposed CoopCon is implemented in ndnSIM. And simulation results show that CoopCon consistently achieves higher total throughput than existing work. In particular, the total throughput of consumers deployed with CoopCon is 19.4% higher than that of consumers deployed with PCON in the BRITE-generated topology. Additionally, CoopCon also achieves the best fairness in a dumbbell topology, with a fairness index of even 0.96.

Algorithmic Fairness, Risk, and the Dominant Protective Agency

With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among algorithmic group fairness measures proposed, one—sufficiency (well-calibration) is morally defensible for insurers to use, whereas independence (statistical parity or demographic parity) and separation (equalized odds) are not normatively appropriate in the insurance context. Such a result may seem to be of relatively narrow interest to insurers and insurance scholars only. We argue, however, that arguments such as that offered by Baumann and Loi have an important but so far overlooked connection to the derivation of the minimal state offered by Nozick (1974) and thus to political philosophy at large.

On the trade-off between ranking effectiveness and fairness

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user’s model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed.

Remittances-ecological footprint nexus in Africa: Do ICTs matter?

The growth of migrant remittances poses a real challenge in developing countries both in terms of development and the quality of the environment. This paper examines the effect of migrant remittances on the ecological footprint in Africa with taking account the role of Information and Communication Technology (ICT). Unlike previous work that has considered carbon dioxide emissions as a measure of environmental degradation, this study takes into account the ecological footprint as a fair measure of environmental quality. Our sample consists of 30 African countries from 2000 to 2021. We use two methodologies, respectively Two-Stage Least Squares (2SLS) with instrumental variable and quantile for robustness. The results show that remittances contribute to environmental degradation by increasing the ecological footprint. Moreover, taking into account the modified environmental Kuznets curve effect, our results show that more migrants' remittances increase, more they contribute to improving the environment by improving the ecological footprint. Our results remain robust using quantile regression. Moreover, ICTs reduce the adverse effect of remittances on environmental degradation. In addition, the results reveal that urbanization and human capital improve environmental quality but energy consumption damage it in Africa. Besides, reducing remittances costs and considering incentives to increase remittances inflows to the country remains a suitable target under sustainable development. Finally, there is evidence to implement effective financial system to soften investment credits granting intended for clean technology. Households and individuals should minimize their use of remittances to finance carbon-intensive items like automobiles and others. Moreover, is been necessary to improve ICT penetration and his efficiency in the African countries’ context.

Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study

Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of fairness in recommender systems. Specifically, we focus solely on exposure-based fairness measures of individual items that aim to quantify the disparity in how individual items are recommended to users, separate from item relevance to users. We gather all such measures and we critically analyse their theoretical properties. We identify a series of limitations in each of them, which collectively may render the affected measures hard or impossible to interpret, to compute, or to use for comparing recommendations. We resolve these limitations by redefining or correcting the affected measures, or we argue why certain limitations cannot be resolved. We further perform a comprehensive empirical analysis of both the original and our corrected versions of these fairness measures, using real-world and synthetic datasets. Our analysis provides novel insights into the relationship between measures based on different fairness concepts, and different levels of measure sensitivity and strictness. We conclude with practical suggestions of which fairness measures should be used and when. Our code is publicly available. To our knowledge, this is the first critical comparison of individual item fairness measures in recommender systems.

Method for evaluating fairness of electricity tariffs with regard to income level of residential buildings

Modern advancements in energy technology, such as smart meters and renewable power generation, have contributed to the increasing penetration of time-variable electricity pricing plans. Under such plans, consumers experience a higher financial burden for consuming electricity when overall demand is high. Lower-income households may be disproportionately burdened by the transition to time-variable pricing because they tend to have less efficient homes and appliances, which may necessitate greater overall electricity consumption, especially during peak times. The goal of this work is to create a broadly applicable framework for evaluating the fairness of utility pricing plans. The proposed slope analysis method examines the distribution of a fairness metric across income levels in order to determine the level of fairness exhibited by a pricing plan. This work utilizes three fairness metrics, which are based on total household electricity bill and income, as a proof-of-concept for the slope analysis method and assesses their viability for fairness research. The proposed method also utilizes household energy models to represent various income levels for any location with sufficient data. The framework is evaluated using simulated households across 5 income levels and 3 climate zones in the United States. Fairness metrics are applied to the utility bills calculated under Real-Time Pricing and existing tariffs offered at each location. The proposed fairness evaluation method provides a quantitative measure of fairness and is broadly applicable across location and pricing plans. The metric based on the change in percentage of income spent on utilities considers the relative financial burden on households, which results in the slope analysis method outputting conclusive, accurate fairness determinations more often than the other examined metrics. The results demonstrate disparity in energy affordability, and the proposed slope analysis and model simulation methods provide a readily transferable testbed to evaluate energy policy equity.

Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.

Uncovering Bias: Exploring Machine Learning Techniques for Detecting and Mitigating Bias in Data – A Literature Review

