Fair Decisions Research Articles

EXPRESS: Fairness as a Robust Utilitarianism

This article focuses on the social choice problem in which decisions are based on the utility of multiple stakeholder types. The sum of these utilities – Utilitarian welfare - is one of the most important objectives in solving the social choice problem. While it is the most efficient solution, maximizing Utilitarian welfare may lead to unfair outcomes. However, Encouraging a Utilitarian decision-maker to adopt a fair decision is challenging due to the associated efficiency loss. This article takes a novel perspective by motivating a Utilitarian decision-maker to make fair decisions from an uncertainty-averse standpoint. We study the problem where the proportions of stakeholder types are uncertain and propose a distributionally robust optimization (DRO) model that maximizes the worst-case Utilitarian welfare over an ϕ-divergence-based uncertainty set. We provide three aspects of the relationship between fairness and the uncertain-averse Utilitarian welfare maximization. First, we establish that the worst-case Utilitarian welfare adheres to all five axioms of unfairness-averse cardinal welfare functions with two stakeholder types and satisfies four of these when this number exceeds two. Second, we demonstrate that with the maximal extent of uncertainty aversion, the DRO model identifies the Egalitarian welfare maximizer, which prioritizes fairness. Further, given serveral conventional assumptions, the proposed model selects the Nash welfare maximizer, an objective trade-off between efficiency and fairness, with moderate levels of uncertainty aversion. Lastly, we present numerical studies of two specific instances of the social choice problem – resource allocation and facility location – to show that, as uncertainty aversion increases, our model increasingly emphasizes fairness.

The development of the ideas of “electronic court” and “electronic mediation” in Russian and foreign law

This article focuses on the development and implementation of information technologies in courts of general jurisdiction. It compares the concept of “electronic court” in Russia and abroad and concludes with their different contents. Further, the article also focuses on using the “E-Court” system in Russia and “E-SUD” in Uzbekistan. This article analyzes the main problems hindering the development of mediation in Russia. In the future, mediation may become a key alternative to litigation because it saves time and money, maintains relations between the parties to a dispute, and makes a fair decision based on their sake. The development of mediation directly depends on the improvement of the legislative framework. Based on the identified issues, recommendations that will help to realize the potential of mediation have been proposed. The article aims to analyze the possibilities of developing electronic mediation and offers recommendations on the confidentiality of online mediation.

#2742 Artificial intelligence accurately approximates MGPE dose response relationship in a North American Dialysis Population

Abstract Background and Aims The Anemia Control Model (ACM) is a certified medical device aimed at optimizing the management of CKD-related anemia among patients on dialysis. ACM is based on an artificial neural network (ANN) trained on over 900’000 patients treated in the European Nephrocare Network. Over the years, ACM has offered physicians hundreds of thousands of erythropoietin stimulating agent (ESA) and intravenous iron dosing suggestions and ensured high hemoglobin target achievement rate while minimizing drug use. However, evidence concerning its validity in non-European samples is still limited. Apollo Dial DB is the largest, fully anonymized, collaborative, international database of dialysis patients. The database includes detailed clinical information of more than 500,000 patients over millions of patient-months of observation with an extensive representation over geographies as well as ethnic groups. Given its detailed medical information and size, the database may offer unprecedented opportunities to develop and test equitable and fair decision support systems based on artificial intelligence. In this study we sought to re-train and validate and assess the accuracy of the ACM in an ethnically diverse sample of North American dialysis patients in Apollo Dial DB. Method We constructed the cohort for training and validation of this new ACM version by including a random sample of patients treated in the North American Fresenius Medical Care Dialysis Network. The sample accounted for 20% of the entire North American Section of the Apollo Dial DB dataset. Each patient history has been segmented in a collection of patient-months, which was the unit of analysis. The initial cohort was split in 3 datasets for training (70%), validation (20%) and test (10%). The input variables included laboratory test results, body weight, treatment data, age, sex, and previous ESA and iron prescription. The outcome of interest was the variation in serum hemoglobin concentration over 1 month of follow up. We compared the accuracy of the original ACM based on ANN (baseline model) with a newly trained algorithm based on high-performance light GBM. Results We included 44,220 patients followed by 2,277,276 patient-months overall. The mean age=60.9 ± 13.4, 59.2% were male, 56.4% were Caucasians, 32.2% African American, 3.2% Asians and 8.2% were others. At baseline, mean Hb= 10.9 ± 1.2. The original EMEA version of the Anemia Control Model (ACM) was still accurate in North American patients (mean absolute error, MAE = 0.65 g/dL). The fine-tuned light GBM model achieved a 16% reduction in error rate (MAE = 0.55 g/dL; Fig. 1). No difference in MAE was observed across sex and ethnic groups. Conclusion This study demonstrates the excellent predictive performance of the original ANN-based ACM in the North American dialysis population. Fine tuning by re-training ACM on the dataset further improved the accuracy of the model with substantial equivalence in performance across different demographics, thus ensuring algorithmic fairness. ACM is a promising tool for anemia management in ethnically diverse dialysis population and different healthcare settings.

