Relevant bibliographies by topics / Fairness

Academic literature on the topic 'Fairness'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Fairness"

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Franck, Thomas M. "Fairness in Fairness Discourse." Proceedings of the ASIL Annual Meeting 95 (2001): 167–72. http://dx.doi.org/10.1017/s027250370005686x.

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Park, Youngjun, and Sunyoung Kim. "A Study on Public Perception of Fairness in Arts Support Project: Focused on the Case of Arts Council Korea Grants for Art Project." Korean Society of Culture and Convergence 45, no. 3 (March 30, 2023): 275–90. http://dx.doi.org/10.33645/cnc.2023.03.45.03.275.

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In Jun 2021 Moon Joon-yong, a media artist and the son of president Moon Jae-In received 69million won from the Arts Council Korea. This caused a social debate about the fairness of art support project. The research of the fairness perception of artists has been conducted. However the public perception of fairness has been insufficiently analyzed. Therefore, this study tries to analyze the public perception of fairnesss thereby finding solutions to social conflicts. A mixed research method was adopted for this research. The results of this study is the perception of fairness can be changed according to the view on various goals of art support. It is expected that this study will contribut to make the formation of desirable public art support policy.
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Lippmann, Ralf. "Fairness." Forum Kinder- und Jugendsport 2, no. 2 (November 5, 2021): 93–94. http://dx.doi.org/10.1007/s43594-021-00050-9.

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Schmid, Bernd. "Fairness." Konfliktdynamik 4, no. 3 (2015): 255. http://dx.doi.org/10.5771/2193-0147-2015-3-255.

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Mortenson, Lee E. "Fairness." Oncology Issues 9, no. 3 (May 1994): 2. http://dx.doi.org/10.1080/10463356.1994.11904465.

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Hooker, Brad. "Fairness." Ethical Theory and Moral Practice 8, no. 4 (August 2005): 329–52. http://dx.doi.org/10.1007/s10677-005-8836-2.

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Bonald, T., L. Massoulié, A. Proutière, and J. Virtamo. "A queueing analysis of max-min fairness, proportional fairness and balanced fairness." Queueing Systems 53, no. 1-2 (June 2006): 65–84. http://dx.doi.org/10.1007/s11134-006-7587-7.

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Karaata, M. H. "Self-stabilizing strong fairness under weak fairness." IEEE Transactions on Parallel and Distributed Systems 12, no. 4 (April 2001): 337–45. http://dx.doi.org/10.1109/71.920585.

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Estornell, Andrew, Sanmay Das, Brendan Juba, and Yevgeniy Vorobeychik. "Popularizing Fairness: Group Fairness and Individual Welfare." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7485–93. http://dx.doi.org/10.1609/aaai.v37i6.25910.

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Group-fair learning methods typically seek to ensure that some measure of prediction efficacy for (often historically) disadvantaged minority groups is comparable to that for the majority of the population. When a principal seeks to adopt a group-fair approach to replace another, the principal may face opposition from those who feel they may be harmed by the switch, and this, in turn, may deter adoption. We propose that a potential mitigation to this concern is to ensure that a group-fair model is also popular, in the sense that, for a majority of the target population, it yields a preferred distribution over outcomes compared with the conventional model. In this paper, we show that state of the art fair learning approaches are often unpopular in this sense. We propose several efficient algorithms for postprocessing an existing group-fair learning scheme to improve its popularity while retaining fairness. Through extensive experiments, we demonstrate that the proposed postprocessing approaches are highly effective in practice.
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Murniati, Monika Palupi, Ranto Sihombing, and Clara Susilowati. "EFEK PENGUKURAN KEUANGAN DAN NON KEUANGAN TERHADAP KINERJA: PANDANGAN TEORI PENETAPAN TUJUAN." Jurnal Organisasi dan Manajemen 12, no. 1 (March 26, 2016): 13–25. http://dx.doi.org/10.33830/jom.v12i1.22.2016.

