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Artykuły w czasopismach na temat „Fairness constraints”

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Data publikacji: 6 września 2023

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1

Detassis, Fabrizio, Michele Lombardi, and Michela Milano. "Teaching the Old Dog New Tricks: Supervised Learning with Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 3742–49. http://dx.doi.org/10.1609/aaai.v35i5.16491.

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Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver: this enables taking advantage of decades of research on constrained o

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Ben-Porat, Omer, Fedor Sandomirskiy, and Moshe Tennenholtz. "Protecting the Protected Group: Circumventing Harmful Fairness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5176–84. http://dx.doi.org/10.1609/aaai.v35i6.16654.

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The recent literature on fair Machine Learning manifests that the choice of fairness constraints must be driven by the utilities of the population. However, virtually all previous work makes the unrealistic assumption that the exact underlying utilities of the population (representing private tastes of individuals) are known to the regulator that imposes the fairness constraint. In this paper we initiate the discussion of the \emph{mismatch}, the unavoidable difference between the underlying utilities of the population and the utilities assumed by the regulator. We demonstrate that the mismatc

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Li, Fengjiao, Jia Liu, and Bo Ji. "Combinatorial Sleeping Bandits With Fairness Constraints." IEEE Transactions on Network Science and Engineering 7, no. 3 (2020): 1799–813. http://dx.doi.org/10.1109/tnse.2019.2954310.

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Pi, Jiancai. "Fairness compatibility constraints and collective actions." Frontiers of Economics in China 2, no. 4 (2007): 644–52. http://dx.doi.org/10.1007/s11459-007-0033-x.

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Vukadinović, Vladimir, and Gunnar Karlsson. "Multicast scheduling with resource fairness constraints." Wireless Networks 15, no. 5 (2007): 571–83. http://dx.doi.org/10.1007/s11276-007-0085-y.

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Wang, Xiao Fei, Xi Zhang, Yue Bing Chen, Lei Zhang, and Chao Jing Tang. "Spectrum Assignment Algorithm Based on Clonal Selection in Cognitive Radio Networks." Advanced Materials Research 457-458 (January 2012): 931–39. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.931.

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An improved-immune-clonal-selection based spectrum assignment algorithm (IICSA) in cognitive radio networks is proposed, combing graph theory and immune optimization. It uses constraint satisfaction operation to make encoded antibody population satisfy constraints, and realizes the global optimization. The random-constraint satisfaction operator and fair-constraint satisfaction operator are designed to guarantee efficiency and fairness, respectively. Simulations are performed for performance comparison between the IICSA and the color-sensitive graph coloring algorithm. The results indicate tha

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7

Piron, Robert, and Luis Fernandez. "Are fairness constraints on profit-seeking important?" Journal of Economic Psychology 16, no. 1 (1995): 73–96. http://dx.doi.org/10.1016/0167-4870(94)00037-b.

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Zheng, Jiping, Yuan Ma, Wei Ma, Yanhao Wang, and Xiaoyang Wang. "Happiness maximizing sets under group fairness constraints." Proceedings of the VLDB Endowment 16, no. 2 (2022): 291–303. http://dx.doi.org/10.14778/3565816.3565830.

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Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and admission, it is crucial to ensure that the HMS can fairly represent different groups of candidates without bias and discrimination. However, although the HMS problem was extensively studied in the database community, existing algorithms do

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9

Heaton, Stephen. "FINALITY OR FAIRNESS?" Cambridge Law Journal 73, no. 3 (2014): 477–80. http://dx.doi.org/10.1017/s0008197314000919.

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THE finality of proceedings, resource constraints, a presumption of guilt, and the existence of the Criminal Cases Review Commission (“CCRC”) all combine to outweigh the principle of fairness for a convicted individual. Such was the stark conclusion of the Supreme Court in dismissing Kevin Nunn's application to force prosecution authorities to grant access to material which he believed would help him get his conviction quashed: R. (Nunn) v Chief Constable of Suffolk Constabulary [2014] UKSC 37, [2014] 3 W.L.R. 77.

