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Relevant bibliographies by topics / Fairness constraints

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Journal articles

Academic literature on the topic 'Fairness constraints'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Fairness constraints"

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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2

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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3

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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4

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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5

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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6

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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8

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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10

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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