Fairness Index Research Articles

A functional vulnerability framework for biodiversity conservation

Setting appropriate conservation strategies in a multi-threat world is a challenging goal, especially because of natural complexity and budget limitations that prevent effective management of all ecosystems. Safeguarding the most threatened ecosystems requires accurate and integrative quantification of their vulnerability and their functioning, particularly the potential loss of species trait diversity which imperils their functioning. However, the magnitude of threats and associated biological responses both have high uncertainties. Additionally, a major difficulty is the recurrent lack of reference conditions for a fair and operational measurement of vulnerability. Here, we present a functional vulnerability framework that incorporates uncertainty and reference conditions into a generalizable tool. Through in silico simulations of disturbances, our framework allows us to quantify the vulnerability of communities to a wide range of threats. We demonstrate the relevance and operationality of our framework, and its global, scalable and quantitative comparability, through three case studies on marine fishes and mammals. We show that functional vulnerability has marked geographic and temporal patterns. We underline contrasting contributions of species richness and functional redundancy to the level of vulnerability among case studies, indicating that our integrative assessment can also identify the drivers of vulnerability in a world where uncertainty is omnipresent.

Cooperative NOMA-Based User Pairing for URLLC: A Max–Min Fairness Approach

In this article, cooperative nonorthogonal multiple access (C-NOMA) is considered in short packet communications with finite blocklength codes. The performance of a decode-and-forward relaying along with selection combining (SC) and maximum ratio combining (MRC) strategies at the receiver side is examined. We explore joint user pairing and resource allocation to maximize fair throughput in a downlink scenario. In each pair, the user with a stronger channel (strong user) acts as a relay for the other one (weak user), and optimal power and blocklength are allocated to achieve max–min throughput. To this end, first, only one pair is considered, and optimal resource allocation is explored. Also, a suboptimal algorithm is suggested, which converges to a near-optimal solution. Finally, the problem is extended to a general scenario, and a suboptimal C-NOMA-based user pairing is proposed. Numerical results show that the proposed C-NOMA scheme in both SC and MRC strategies significantly improves the users’ fair throughput compared to the NOMA and orthogonal multiple access (OMA). It is also investigated that the proposed pairing scheme based on C-NOMA outperforms the hybrid NOMA/OMA scheme from the average throughput perspective, while the fairness index degrades slightly.

Fairness Measures of Machine Learning Models in Judicial Penalty Prediction

<p>Machine learning (ML) has been widely adopted in many software applications across domains. However, accompanying the outstanding performance, the behaviors of the ML models, which are essentially a kind of black-box software, could be unfair and hard to understand in many cases. In our human-centered society, an unfair decision could potentially damage human value, even causing severe social consequences, especially in decision-critical scenarios such as legal judgment. Although some existing works investigated the ML models in terms of robustness, accuracy, security, privacy, quality, etc., the study on the fairness of ML is still in the early stage. In this paper, we first proposed a set of fairness metrics for ML models from different perspectives. Based on this, we performed a comparative study on the fairness of existing widely used classic ML and deep learning models in the domain of real-world judicial judgments. The experiment results reveal that the current state-of-the-art ML models could still raise concerns for unfair decision-making. The ML models with high accuracy and fairness are urgently demanding.</p> <p> </p>

A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System

Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness.

Spatial Pattern and Fairness Measurement of Educational Resources in Primary and Middle Schools: A Case Study of Chengdu-Chongqing Economic Circle.

China’s education has developed rapidly in recent years, but the issue of educational equality still exists. Currently, there are few studies on educational resources, and their spatial pattern and fairness remain unclear. Thus, this study selected the point of interest data and spatial analysis methods to depict the spatial pattern of educational resources (containing the number of teachers, students, facilities, etc.). Then, we evaluated the equity of educational resources (including the number of schools and school teachers) in terms of geographic and population distribution by combining statistical yearbook data with two indices (the index of dissimilarity and agglomeration degree) to promote healthy urban development. The results show the following. (1) Educational resources have a multicenter spatial structure of “dual cores and multiple sub-centers”. The Moran index reflects a weak positive spatial correlation between educational resources. (2) The index of dissimilarity is between 0.02 and 0.21, which shows that the allocation of resources is relatively balanced. Regarding internal units, obvious differences exist in the agglomeration degree and equilibrium of educational resources.