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Tan, Xianghua, Shasha Wang, Weili Zeng, and Zhibin Quan. "A Collaborative Optimization Method of Flight Slots Considering Fairness Among Airports." Mathematical Problems in Engineering 2022 (September 10, 2022): 1–18. http://dx.doi.org/10.1155/2022/1418911.

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With the rapid development of civil aviation transportation, an increasing number of airport groups are formed. However, the existing literature on fairness mostly focuses on the fairness among airlines. There is no research on the realization of scheduling fairness among airports with overlapping resources in the airport group. The goal of this paper is to comprehensively consider efficiency and fairness in slot scheduling, where fairness should include both interairline and interairport fairness. Subsequently, we developed a collaborative optimization model for airport group that takes into

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11

Goto, Masahiro, Fuhito Kojima, Ryoji Kurata, Akihisa Tamura, and Makoto Yokoo. "Designing Matching Mechanisms under General Distributional Constraints." American Economic Journal: Microeconomics 9, no. 2 (2017): 226–62. http://dx.doi.org/10.1257/mic.20160124.

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To handle various applications, we study matching under constraints. The only requirement on the constraints is heredity; given a feasible matching, any matching with fewer students at each school is also feasible. Heredity subsumes existing constraints such as regional maximum quotas and diversity constraints. With constraints, there may not exist a matching that satisfies fairness and nonwastefulness (i.e., stability). We demonstrate our new mechanism, the Adaptive Deferred Acceptance mechanism (ADA), satisfies strategy-proofness for students, nonwastefulness, and a weaker fairness property.

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12

Rezaei, Ashkan, Rizal Fathony, Omid Memarrast, and Brian Ziebart. "Fairness for Robust Log Loss Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5511–18. http://dx.doi.org/10.1609/aaai.v34i04.6002.

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Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametri

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13

Schoeffer, Jakob, Alexander Ritchie, Keziah Naggita, Faidra Monachou, Jessica Finocchiaro, and Marc Juarez. "Online Platforms and the Fair Exposure Problem under Homophily." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11899–908. http://dx.doi.org/10.1609/aaai.v37i10.26404.

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In the wake of increasing political extremism, online platforms have been criticized for contributing to polarization. One line of criticism has focused on echo chambers and the recommended content served to users by these platforms. In this work, we introduce the fair exposure problem: given limited intervention power of the platform, the goal is to enforce balance in the spread of content (e.g., news articles) among two groups of users through constraints similar to those imposed by the Fairness Doctrine in the United States in the past. Groups are characterized by different affiliations (e.

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14

Zhai, Zhou, Lei Luo, Heng Huang, and Bin Gu. "Faster Fair Machine via Transferring Fairness Constraints to Virtual Samples." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11918–25. http://dx.doi.org/10.1609/aaai.v37i10.26406.

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Fair classification is an emerging and important research topic in machine learning community. Existing methods usually formulate the fairness metrics as additional inequality constraints, and then embed them into the original objective. This makes fair classification problems unable to be effectively tackled by some solvers specific to unconstrained optimization. Although many new tailored algorithms have been designed to attempt to overcome this limitation, they often increase additional computation burden and cannot cope with all types of fairness metrics. To address these challenging issue

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15

Motchoulski, Alexander, and Phil Smolenski. "Principles of Collective Choice and Constraints of Fairness." Journal of Philosophy 116, no. 12 (2019): 678–90. http://dx.doi.org/10.5840/jphil20191161243.

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In “The Difference Principle Would Not Be Chosen behind the Veil of Ignorance,” Johan E. Gustafsson argues that the parties in the Original Position (OP) would not choose the Difference Principle to regulate their society’s basic structure. In reply to this internal critique, we provide two arguments. First, his choice models do not serve as a counterexample to the choice of the difference principle, as the models must assume that individual rationality scales to collective contexts in a way that begs the question in favor of utilitarianism. Second, the choice models he develops are incompatib

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16

Echenique, Federico, Antonio Miralles, and Jun Zhang. "Fairness and efficiency for allocations with participation constraints." Journal of Economic Theory 195 (July 2021): 105274. http://dx.doi.org/10.1016/j.jet.2021.105274.