The presence of Bias in models developed using machine learning algorithms has emerged as a critical issue. This literature review explores the topic of uncovering the existence of bias in data and the application of techniques for detecting and mitigating Bias. The review provides a comprehensive analysis of the existing literature, focusing on pre-processing techniques, post-pre-processing techniques, and fairness constraints employed to uncover and address the existence of Bias in machine learning models. The effectiveness, limitations, and trade-offs of these techniques are examined, highlighting their impact on advocating fairness and equity in decision-making processes.
 The methodology consists of two key steps: data preparation and bias analysis, followed by machine learning model development and evaluation. In the data preparation phase, the dataset is analyzed for biases and pre-processed using techniques like reweighting or relabeling to reduce bias. In the model development phase, suitable algorithms are selected, and fairness metrics are defined and optimized during the training process. The models are then evaluated using performance and fairness measures and the best-performing model is chosen. The methodology ensures a systematic exploration of machine learning techniques to detect and mitigate bias, leading to more equitable decision-making.
 The review begins by examining the techniques of pre-processing, which involve cleaning the data, selecting the features, feature engineering, and sampling. These techniques play an important role in preparing the data to reduce bias and promote fairness in machine learning models. The analysis highlights various studies that have explored the effectiveness of these techniques in uncovering and mitigating bias in data, contributing to the development of more equitable and unbiased machine learning models. Next, the review delves into post-pre-processing techniques that focus on detecting and mitigating bias after the initial data preparation steps. These techniques include bias detection methods that assess the disparate impact or disparate treatment in model predictions, as well as bias mitigation techniques that modify model outputs to achieve fairness across different groups. The evaluation of these techniques, their performance metrics, and potential trade-offs between fairness and accuracy are discussed, providing insights into the challenges and advancements in bias mitigation. Lastly, the review examines fairness constraints, which involve the imposition of rules or guidelines on machine learning algorithms to ensure fairness in predictions or decision-making processes. The analysis explores different fairness constraints, such as demographic parity, equalized odds, and predictive parity, and their effectiveness in reducing bias and advocating fairness in machine learning models. Overall, this literature review provides a comprehensive understanding of the techniques employed to uncover and mitigate the existence of bias in machine learning models. By examining pre-processing techniques, post-pre-processing techniques, and fairness constraints, the review contributes to the development of more fair and unbiased machine learning models, fostering equity and ethical decision-making in various domains. By examining relevant studies, this review provides insights into the effectiveness and limitations of various pre-processing techniques for bias detection and mitigation via Pre-processing, Adversarial learning, Fairness Constraints, and Post-processing techniques.

Analysis on Fairness and Efficiency of the 3-PLP LDM System Using a Normalized Channel Capacity

This article presents a novel approach to investigate efficient resource allocation in the layered division multiplexing (LDM) system, a key technology in next-generation broadcasting systems. The LDM system provides diverse services, resulting in different channel capacities for each service, unlike the conventional non-orthogonal multiple access scheme that aims for equal channel capacity among users. To achieve efficient resource allocation among services, we proposed a new method that normalizes the channel capacities allocated to different services while maintaining high fairness. Our proposed method involves normalizing the channel capacities of each service and finding the point where the sum of normalized channel capacities is maximized. This point can be obtained using the Lagrange multiplier method, ensuring a high normalized fairness index. Through simulations, we confirmed that the proposed approach maintains high fairness in the LDM system and achieves the highest normalized channel capacities compared to other transmission techniques. These findings provide valuable insights into resource allocation strategies for the LDM system, enabling a fair comparison of efficiency and performance within a multi-service environment.

Gerrymandering individual fairness

Individual fairness requires that similar individuals are treated similarly. It is supposed to prevent the unfair treatment of individuals on the subgroup level and to overcome the problem that group fairness measures are susceptible to manipulation or gerrymandering. The goal of the present paper is to explore the extent to which individual fairness itself can be gerrymandered. It will be proved that individual fairness can be gerrymandered in the context of predicting scores. Then, it will be argued that individual fairness is a very weak notion of fairness for some choices of feature space and metric. Finally, it will be discussed which properties of (individual) fairness are desirable.

A chaotic whale optimization algorithm (CWOA) based multiobjective conjoint spectrum utilization in a cognitive radio (CR) network

SummaryIn this manuscript, a novel energy detection‐based chaotic whale optimization algorithm (ED‐CWOA) is proposed to maximize the spectrum utilization, duration, and threshold in cognitive radio network (CRN). Here, the spectrum utilization is designed taking into account sensing duration and threshold in multi‐objective function is formulated by considering its primary user (PU) protection restrictions and secondary user (SU) throughput. To get the global optimal solution, the CWOA and the multi‐objective optimization problem are finally carried out on a single objective. The execution is performed on NS2 simulator. The simulation outcome demonstrates that the proposed ED‐CWOA system has lower delay 33.878%, 26.206%; higher delivery ratio 35.536%, 22.65%; lower Fairness Index 46.66%, 58.332%; lower energy consumption 25.462%, 35.524%; and higher throughput 96.82%, 49.28% for different nodes analyzed with the existing methods, like energy detection‐based bivariate Levy‐stable bat algorithm (ED‐BLSBA) and genetic algorithm‐based energy detection (ED‐GA), respectively. The simulation outcome indicates that the proposed system may be able to get the optimal global solutions efficiently and accurately.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

State information security strategies in conditions of hybrid threats

Hybrid information threats under the conditions of modern development of digital technologies are currently becoming one of the major issues for a modern democracy. The amount of damage that hybrid threats bring to the world economy contributes to the establishment of effective legal mechanisms to combat them. The purpose of the study was to single out the information security strategies under conditions of hybrid threats, including the spread of disinformation and fake news. The application of the comparative analysis method mad it possible to identify the gaps in information security strategies for countering hybrid threats. The strategy of information security in the conditions of hybrid threats is a coordinated action plan aimed at countering and fighting hybrid threats to safeguard cyberspace and preserve a democracy. Information security against hybrid threats is based on such cornerstones as: availability, confidentiality, integrity of information data, and safety. Enhancement of information security under the conditions of hybrid threats should be carried out at the expense of digital transformation, increasing the level of digital literacy of society and establishing a fair responsibility measure for a purposeful spreading of disinformation. The perspective of further research is addressing information security strategies as well as elaborating practical guidelines for the formation of a secure information space.