A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission

The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning approaches to make these predictions may result in health disparities caused by biases in the data related to social determinants (such as race, age, and gender). These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model’s decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. This pipeline analyses the clinical data and determines whether biases exist in the data, if so, it removes those biases before making predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures. The findings of the results show that when we mitigate biases early during the data ingestion, we get fairer predictions.

An Infrastructure Investment Primer: From Valuation to Allocation and Manager Selection

Infrastructure investments can play several important roles in the portfolio, from generating yield and income to creating diversification and matching or hedging liabilities. They are also relatively large and illiquid and, as such, present valuation and risk measurement challenges. Stale, biased, and limited contributed data have long left investors and regulators only able to make educated guesses. Recent developments in data collection and asset pricing techniques for private assets allow granular market-based valuations and risk-adjusted return calculations. This article introduces standard definitions for infrastructure investments and styles as well as a modern and robust asset pricing framework for the production of fair valuations and useful risk measures. It also presents stylized empirical facts based on 20 years of data for the risk-adjusted performance of unlisted infrastructure equity, systematic and extreme risks, asset allocation, and fund manager selection.

Egalitarian Machine Learning

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)).

Fair performance-based user recommendation in eCoaching systems

Offering timely support to users in eCoaching systems is a key factor to keep them engaged. However, coaches usually follow a lot of users, so it is hard for them to prioritize those with whom they should interact first. Timeliness is especially needed when health implications might be the consequence of a lack of support. In this paper, we focus on this last scenario, by considering an eCoaching platform for runners. Our goal is to provide a coach with a ranked list of users, according to the support they need. Moreover, we want to guarantee a fair exposure in the ranking, to make sure that users of different groups have equal opportunities to get supported. In order to do so, we first model their performance and running behavior and then present a ranking algorithm to recommend users to coaches, according to their performance in the last running session and the quality of the previous ones. We provide measures of fairness that allow us to assess the exposure of users of different groups in the ranking and propose a re-ranking algorithm to guarantee a fair exposure. Experiments on data coming from the previously mentioned platform for runners show the effectiveness of our approach on standard metrics for ranking quality assessment and its capability to provide a fair exposure to users. The source code and the preprocessed datasets are available at: https://github.com/wiguider/Fair-Performance-based-User-Recommendation-in-eCoaching-Systems.

An Underwater Cooperative Spectrum Sharing Protocol for a Centralized Underwater Cognitive Acoustic Network.

To efficiently utilize nonexclusive underwater acoustic frequencies, we propose an Underwater Cooperative Spectrum Sharing (UCSS) protocol for a centralized underwater cognitive acoustic network that mainly consists of two parts. In the first part, to check the random occurrence of interferers periodically, the time domain is divided into frames that consist of a sensing and a non-sensing sub-frame. Then, we set the ratio of the two sub-frames to enhance the sensing rate via simulations. As a result, there exists the upper limit of the ratio, which can be used for determining the proportion of the sensing time within a frame. The second part is to design two heuristic resource allocation (RA) algorithms. One is a multiround RA (MRRA), where a central entity allocates a data channel (i.e., resource) to a CU each round so that multiple rounds are executed until no CUs need to be allocated or there is a lack of data channels. The other is a single-round RA (SRRA), where a CU is allocated to as many data channels as its QoS within a round. We also specify four rules to determine the allocation order of the CUs: random, fixed, high-QoS-based, and low-channel allocation-rate-based. In this study, we investigate the best RA allocation order pair supporting the highest channel allocation rate and fairness index via extensive simulations. It is shown that the MRRA outperformed the SRRA, regardless of allocation orders at any conditions, and the random and low-channel allocation-rate-based allocation orders with MRRA supported the best performance. In particular, even without the optimization process, the MRRA guarantees more than 95% fairness.