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17

Balzano, Walter, Marco Lapegna, Silvia Stranieri, and Fabio Vitale. "Competitive-blockchain-based parking system with fairness constraints." Soft Computing 26, no. 9 (2022): 4151–62. http://dx.doi.org/10.1007/s00500-022-06888-1.

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18

Kryger, Esben Masotti. "Pension Fund Design under Long-term Fairness Constraints." Geneva Risk and Insurance Review 35, no. 2 (2010): 130–59. http://dx.doi.org/10.1057/grir.2009.10.

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19

Roy, Rita, and Giduturi Appa Rao. "A Framework for an Efficient Recommendation System Using Time and Fairness Constraint Based Web Usage Mining Technique." Ingénierie des systèmes d information 27, no. 3 (2022): 425–31. http://dx.doi.org/10.18280/isi.270308.

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Users prefer to use various websites like Facebook, Gmail, and YouTube. We can make the system predict what pages we expect in the future and give the users what they have requested. Based on the data gathered and analyzed, we can predict the user's future navigation patterns in response to the user's requests. In order to track down users’ navigational sessions, the web access logs created at a specific website are processed. Grouping the user session data is then done into clusters, where inter-cluster similarities are minimized, although the intra-cluster similarities are maximised. Recent

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20

Sun, Rui, Fengwei Zhou, Zhenhua Dong, et al. "Fair-CDA: Continuous and Directional Augmentation for Group Fairness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9918–26. http://dx.doi.org/10.1609/aaai.v37i8.26183.

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In this work, we propose Fair-CDA, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the features highly related to the sensitive attributes. Then we show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable. To alleviate the accuracy degradation caused by fairness constraints, we further introduce a calibrated model to impute

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21

Li, Yunyi, Maria De-Arteaga, and Maytal Saar-Tsechansky. "When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 10, no. 1 (2022): 133–46. http://dx.doi.org/10.1609/hcomp.v10i1.21994.

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An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of these two categories, methods that propose active learning under fairness constraints have been developed. However, proposed bias mitigation strategies typically overlook the bias presented in the observed labels. In this work, we study fair

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22

Islam, Md Mouinul, Dong Wei, Baruch Schieber, and Senjuti Basu Roy. "Satisfying complex top- k fairness constraints by preference substitutions." Proceedings of the VLDB Endowment 16, no. 2 (2022): 317–29. http://dx.doi.org/10.14778/3565816.3565832.

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Given m users (voters), where each user casts her preference for a single item (candidate) over n items (candidates) as a ballot, the preference aggregation problem returns k items (candidates) that have the k highest number of preferences (votes). Our work studies this problem considering complex fairness constraints that have to be satisfied via proportionate representations of different values of the group protected attribute(s) in the top- k results. Precisely, we study the margin finding problem under single ballot substitutions , where a single substitution amounts to removing a vote fro

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23

Yin, Yingqi, Fengye Hu, Ling Cen, Yu Du, and Lu Wang. "Balancing Long Lifetime and Satisfying Fairness in WBAN Using a Constrained Markov Decision Process." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/657854.

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As an important part of the Internet of Things (IOT) and the special case of device-to-device (D2D) communication, wireless body area network (WBAN) gradually becomes the focus of attention. Since WBAN is a body-centered network, the energy of sensor nodes is strictly restrained since they are supplied by battery with limited power. In each data collection, only one sensor node is scheduled to transmit its measurements directly to the access point (AP) through the fading channel. We formulate the problem of dynamically choosing which sensor should communicate with the AP to maximize network li

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24

Liao, Yiqiao, and Parinaz Naghizadeh. "Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8764–72. http://dx.doi.org/10.1609/aaai.v37i7.26054.

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Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in the measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demogra

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25

Suksompong, Warut. "Constraints in fair division." ACM SIGecom Exchanges 19, no. 2 (2021): 46–61. http://dx.doi.org/10.1145/3505156.3505162.