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.

Fair Machine Guidance to Enhance Fair Decision Making

Human judgment is often subject to bias, leading to unfair decisions. This is particularly problematic when assessments have significant consequences, underscoring the importance of guiding humans towards fairness. Although recent advancements in AI have facilitated decision support, it is not always feasible to employ AI assistance in real-world scenarios. Therefore, this study focuses on developing and evaluating a method to guide humans in making fair judgments. Our experimental results confirmed that our approach effectively promotes fairness in human decision-making.

Systemic barriers preventing farmer engagement in the agricultural climate transition: a qualitative study

AbstractFarmers and the agricultural sector are facing an uncertain future, due to rapidly changing agricultural policy and a nexus of commercial, environmental and biodiversity challenges brought on by anthropogenic climate change. Balancing the interests of the farming community and natural environment is generating organisational and personal conflict, rather than effective agricultural adaption and a focus on the net zero transition. In this paper, we consider how farmers and the wider farming community can participate in engagement and decision-making for the transition, on their own terms, whilst ensuring social justice for fair and equitable decision-making. We discuss findings from our research project, that used questionnaires and follow up semi-structured interviews with farmers and farming groups to reveal systemic issues that prevent farmers engaging with the climate transition to net zero during their everyday practice. We highlight the structural relationships that form these barriers and frustrate farmers when designing and implementing farm interventions to agricultural systems during times of transition. Farmers have highlighted barriers to be associated with policy, (uncertain government strategy, landowner bias in government policy and tenancy restrictions, government bureaucracy—particularly environment schemes), commercial (market forces—downward pressure from supermarkets on price points, availability of labour due to seasonal migration restrictions, increasing cost of labour) and interpersonal (succession—uncertain future of the family farm due to limited income). This frustrates the ability of farmers to address climate change through farming practices and undermines the implementation of innovative engagement practices, such as co-produced interventions, in policy and decision-making settings.

Fair group decisions via non-deterministic proportional consensus

AbstractAre there group decision methods which (i) give everyone, including minorities, an equal share of effective decision power even when voters act strategically, (ii) promote consensus and equality, rather than polarization and inequality, and (iii) do not favour the status quo or rely too much on chance? We describe two non-deterministic group decision methods that meet these criteria, one based on automatic bargaining over lotteries, the other on conditional commitments to approve compromise options. Using theoretical analysis, agent-based simulations and a behavioral experiment, we show that these methods prevent majorities from consistently suppressing minorities, which can happen in deterministic methods, and keeps proponents of the status quo from blocking decisions, as in other consensus-based approaches. Our simulations show that these methods achieve aggregate welfare comparable to common voting methods, while employing chance judiciously, and that the welfare costs of fairness and consensus are small compared to the inequality costs of majoritarianism. In an incentivized experiment with naive participants, we find that a sizable fraction of participants prefers to use a non-deterministic voting method over Plurality Voting to allocate monetary resources. However, this depends critically on their position within the group. Those in the majority show a strong preference for majoritarian voting methods.