Symbiosis Between D2D Communication and Industrial IoT for Industry 5.0 in 5G mm-Wave Cellular Network: An Interference Management Approach

Industrial Internet of Things (IIoT) paves way into Industry 5.0, which incorporates human–machine collaboration, thereby making manufacturing industry efficient. As 5G architecture supports massive IoT connectivity and has higher spectrum efficiency, device-to-device (D2D) communication is favorable at 28 GHz. While transmitting data from sensors to end user through IoT network, interference affects the system. Thus, an efficient resource allocation scheme is needed for minimizing interference and increasing data rate. Here, formulated problem is divided into two subproblems, channel assignment and power optimization in order to lower computational complexity. A partial resource multiplexing scheme is proposed that will allocate channels to available D2D users. Later, power optimization problem is formulated which is determined through Lagrangian dual optimization technique. Dynamic sectorization overcomes issue of increase in user traffic. Stability factor, fairness index (FI), and energy efficiency depict the performance superiority of proposed scheme over existing schemes. Simulation results prove efficacy of proposed system.

Fair Virtual Energy Storage System Operation for Smart Energy Communities

A virtual energy storage system (VESS) logically shares a physical energy storage system among multiple units. In resource sharing, the distribution of benefits is a critical problem. As a resolution, this study proposes a fair VESS operation method for smart energy communities that involve groups of energy consumption units. First, the cost and resource fairness indices are defined as the benefit and VESS usage proportional to the investment cost, respectively. The fair VESS operation problem is formulated considering the fairness indices that could be solved optimally by applying gradient methods without additional computational burden. The simulation results using the dataset in Korea demonstrate that the proposed operation allows the fair distribution of the benefit and resource usage among units with a marginal benefit reduction of approximately 5% in relation to the VESS operation to maximize the benefit. Moreover, it is shown that the resource fairness that controls the VESS usage limits the total benefit, and the cost fairness distributes the benefit among units according to the cost contribution. Furthermore, the proposed VESS operation can manage the VESS lifetime and improve the system performance of the utility grid.

Improving the Sum Rate and Fairness of MIMO Downlink Communications

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes.

Research on Resource Evaluation Mapping and Asteroid Mining Based on HFSE Model and Entropy Weight Method

In view of the scarcity of resources, we analyze the mapping process from existing resources to expected production value from a humanitarian point of view. After analyzing the fairness index from six different angles, we propose a solution and establish an HFSE model that reflects the difference between a country's economic structure and expectations. In addition, we also synthesize six aspects of the fairness index through the entropy weight method and get the overall fairness of a country. According to the current global situation, the policy of asteroid mining has been put forward. These recommendations enhance global competitiveness promptly and ensure that industries with national characteristics can give full play to their advantages and develop productive forces in an all-around way.

Minimal matchings of point processes

Suppose that red and blue points form independent homogeneous Poisson processes of equal intensity in {{mathbb {R}}}^d. For a positive (respectively, negative) parameter gamma we consider red-blue matchings that locally minimize (respectively, maximize) the sum of gamma th powers of the edge lengths, subject to locally minimizing the number of unmatched points. The parameter can be viewed as a measure of fairness. The limit gamma rightarrow -infty is equivalent to Gale-Shapley stable matching. We also consider limits as gamma approaches 0, 1-, 1+ and infty . We focus on dimension d=1. We prove that almost surely no such matching has unmatched points. (This question is open for higher d). For each gamma <1 we establish that there is almost surely a unique such matching, and that it can be expressed as a finitary factor of the points. Moreover, its typical edge length has finite rth moment if and only if r<1/2. In contrast, for gamma =1 there are uncountably many matchings, while for gamma >1 there are countably many, but it is impossible to choose one in a translation-invariant way. We obtain existence results in higher dimensions (covering many but not all cases). We address analogous questions for one-colour matchings also.