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The fair allocation of resources to interested agents is a fundamental problem in society. While the majority of the fair division literature assumes that all allocations are feasible, in practice there are often constraints on the allocation that can be chosen. In this survey, we discuss fairness guarantees for both divisible (cake cutting) and indivisible resources under several common types of constraints, including connectivity, cardinality, matroid, geometric, separation, budget, and conflict constraints. We also outline a number of open questions and directions.

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26

Hu, Yaowei, and Lu Zhang. "Achieving Long-Term Fairness in Sequential Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9549–57. http://dx.doi.org/10.1609/aaai.v36i9.21188.

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In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimiza

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27

Becker, Ruben, Gianlorenzo D'Angelo, and Sajjad Ghobadi. "On the Cost of Demographic Parity in Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14110–18. http://dx.doi.org/10.1609/aaai.v37i12.26651.

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Modeling and shaping how information spreads through a network is a major research topic in network analysis. While initially the focus has been mostly on efficiency, recently fairness criteria have been taken into account in this setting. Most work has focused on the maximin criteria however, and thus still different groups can receive very different shares of information. In this work we propose to consider fairness as a notion to be guaranteed by an algorithm rather than as a criterion to be maximized. To this end, we propose three optimization problems that aim at maximizing the overall sp

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28

Busard, Simon, Charles Pecheur, Hongyang Qu, and Franco Raimondi. "Reasoning about Strategies under Partial Observability and Fairness Constraints." Electronic Proceedings in Theoretical Computer Science 112 (March 1, 2013): 71–79. http://dx.doi.org/10.4204/eptcs.112.12.

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Li, G., and H. Liu. "Resource Allocation for OFDMA Relay Networks With Fairness Constraints." IEEE Journal on Selected Areas in Communications 24, no. 11 (2006): 2061–69. http://dx.doi.org/10.1109/jsac.2006.881627.

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Zhang, Honghai, Qiqian Zhang, and Lei Yang. "A User Equilibrium Assignment Flow Model for Multiairport Open Network System." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/631428.

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To reduce flight delays and promote fairness in air traffic management, we study the imbalance problem between supply and demand in airport network system from the view of both the system and the users. First, we establish an open multiairport oriented network flow system with the correlation between the arrival and departure in capacity-constrained airports, as well as the relevance between multiairports united flights. Then, based on the efficiency rationing principle, we propose an optimization model to reassign flow with user equilibrium constraints. These constraints include Gini coeffici

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Biswas, Arpita, and Siddharth Barman. "Matroid Constrained Fair Allocation Problem." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9921–22. http://dx.doi.org/10.1609/aaai.v33i01.33019921.

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We consider the problem of allocating a set of indivisible goods among a group of homogeneous agents under matroid constraints and additive valuations, in a fair manner. We propose a novel algorithm that computes a fair allocation for instances with additive and identical valuations, even under matroid constraints. Our result provides a computational anchor to the existential result of the fairness notion, called EF1 (envy-free up to one good) by Biswas and Barman in this setting. We further provide examples to show that the fairness notions stronger than EF1 does not always exist in this sett

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Ma, Wen Min, Hai Jun Zhang, Xiang Ming Wen, Wei Zheng, and Zhao Ming Lu. "A Novel QoS Guaranteed Cross-Layer Scheduling Scheme for Downlink Multiuser OFDM Systems." Applied Mechanics and Materials 182-183 (June 2012): 1352–57. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1352.

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To provide quality-of-service (QoS) differentiation and guarantee user fairness, efficient power and spectrum utilization for the downlink multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, a novel QoS guaranteed cross-layer (QGCL) scheduling scheme is proposed in this paper. The scheme formulates the scheduling into an optimization problem of overall system utility under the system constraints. Moreover, we propose a simple and efficient binary constrained particle swarm optimization (PSO) to solve the scheduling more effectively. Comparing with the classical methods, sim

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Zhang, Xueru, Mohammad Mahdi Khalili, and Mingyan Liu. "Long-Term Impacts of Fair Machine Learning." Ergonomics in Design: The Quarterly of Human Factors Applications 28, no. 3 (2019): 7–11. http://dx.doi.org/10.1177/1064804619884160.