THE EXPERT'S OPPORTUNITIES TO PROVIDE ADDITIONAL CONCLUSIONS ON EXISTING AND NEWLY RAISED QUESTIONS IN HIS TESTIMONY

The production of forensic examinations is important for establishing the objective truth in criminal cases. Making a fair decision in each specific case often requires the use of special knowledge possessed by experts. The current criminal procedural legislation regulates in detail the material and procedural aspects of the organization and implementation of forensic examinations, as well as the peculiarities of using the results of their conduct in court proceedings. Forensic experts are often called upon to participate in criminal proceedings. However, despite the developed legislative framework and large-scale law enforcement practice, a number of controversial issues still remain that affect the institution of «additional expert conclusions». The purpose of the presented study is to analyze the expert’s ability to provide additional conclusions on existing and newly raised questions in his testimony. The authors come to the conclusion that the current legislation rightly provides grounds for additional and repeated examinations, which allows in various situations related to both the ambiguity and incompleteness of the submitted expert opinion, and the presence of specific contradictions in it, to resort to appropriate examinations. Attention is focused on the need to appoint additional examinations when new questions and circumstances arise in the case, since their solution and accounting require the use of special knowledge, as well as full-fledged research in individual situations.

The Revitalization of Extraordinary Legal Remedies Cassation for Legal Interests in the Criminal Justice System in Indonesia

The decision of judges are not free from errors or mistakes, and often even shows partiality (not neutral) towards certain parties. Extraordinary legal action: cassation for legal purposes can be filed against all decisions that have obtained permanent legal force from other courts, apart from the Supreme Court. Great. The purpose of this research is to determine the factors that cause the Attorney General to rarely use the extraordinary legal remedy of cassation for legal purposes. To revitalize the extraordinary legal effort of cassation for legal purposes so that it can run effectively in the criminal justice system in Indonesia. The findings from this research are that revitalization can be carried out by providing adequate explanations, especially the phrases "legal interests" and "must not harm interested parties" or even eliminating the provisions of Article 359 paragraph (2) of the Criminal Procedure Code, so that it does not give rise to different interpretations and judges can give fair decisions, without being shackled by the phrase "must not harm interested parties".

A Granular Approach to Optimal and Fair Patient Placement in Hospital Emergency Departments

Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care quality and outcomes. There is evidence that certain groups of patients experience longer LOS based on their gender or race, especially with regard to the part of LOS that is attributable to waiting to be seen by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming (MILP) predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable pieces. We incorporate considerations for fairness and reformulate the MILP formulation into a compact and computationally tractable formulation that can be solved efficiently in real time. To deal with uncertainty, we propose a sampling-based solution, and provide provable guarantees regarding its convergence, stability and sample complexity. The proposed solution increases the throughput of the ED by [Formula: see text] and decreases the average wait time by [Formula: see text] compared to current hospital practice. In addition, the method is near-optimal in terms of throughput, and produces high-quality solutions in terms of average wait time compared to a clairvoyant oracle. Our proposed approach demonstrates desirable properties when it comes to fairness in patient prioritization, illustrating a path for addressing hidden biases in patient ED wait times and hospital operations as a whole. This work was conducted in collaboration with a large US academic medical center. Data from more than 40,000 patient visits were used to shape and evaluate the predictive-prescriptive models. An important practical contribution is translating a complex algorithm’s output into recommendations that can be operationalized in the context of existing processes in the ED. Specifically, we develop an interpretable metamodel that is trained to mimic the predictive-prescriptive algorithm’s decisions and provides a transparent set of rules for patient placement. The method will be used by the hospital to improve patient flow and quality of care as well as to support more fair and consistent bed allocation decisions.

Analisis Mengenai Diskresi dengan Freies Ermessen

This discretion has consequences from the concept of a welfare state law, where discretionary authority is a necessity for the state to implement welfare for society effectively and efficiently. Discretion also acts as a substitute or workaround for weaknesses in regulations, enabling governments to respond and handle unforeseen situations in a manner consistent with general principles of good governance. The aim of this research is to investigate and identify effective monitoring and accountability mechanisms for the use of Freies Ermessen in the legal system in order to increase integrity and fairness in decision making. The research method used in this research is a qualitative approach with descriptive methods and literature studies. A qualitative approach was chosen because it provides the ability to deepen and understand complex phenomena and allows researchers to explore various perspectives and relevant contexts. The research results show that regarding the use of Freies Ermessen in the legal system, there is a need to develop effective monitoring and accountability mechanisms. Transparency in the decision-making process, setting clear standards, and community involvement are key to ensuring that discretion is used with integrity and in accordance with legal principles. In addition, the role of an independent court system in examining decisions taken and strengthening accountability mechanisms for authority holders is essential in preventing abuse of power.