Allocation with Weak Priorities and General Constraints

Allocating scarce resources with (weak) priority and complex constraints has many applications ranging from course allocation, and healthcare rationing to refugee resettlement. Its generality, however, mostly leads to impossibility results. We offer a positive result with a mechanism based on a new concept called competitive stable equilibrium. This is a natural extension of competitive equilibrium with endowed budgets that accommodates weak priorities. Specifically, an agent only needs to pay for a resource if it belongs to the last tier among the agents currently consuming the resource. This concept applies to both frameworks of one-sided and two-sided markets in a unifying way and allows for a more flexible tie-breaking rule by giving agents different budgets. We empirically apply our mechanism to reassign season tickets to families in the presence of social distancing. Our simulation results show that our method outperforms existing ones in both efficiency and fairness measures.

Study on Conditional Conservatism Within Fair Value Measurements Based on Anti-discount Expectations.

As a rapidly growing emerging capital market, China has attracted the attention of both investors and scholars. To alleviate the expectation of external users of listed companies’ financial statements to “discount” items in levels 2 and 3 of the fair value measurement, listed companies will treat these items as conditional conservatism. It refers to the conservatism of companies when confirming bad news of unrealized gains and losses sooner than confirming good news. A sample was selected for empirical analysis to verify the existence of this relationship. The results are as follows: (1) the higher the proportion of levels 2 and 3 fair value measurements, the stronger the conditional conservatism of the company’s profit and loss; (2) the higher the proportion of cash holding of operating activities in a company’s operating profit and the higher the audit quality, the higher the proportion of levels 2 and 3 fair value hierarchy measurements, and the stronger the conditional conservatism of the company’s profit and loss; and (3) the lesser the internal control defects in a company, the higher the proportion of levels 2 and 3 fair value hierarchy measurements, and the stronger the conditional conservatism of its profit and loss. The findings provide empirical evidence to identify listed companies adopting conditional conservatism to alleviate discounting expectations of fair value items of financial statements’ external users and provide a reference for improving the standards and regulation of listed companies.

Fairness and Utilitarianism in Allocating Energy to EVs During Power Contingencies Using Modified Division Rules

In order to maximize utilitarianism and fairness in allocating energy to electric vehicles (EVs) during outages, this study proposes an optimization method based on modified division rules. The idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is introduced in this paper to maximize the number of EVs that are served during power contingencies. For EVs carrying out the time-critical task(s), the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is defined as the amount of energy required by EVs to accomplish their upcoming task(s). For EVs carrying out delay-tolerant tasks, it is defined as the amount of energy required by EVs to travel to a nearby healthy charging station. To this end, an EV ranking mechanism is devised considering the full and essential energy demands along with the urgency of EVs. Subsequently, EVs with higher ranks are prioritized during energy allocation. The performance of the proposed method is compared with four existing division rules, i.e., proportional, constrained equal awards, constrained equal losses, and sequential priority rules. A utilitarianism index is proposed to analyze the performance of these methods and fairness is evaluated using existing indices, such as Jain's fairness index and cost of fairness index. The proposed method has outperformed existing division rules in both utilitarianism and fairness for the essential energy demand. Finally, a sensitivity analysis of uncertain parameters such as EV fleet size, available/required energy, and weight factors is carried out to analyze the performance of the proposed method under various conditions.

Paper 21: Anterior Displacement of Tibial Spine Fractures, Does Anatomic Reduction Matter?