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The aim of this study tested the effects of the perception of fairness measure for assessing performance against the performance of the mediation of impact, competence and self-determination as an aspect of empowerment. This research uses experimental studies as a method of data collection by providing a variable size treatmen on performance measurement with the difficulty level objectives and the level of difficulty of the task. Experimental design used 2 x 2 x 2 between subjects. Results from this study showed differences in the perception of fairness subordinate to measures of performance. But the interaction between the size of the performance measurement with goal difficulty and task difficulty did not provide evidence of a significant difference in perceptions of fairness of the performance measure. The mediating effect of impact, competence and self-determination with the perception fairness subordinate to the performance provides empirical evidence that perceptions of fairness subordinate to measure performance assessment can form subordinate beliefs are described by impact, competence and self-determination and enhance the performance of subordinate. Tujuan riset ini adalah untuk menguji efek persepsi fairness ukuran penilain kinerja terhadap kinerja dengan mediasi impact, competence dan self determination sebagai aspek empowerment. Riset ini menggunakan studi eksperimen sebagai metoda pengumpulan data dengan memberikan treatment pada variabel ukuran penilaian kinerja dengan tingkat kesulitan tujuan dan tingkat kesulitan tugas. Desain eksperimen yang digunakan adalah 2 x 2 x 2 antar subjek. Hasil dari riset ini menunjukkan adanya perbedaan persepsi fairness subordinat terhadap ukuran kinerja. Tetapi interaksi antara ukuran penilaian kinerja dengan kesulitan tujuan dan kesulitan tugas tidak memberikan bukti adanya perbedaan signifikan persepsi fairness terhadap ukuran penilaian kinerja. Efek mediasi impact, competence dan self determination dengan persepsi fairnessi subordinat terhadap kinerja memberikan bukti empiris bahwa persepsi fairness subordinat terhadap ukuran penilaian kinerja dapat membentuk keyakinan subordinat yang dijelaskan oleh impact, competence dan self determination dan meningkatkan kinerja subordinat.
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Dissertations / Theses on the topic "Fairness"

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Ahmad, Suhail. "Fairness in prison." Thesis, University of Cambridge, 1996. https://www.repository.cam.ac.uk/handle/1810/252190.

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Westhoff, André Oliver. "Die Fairness Opinion /." Düsseldorf : IDW-Verl, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=015441163&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Wiederkehr, René. "Fairness als Verfassungsgrundsatz." Bern Stämpfli, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2846060&prov=M&dok_var=1&dok_ext=htm.

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Tosi, Justin R. "Legitimacy and Fairness." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/579109.

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The essays included in this dissertation develop a fair play account of state legitimacy. I argue for a modest revision to the traditional analysis of legitimacy. I then defend the principle of fair play against common objections. Next, I argue that the principle of fair play is capable of generating all the rights included in the new analysis of legitimacy defended earlier. Finally, I argue that the principle of fair play grounds the legitimacy of existing reasonably just states.
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Denda, Robert. "Fairness in computer networks /." Aachen : Shaker, 2004. http://www.loc.gov/catdir/toc/fy0715/2007464042.html.

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Asokan, N. "Fairness in electronic commerce." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ32811.pdf.