LEGAL IDEAS OF ABAI AND PROBLEMS OF ITS USE IN SOCIETY

The article outlines the need to apply the legal views and ideas of Abai in the development of modern public legal culture, as well as in the protection of human rights, and based on the results of the study, scientific and methodological recommendations are given on the identified problems. The subject of the research work is the development of moral, democratic, human qualities through the ideas of Abai. The article notes that the work of Abay forms the worldview, being, civil position of any person. The article also emphasizes the need to study the works of Abai, which talk about law, law and the rule of law. In the disclosure of the content of the topic, the works of Abai were studied and a comparative analysis of its theoretical foundations for application in the field of law at the present stage was made, and a survey was organized to promote the legal aspects of Abai's works and events of various forms were held, the results of which are analyzed in this article. The results of the research showed the absence of full-fledged, systematic works on this topic. The article discusses the main modern approaches to the study of problems arising from the discussion of Abay's work, as well as the use of traditional and modern methods that affect the change in modern practice of teaching and education. In this regard, recommendations will be given on educating young people in the spirit of justice, patriotism, conscientious work and the development of national and legal culture. To date, it has been determined that in the adoption of high-quality laws, the issuance of fair judicial decisions, as well as the prevention of corruption crimes, the discussion of legal norms, we get a lot from Abay's heritage. For the prevention of offenses in all spheres of society and for the study and application of Abai's legal views, it is necessary to involve specialists in the field of law along with teachers. As a result of the work, it was concluded that it is necessary to develop a public legal culture based on the works of Abai.

Proportional Aggregation of Preferences for Sequential Decision Making

We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. Instead of going with the most popular choice in each round, we aim for proportional representation, using axioms inspired by the multi-winner voting literature. The axioms require that every group of α% of the voters, if it agrees in every round (i.e., approves a common alternative), then those voters must approve at least α% of the decisions. A stronger version of the axioms requires that every group of α% of the voters that agrees in a β fraction of rounds must approve β⋅α% of the decisions. We show that three attractive voting rules satisfy axioms of this style. One of them (Sequential Phragmén) makes its decisions online, and the other two satisfy strengthened versions of the axioms but make decisions semi-online (Method of Equal Shares) or fully offline (Proportional Approval Voting). We present empirical results for these rules based on synthetic data and U.S. political elections. We also run experiments using the moral machine dataset about ethical dilemmas. We train preference models on user responses from different countries and let the models cast votes. We find that aggregating these votes using our rules leads to a more equal utility distribution across demographics than making decisions using a single global preference model.

A Sequentially Fair Mechanism for Multiple Sensitive Attributes

In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to solve this task, which work well in many practical applications. However, the applicability and effectivity of these tools and definitions becomes less straightfoward in the case of multiple sensitive attributes. To tackle this issue, we propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. We accomplish this by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This method also provides a closed-form solution for the optimal, sequentially fair predictor, permitting a clear interpretation of inter-sensitive feature correlations. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness. This extension permits a targeted prioritization of fairness improvements for a specific attribute within a set of sensitive attributes, allowing for a case specific adaptation. A data-driven estimation procedure for the derived solution is developed, and comprehensive numerical experiments are conducted on both synthetic and real datasets. Our empirical findings decisively underscore the practical efficacy of our post-processing approach in fostering fair decision-making.

Long-Term Fair Decision Making through Deep Generative Models

This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we leverage the temporal causal graph and use the 1-Wasserstein distance between the interventional distributions of different demographic groups at a sufficiently large time step as the quantitative metric. Then, we propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model. We formulate the optimization problem as a performative risk minimization and adopt the repeated gradient descent algorithm for learning. The empirical evaluation shows the efficacy of the proposed method using both synthetic and semi-synthetic datasets.

Legal Findings on the Status of Doctors' Employment Relationships in Private Hospitals Based on Supreme Court Decisions

Basically, a written cooperation agreement is important to bind one party to another. In practice, there are still those who do not use a written cooperation agreement in the implementation of cooperation such as the case of a doctor who does not have a cooperation agreement in any form so that if one party terminates cooperation without notice, then there is no basis that can be accounted for. Therefore, the Supreme Court in considering a fair decision decided to make an updated legal discovery regarding the status of employment relations that must be made in writing so that when a dispute arises it can be accounted for before the eyes of the law. It can be concluded that it is important for judges when deciding a case to take into account the laws that apply in society, and every decision must be made based on applicable law and the principle of justice.