Objectives:Operative treatment of displaced tibial spine fractures consists of suture or screw fixation to reduce the fragment in addition to restoring tension of the ACL. In some instances, the reduction is not anatomic, but it is unknown whether this impacts healing, range of motion, or laxity. The purpose of this study is to determine if residual displacement of the anterior portion of a tibial spine fragment affects the range of motion or laxity in post-operative and non-operatively treated tibial spine patients and to assess if anterior lip displacement (ALD) predicts complications in these patients.Methods:Data was gathered from an institutional review board-approved multicenter retrospective cohort of patients treated for tibial spine fractures between January 1, 2000, and January 31, 2019, at 10 institutions. Patients younger than 25 years of age with tibial spine fractures were included. Five-hundred seventy-eight patients were included in the cohort, excluding patients with missing data. Range of motion and ALD measurements were summarized and compared from pre-treatment to the patient’s final visit. ALD measurements were categorized into four groups: 0 to <1 mm, excellent; 1 to <3 mm, good; 3 to 5mm, fair; and >5mm, poor.Results:Eighty-two percent (474/578) of patients had an operative treatment while only 18% of patients (104/578) had a non-operative treatment. There was a higher proportion of patients who had a positive Lachman test in the non-operative group (12%; 12/104) compared to the operative group (4%; 18/474; p=0.001). The surgical group had a median time of 3.4 months from surgery (range, 0.10 to 91.86 months) to final displacement measurement, while the non-operative group had a median time of 2.6 months from initial treatment to final displacement measurement (range, 0.07 to 61.37 months). Overall, there was no significant difference in the final range of motion measurements between these groups. The median anterior lip displacement measurement prior to treatment was 6.1mm and decreased to 0.7mm after treatment (p<0.001). At the final visit for patients, over half (52%; 248/477) were categorized in the excellent group for the anterior lip displacement measurement compared to only 4% (18/434) pre-treatment. Total range of motion prior to treatment was 75 degrees and it increased to 140 degrees after treatment (p<0.001). Patients categorized as having a fair final anterior lip displacement measurement had a 9.3 degree decrease in their final range of motion compared to patients categorized in the excellent group (β=-9.3; 95% CI=-13.77, -4.75; p<0.001). Patients categorized as having a poor final anterior lip displacement measurement had a 10.9 degree decrease in their final range of motion compared to patients categorized in the excellent group (β=-10.9; 95% CI=-18.26, -3.47; p=0.004). There were no associations detected between final anterior lip displacement category and laxity. Focusing on the operative group, there were no significant differences detected in laxity among the four different final anterior lip measurements (p=0.76). Post-hoc tests determined that the median total range of motion was larger for the excellent group (140 degrees) compared to the fair group (132.5 degrees; p<0.001). Similarly, there were significant differences detected between the excellent and fair group for flexion and extension measurements (p<0.001 and p=0.001, respectively). Eighteen patients (one non-operative) required reoperation for arthrofibrosis. Nine patients, one originally non-operative, had an ipsilateral ACL injury. Seven of these patients required ACL reconstruction. Operative patients who had both an extension and flexion contracture had 2.2 times the odds of having a complication compared to patients who did not have any contractures, this was independent of final ALD.Conclusions:Operative patients demonstrated greater ACL stability than the non-operative group, but had more complications and reoperations, independent from ALD. While ALD measurements of ‘fair’, ‘good’, and ‘poor’ all resulted in some loss of range of motion, this was of minimal clinical significance in patients at final follow up, suggesting that anatomic reduction may not be mandatory.UPLOAD-https://planion-client-files.s3.amazonaws.com/AOSSM/blobs/2f636269-0d58-4854-8d4a-568086ef7844/1/AOSSM_Table_Yen.docx

6LoWPAN in Wireless Sensor Network with IoT in 5G Technology for Network Secure Routing and Energy Efficiency

Today, interconnection and routing protocols must discover the best solution for secure data transformation with a variety of smart devices due to the growing influence of information technology, such as Internet of Things (IoT), in human life. In order to handle routing concerns with regard to new interconnection approaches like the 6LoWPAN protocol, it is required to offer an improved solution. This research propose novel technique in 6LoWPAN network secure routing and energy efficiency (EE) for WSN in IoT application based on 5G technology. Here the energy optimization has been carried out using clustered channel aware least square support vector machine (Cl_CHLSSVM). Then the secure routing has been carried out using fuzzy based Routing Protocol for low-power and Lossy Networks with kernel-particle swarm optimization (Fuz_RPL_KPSO). To serve needs of IoT applications, proposed method is cognizant of both node priorities as well as application priorities. Applications' sending rate allocation is modeled as a constrained optimization issue.Pxperimental analysis is carried out in terms of throughput of 96%, weighted fairness index of 77%, end-to-end delay of 59%, energy consumption of 86%, and buffer dropped packets of 51%.