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Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and ill

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Lee, Joshua, Yuheng Bu, Prasanna Sattigeri, et al. "A Maximal Correlation Framework for Fair Machine Learning." Entropy 24, no. 4 (2022): 461. http://dx.doi.org/10.3390/e24040461.

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As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and is shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables that are more computationally efficient than exist

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Nguyen, Bich-Ngan T., Phuong N. H. Pham, Van-Vang Le, and Václav Snášel. "Influence Maximization under Fairness Budget Distribution in Online Social Networks." Mathematics 10, no. 22 (2022): 4185. http://dx.doi.org/10.3390/math10224185.

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In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community’s elements is selected into a seed set hol

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Li, Xuran, Peng Wu, and Jing Su. "Accurate Fairness: Improving Individual Fairness without Trading Accuracy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14312–20. http://dx.doi.org/10.1609/aaai.v37i12.26674.

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Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness. We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. Informally, it requires the treatments of an individual and the individual's similar counterparts to conform to a uniform target, i.e., the ground truth of the individual. We prove that accurate fairness also implies typical gr

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37

Wang, Depei, Lianglun Cheng, and Tao Wang. "Fairness-aware genetic-algorithm-based few-shot classification." Mathematical Biosciences and Engineering 20, no. 2 (2022): 3624–37. http://dx.doi.org/10.3934/mbe.2023169.

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<abstract><p>Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in our daily lives; however, it has been shown that biased data can cause unfairness in decision-making. In light of this, computational techniques are needed to limit the inequities in algorithmic decision-making. In this letter, we present a framework to join fair feature selection and fair meta-learning to do few-shot classification, which contains three parts: (1) a pre-processing component acts as an intermediate bridge between fair genetic algorithm (FairGA) and fair few-sh

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Steinmann, Sarina, and Ralph Winkler. "Sharing a River with Downstream Externalities." Games 10, no. 2 (2019): 23. http://dx.doi.org/10.3390/g10020023.

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We consider the problem of efficient emission abatement in a multi polluter setting, where agents are located along a river in which net emissions accumulate and induce negative externalities to downstream riparians. Assuming a cooperative transferable utility game, we seek welfare distributions that satisfy all agents’ participation constraints and, in addition, a fairness constraint implying that no coalition of agents should be better off than it were if all non-members of the coalition would not pollute the river at all. We show that the downstream incremental distribution, as introduced b

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Van Bulck, David, and Dries Goossens. "Handling fairness issues in time-relaxed tournaments with availability constraints." Computers & Operations Research 115 (March 2020): 104856. http://dx.doi.org/10.1016/j.cor.2019.104856.

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Schulz, Andreas S., and Nicolás E. Stier-Moses. "Efficiency and fairness of system-optimal routing with user constraints." Networks 48, no. 4 (2006): 223–34. http://dx.doi.org/10.1002/net.20133.

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Kügelgen, Julius von, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Schölkopf. "On the Fairness of Causal Algorithmic Recourse." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9584–94. http://dx.doi.org/10.1609/aaai.v36i9.21192.

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Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individuals to remedy an unfavourable classification. We propose two new fair-ness criteria at the group and individual level, which—unlike prior work on equalising the average group-wise distance from the decision boundary—explicitly account for causal relationships between features, thereby capturing downstream effects of recourse actions performed in the physical world. We explore how our criteria relate to others, such as

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Kaleta, Mariusz. "Price of Fairness on Networked Auctions." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/860747.

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We consider an auction design problem under network flow constraints. We focus on pricing mechanisms that provide fair solutions, where fairness is defined in absolute and relative terms. The absolute fairness is equivalent to “no individual losses” assumption. The relative fairness can be verbalized as follows: no agent can be treated worse than any other in similar circumstances. Ensuring the fairness conditions makes only part of the social welfare available in the auction to be distributed on pure market rules. The rest of welfare must be distributed without market rules and constitutes th

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Hao, Jingjing, Xinquan Liu, Xiaojing Shen, and Nana Feng. "Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints." Discrete Dynamics in Nature and Society 2019 (March 12, 2019): 1–10. http://dx.doi.org/10.1155/2019/2930502.