Towards fair decision: A novel representation method for debiasing pre-trained models

Pretrained language models (PLMs) are frequently employed in Decision Support Systems (DSSs) due to their strong performance. However, recent studies have revealed that these PLMs can exhibit social biases, leading to unfair decisions that harm vulnerable groups. Sensitive information contained in sentences from training data is the primary source of bias. Previously proposed debiasing methods based on contrastive disentanglement have proven highly effective. In these methods, PLMs can disentangle sensitive information from non-sensitive information in sentence embedding, and then adapts non-sensitive information only for downstream tasks. Such approaches hinge on having good sentence embedding as input. However, recent research found that most non-fine-tuned PLMs such as BERT produce poor sentence embedding. Disentangling based on these embedding will lead to unsatisfactory debiasing results. Taking a finer-grained perspective, we propose PCFR (Prompt and Contrastive-based Fair Representation), a novel disentanglement method integrating prompt and contrastive learning to debias PLMs. We employ prompt learning to represent information as sensitive embedding and subsequently apply contrastive learning to contrast these information embedding rather than the sentence embedding. PCFR encourages similarity among different non-sensitive information embedding and dissimilarity between sensitive and non-sensitive information embedding. We mitigate gender and religion biases in two prominent PLMs, namely BERT and GPT-2. To comprehensively assess debiasing efficacy of PCFR, we employ multiple fairness metrics. Experimental results consistently demonstrate the superior performance of PCFR compared to representative baseline methods. Additionally, when applied to specific downstream decision tasks, PCFR not only shows strong de-biasing capability but also significantly preserves task performance.

Penyelamatan dan Penyelesaian Kredit yang Bermasalah dalam Perjanjian Simpan Pinjam pada Koperasi (Berdasarkan Studi Putusan Nomor 15/Pdt.G/2023/Pn/Mgg)

The case of a simple claim for breach of contract in the Magelang court decision number 15/Pdt.G/2023/PN Mgg was caused by the claim that the peace deed he made was legally flawed and did not have binding legal force. In the process of granting credit, it is in accordance with the 5C credit granting procedure, especially regarding collateral or guarantees, but there is a legal defect in the collateral for mortgage rights whose ownership status is owned by deceased heirs. This research uses normative juridical research with descriptive analysis research methods. Analysis of the Magelang court decision Number 15/Pdt.G/2023/PN Mgg. The results of the research concluded that rescue and settlement of problematic credit was provided after it arose and was resolved in relation to the simple lawsuit decision in the Magelang Court case by filing a simple lawsuit because a simple lawsuit was filed in a case with a material claim value of a maximum of IDR 500 million, namely: breach of contract (default); and/or unlawful acts. In this case, the cooperative filed a lawsuit with the Magelang District Court to obtain a fair decision. Once it is declared rejected in its entirety, the debtor does not compensate for the loss of the lawsuit.

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$54\%$</tex-math></inline-formula> lower than a centralized framework based on deep-Q networks.

Fair and stable matching decision-making with multiple hesitant fuzzy elements

The two-sided matching (TSM) decision-making is an interdisciplinary research field encompassing management science, behavioral science, and computer science, which are widely applied in various industries and everyday life, generating significant economic and social value. However, in the decision-making process of real-world TSM, the complexity of the decision-making problem and environment lead to the preference information provided by the two-sided agents being ambiguous and uncertain. The purpose of this study is to develop a new fair and stable matching methodology to resolve the TSM problem with multiple hesitant fuzzy element (HFE) information. The decision-making process is as follows. First, the TSM problem with four kinds of HFEs is described. To solve this problem, the HFE value of each index is normalized and then is transformed into the closeness degree by using the bidirectional projection technology. Second, based on the closeness degree, the weight of each index is calculated by using the Critic method. Then, the agent satisfaction is obtained by aggregating the closeness and the weights. Next, a fair and stable TSM model to maximizing agent satisfactions under the constraints of one-to-one stable matching is constructed. The best TSM scheme can be obtained by solving the TSM model. Finally, an example of logistics technology cooperation is provided to verify the effectiveness and feasibility of the presented model and methodology. The proposed methodology develops a novel fuzzy information presentation tool and constructs a TSM model considering the fairness and stability, which is of great significance to investigate the TSM decision-making and the resolution of real-life TSM problems under the uncertain and fuzzy environments. One future research direction is to consider multiple psychological and behavioral factors of two-sided agents in TSM problems.