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Hosein, Adam (Adam Omar). "The significance of fairness/." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55180.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 104-107).
This dissertation is about fairness and the role it plays in political and personal morality. Specifically, I investigate when it is appropriate to rely on considerations of fairness to draw substantive conclusions about what we should do. In Chapter 1 ("Numbers, Fairness and Beneficence") I discuss the "numbers problem," the problem of explaining why you should save more people rather than fewer when forced to choose. Existing non-consequentialist approaches to the problem appeal to fairness to explain why. I argue that this is a mistake and that we can give a more satisfying answer by appealing to requirements of beneficence or generosity. In Chapter 2 ("Fairness, Distributive Justice and Global Justice") I discuss justice in the distribution of resources, both within states and across different states. On one influential view, it is always unjust for one person to have less than another through no fault of her own. State borders, on this account, have no importance in determining which distributions are just. I show that an alternative approach is needed. I argue that distributions of wealth are only unjust in so far as they issue from unfair treatment. It follows that not all inequalities in the distribution of goods are unjust. I use these results to explain how state borders do play a role in determining which inequalities are unjust, since some of these inequalities issue from unfair treatment of citizens by the state. In Chapter 3 ("Contractualism, Politics and Morality") I discuss Rawls' contractualist theory of social justice and Scanlon's extension of it to provide a theory of "rightness", or morality more generally.
(cont.) I argue that while there is some justification for adopting a contractualist theory of social justice, this justification does not support a contractualist theory of rightness. This is because social justice is centrally a matter of cooperative fairness whereas rightness is not.
by Adam Hosein.
Ph.D.
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Hotta, Miho Carleton University Dissertation Psychology. "Fairness of adjudicated allocations." Ottawa, 1992.

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Oettle, Dominik. "Fairness in epidemischen Ereignisverteilungsverfahren." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2007. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-34452.

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Smith, Scott J. "The Relationship Between Perceived Personal Fairness, Social Fairness, Hotel Cancellation Policies and Consumer Patronage." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5510.

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The objective of the study was to examine the relationships between the concepts of personal fairness and social fairness and hotel cancellation policies. These relationships will be explored using the framework of Prospect Theory in terms of consumer patronage (willingness-to-purchase and word-of-mouth). This study includes a brief history of the development of the lodging industry in the United States from inns and taverns to the modern hotel industry that is a critical sector of the hospitality and tourism economy. Current statistics are provided regarding the U.S. and Central Florida hotel industry in order to provide both a national and local economic perspective. The study also provides relevant statistics regarding U.S. domestic traveller information. The included literature review consists of concepts of mental accounting theory, economic utility theory, prospect theory, personal fairness, social fairness, and consumer patronage. The study also discusses how the lodging industry is unique in its implementation of reservation cancellation policies when compared against other industries. Research regarding merchandise return policies is also discussed here. The study was designed to investigate three separate components of both personal and social fairness. The first component investigated the effects of hotel rate price increases and discounts on personal fairness when compared against an existing reference price. The second component studied the perceptions of social fairness on three established hotel cancellation policies. The third component introduces a treatment of distributive and procedural fairness violations as a moderator to observe the effects on consumer patronage for the same three hotel cancellation policies. The data were collected from 415 hotel guests staying in Central Florida hotels near the Orlando international airport using an experimental method which provided different written scenarios regarding hotel pricing and three different hotel cancellation policies. The data was then analyzed using Analysis of Variance (ANOVA) and Tukey's Post Hoc test to provide results that allowed the comparison of effects on each in terms of consumer patronage. The study results indicated that that price increases against established reference prices had a significant negative effect on consumer patronage whereas discounts of the same magnitude had a significant effect only in the middle range. Included smaller and large discounts did not have a significant effect on consumer patronage outside of the middle range. The study results also indicated that there was significant difference in consumer patronage between an Open cancellation policy and a 48 Hour Cancellation Policy. There is a significant difference in consumer patronage when a No Refund policy is compared against both the Open Cancellation Policy and the 48 Hour Cancellation Policy. The study results also show that a violation of either Distributive Fairness or Procedural Fairness has a significant negative effect on consumer patronage for both an Open Cancellation policy and 48 Hour Cancellation Policy. However, when Distributive Fairness or Procedural Fairness violations are introduced as a moderator, there is no significant effect on a No Refund Cancellation Policy. The study and its ensuing results are of importance to the academic community in that it provides additional scholarly support to both Prospect Theory and the theory of mental accounting and the roles that each plays in consumer behavior. From an industry practitioner perspective, the current results provide insight into hotel consumer's attitudes regarding rate increases/ discounts and the implementation of the three different hotel cancelation policies. The results can be utilized to provide justification and guidance in altering or establishing hotel cancellation policies that hotel consumers consider to be fair.
ID: 031001325; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: H. G. Parsa.; Title from PDF title page (viewed April 3, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references (p. 200-208).
Ph.D.
Doctorate
Education and Human Performance
Education; Hospitality Education
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Books on the topic "Fairness"

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Francez, Nissim. Fairness. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6.