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In this paper, the bilevel programming model of the public transport network considering factors such as the per capita occupancy area and travel cost of different groups was established, to alleviate the urban transportation equity and optimize the urban public transport network under fairness constraints. The upper layer minimized the travel cost deprivation coefficient and the road area Gini coefficient as the objective function, to solve the optimization scheme of public transport network considering fairness constraints; the lower layer was a stochastic equilibrium traffic assignment mode

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Pinzón, Carlos, Catuscia Palamidessi, Pablo Piantanida, and Frank Valencia. "On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7993–8000. http://dx.doi.org/10.1609/aaai.v36i7.20770.

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One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed the notion of equality of opportunity (EO), which is compatible with maximal accuracy when the target label is deterministic with respect to the input features. In the probabilistic case, however, the issue is more complicated: It has been shown that under differential privacy constraints, there are data sources for which EO can only be achieved at the total detriment of accuracy, in the sense that a classifier

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Alechina, Natasha, Wiebe van der Hoek, and Brian Logan. "Fair decomposition of group obligations." Journal of Logic and Computation 27, no. 7 (2017): 2043–62. http://dx.doi.org/10.1093/logcom/exx012.

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Abstract We consider the problem of decomposing a group norm into a set of individual obligations for the agents comprising the group, such that if the individual obligations are fulfilled, the group obligation is fulfilled. Such an assignment of tasks to agents is often subject to additional social or organizational norms that specify permissible ways in which tasks can be assigned. An important role of social norms is that they can be used to impose ‘fairness constraints’, which seek to distribute individual responsibility for discharging the group norm in a ‘fair’ or ‘equitable’ way. We pro

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Choi, YooJung, Golnoosh Farnadi, Behrouz Babaki, and Guy Van den Broeck. "Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10077–84. http://dx.doi.org/10.1609/aaai.v34i06.6565.

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As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods often assume a fixed set of observable features to define individuals, but lack a discussion of certain features not being observed at test time. In this paper, we study fairness of naive Bayes classifiers, which allow partial observations. In particular, we introduce the notion of a discrimination pattern, which refers to an individual receiving different classifications depending on whether some sensitive attributes

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Dodevska, Zorica, Sandro Radovanović, Andrija Petrović, and Boris Delibašić. "When Fairness Meets Consistency in AHP Pairwise Comparisons." Mathematics 11, no. 3 (2023): 604. http://dx.doi.org/10.3390/math11030604.

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We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an accept

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Persico, Nicola. "Racial Profiling, Fairness, and Effectiveness of Policing." American Economic Review 92, no. 5 (2002): 1472–97. http://dx.doi.org/10.1257/000282802762024593.

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Citizens of two groups may engage in crime, depending on their legal earning opportunities and on the probability of being audited. Police audit citizens. Police behavior is fair if both groups are policed with the same intensity. We provide exact conditions under which forcing the police to behave more fairly reduces the total amount of crime. These conditions are expressed as constraints on the quantile-quantile plot of the distributions of legal earning opportunities in the two groups. We also investigate the definition of fairness when the cost of being searched reflects the stigma of bein

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Zhao, Cuiru, Youming Li, Bin Chen, Zhao Wang, and Jiongtao Wang. "Resource Allocation for OFDMA-MIMO Relay Systems with Proportional Fairness Constraints." Communications and Network 05, no. 03 (2013): 303–7. http://dx.doi.org/10.4236/cn.2013.53b2056.

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Collins, Brian J., and Fujun Lai. "Examining Affective Constraints of Fairness on OCB: A 3-way Interaction." Academy of Management Proceedings 2013, no. 1 (2013): 13004. http://dx.doi.org/10.5465/ambpp.2013.13004abstract.

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