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Mount, Ferdinand. Fairness. New York: Carroll & Graf Publishers, 2001.

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Pryor, Kimberley Jane. Fairness. New York: Marshall Cavendish Benchmark, 2010.

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Nissim, Francez. Fairness. New York: Springer-Verlag, 1986.

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Mount, Ferdinand. Fairness. London: Chatto & Windus, 2001.

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Sandra, Ziegler. Fairness. Chicago, Ill: Created by Child's World, 1989.

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Zimmermann, Mario. Fairness Opinion. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12163-1.

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Canada. Dept. of Finance. Tax fairness. [Ottawa]: Dept. of Finance Canada, 1997.

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Small, Mary. Fairness is. Minneapolis, Minn: Picture Window Books, 2004.

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Bock, Greg, and Jamie Goode, eds. Empathy and Fairness. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470030585.

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Book chapters on the topic "Fairness"

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Francez, Nissim. "Introduction." In Fairness, 1–14. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_1.

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Francez, Nissim. "Termination and Well-Foundedness." In Fairness, 15–23. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_2.

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Francez, Nissim. "The Method of Helpful Directions." In Fairness, 24–57. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_3.

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Francez, Nissim. "The Method of Explicit Scheduler." In Fairness, 58–93. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_4.

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Francez, Nissim. "Extension and Generalizations of Fairness." In Fairness, 94–131. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_5.

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Francez, Nissim. "Fair Termination of Concurrent Processes." In Fairness, 132–72. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_6.

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Francez, Nissim. "Syntactic Expressibility." In Fairness, 173–201. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_7.

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Francez, Nissim. "Fairness in Temporal Logic." In Fairness, 202–39. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_8.

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Francez, Nissim. "The Functional Approach." In Fairness, 240–78. New York, NY: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4612-4886-6_9.

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Dänzer, Sonja. "Fairness." In Handbuch Gerechtigkeit, 168–73. Stuttgart: J.B. Metzler, 2016. http://dx.doi.org/10.1007/978-3-476-05345-9_27.

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Conference papers on the topic "Fairness"

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Castro, Clinton, David O'Brien, and Ben Schwan. "Fairness and Machine Fairness." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462577.

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Venkatasubramanian, Suresh. "Algorithmic Fairness." In the 38th ACM SIGMOD-SIGACT-SIGAI Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3294052.3322192.

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Salimi, Babak, Luke Rodriguez, Bill Howe, and Dan Suciu. "Interventional Fairness." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3319901.

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Brun, Yuriy, and Alexandra Meliou. "Software fairness." In ESEC/FSE '18: 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3236024.3264838.

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Räz, Tim. "Group Fairness." In FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3442188.3445876.

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Defrance, Marybeth, and Tijl De Bie. "Maximal fairness." In FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3593013.3594048.

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Pan, Weishen, Sen Cui, Jiang Bian, Changshui Zhang, and Fei Wang. "Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467258.

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Panda, Atasi, Anand Louis, and Prajakta Nimbhorkar. "Individual Fairness under Group Fairness Constraints in Bipartite Matching - One Framework to Approximate Them All." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/20.

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We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be assigned to it. There could be multiple optimal solutions that satisfy the group fairness constraints, but this alone ignores item preferences. Our approach explores a `best of both worlds fairness' solution to get a randomized matching, which is ex-ante individually fair and ex-post group-fair. Thus, we seek a `probabilistic individually fair' distribution over `group-fair' matchings where each item has a `high' probability of matching to one of its top choices. This distribution is also ex-ante group-fair. Users can customize fairness constraints to suit their requirements. Our first result is a polynomial-time algorithm that computes a distribution over `group-fair' matchings such that the individual fairness constraints are approximately satisfied and the expected size of a matching is close to OPT. We empirically test this on real-world datasets. We present two additional polynomial-time bi-criteria approximation algorithms that users can choose from to balance group fairness and individual fairness trade-offs. For disjoint groups, we provide an exact polynomial-time algorithm adaptable to additional lower `group fairness' bounds. Extending our model, we encompass `maxmin group fairness,' amplifying underrepresented groups, and `mindom group fairness,' reducing the representation of dominant groups.'
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Luo, Renqiang, Huafei Huang, Shuo Yu, Xiuzhen Zhang, and Feng Xia. "FairGT: A Fairness-aware Graph Transformer." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/50.

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The design of Graph Transformers (GTs) often neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fairness-aware graph learning methods are not directly applicable to address these issues. To tackle this challenge, we propose FairGT, a Fairness-aware Graph Transformer explicitly crafted to mitigate fairness concerns inherent in GTs. FairGT incorporates a meticulous structural feature selection strategy and a multi-hop node feature integration method, ensuring independence of sensitive features and bolstering fairness considerations. These fairness-aware graph information encodings seamlessly integrate into the Transformer framework for downstream tasks. We also prove that the proposed fair structural topology encoding with adjacency matrix eigenvector selection and multi-hop integration are theoretically effective. Empirical evaluations conducted across five real-world datasets demonstrate FairGT's superiority in fairness metrics over existing graph transformers, graph neural networks, and state-of-the-art fairness-aware graph learning approaches.
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Jiang, Tianji, Mostafa H. Ammar, and Ellen W. Zegura. "Inter-receiver fairness." In the 1998 ACM SIGMETRICS joint international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/277851.277913.

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Reports on the topic "Fairness"

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Alesina, Alberto, and George-Marios Angeletos. Corruption, Inequality and Fairness. Cambridge, MA: National Bureau of Economic Research, June 2005. http://dx.doi.org/10.3386/w11399.

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Hvidberg, Kristoffer, Claus Kreiner, and Stefanie Stantcheva. Social Position and Fairness Views. Cambridge, MA: National Bureau of Economic Research, November 2020. http://dx.doi.org/10.3386/w28099.

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Alesina, Alberto, and George-Marios Angeletos. Fairness and Redistribution: U.S. versus Europe. Cambridge, MA: National Bureau of Economic Research, February 2003. http://dx.doi.org/10.3386/w9502.

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Pecorino, Paul, and Mark VanBoening. Bargaining, Fairness and the Labor Allocation Problem. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada414359.

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Haberland, Nicole, and Debbie Rogow. iMatter: Teaching about Puberty, Gender, and Fairness. Population Council, 2015. http://dx.doi.org/10.31899/pgy9.1067.

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Alesina, Alberto, Guido Cozzi, and Noemi Mantovan. The Evolution of Ideology, Fairness and Redistribution. Cambridge, MA: National Bureau of Economic Research, December 2009. http://dx.doi.org/10.3386/w15587.

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Berman, Eli, Michael Callen, Clark Gibson, and James Long. Election Fairness and Government Legitimacy in Afghanistan. Cambridge, MA: National Bureau of Economic Research, March 2014. http://dx.doi.org/10.3386/w19949.

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Llinas, James, Satyaki G. Dastidar, Christopher Bowman, and Kedar Sambhoos. Achieving Fairness" in Data Fusion Performance Evaluation Development". Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada455903.

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Evans, Samuel E. Beyond Just War Doctrine: A Doctrine of Fairness. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada258355.

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Eyster, Erik, Kristof Madarasz, and Pascal Michaillat. Pricing when Customers Care about Fairness but Misinfer Markups. Cambridge, MA: National Bureau of Economic Research, September 2017. http://dx.doi.org/10.3386/w23778.

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