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Journal articles on the topic 'Fairness'

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Published: 4 June 2021
Last updated: 7 September 2024

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1

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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2

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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3

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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4

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

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5

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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6

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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7

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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8

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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9

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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10

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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11

van Dijk, Eric, Marijke C. Leliveld, and Ilja van Beest. "If it walks like fairness, and quacks like fairness, it sometimes is fairness: instrumental and true fairness in bargaining." Netherlands Journal of Psychology 65, no. 4 (December 2009): 155–62. http://dx.doi.org/10.1007/bf03080138.

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12

Tam, Nicoladie. "Quantification of fairness bias in relation to decisions using a relativistic fairness-equity model." Advances in Social Sciences Research Journal 1, no. 4 (July 25, 2014): 169–78. http://dx.doi.org/10.14738/assrj.14.292.

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13

Sève, Bernard, and Pierre Zaoui. "Fairness doctrine." Vacarme 3, no. 3 (1997): 30. http://dx.doi.org/10.3917/vaca.003.0030.

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14

Krakovsky, Marina. "Formalizing fairness." Communications of the ACM 65, no. 8 (August 2022): 11–13. http://dx.doi.org/10.1145/3542815.

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15

Dimitrakakis, Christos, Yang Liu, David C. Parkes, and Goran Radanovic. "Bayesian Fairness." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 509–16. http://dx.doi.org/10.1609/aaai.v33i01.3301509.

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We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in (Kleinberg, Mullainathan, and Raghavan 2016), we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.
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16

Chung, Inkwan. "Scrutinizing Fairness." Korean Journal of Sociology 55, no. 4 (November 30, 2021): 211–18. http://dx.doi.org/10.21562/kjs.2021.11.55.4.211.

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17

Venkatesh, Sudheesh, and Vishnupriya Bose. "Organizational Fairness." NHRD Network Journal 7, no. 4 (October 2014): 38–42. http://dx.doi.org/10.1177/0974173920140405.

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18

Broome, John. "V—Fairness." Proceedings of the Aristotelian Society 91, no. 1 (June 1, 1991): 87–102. http://dx.doi.org/10.1093/aristotelian/91.1.87.

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19

Sher, George. "Kantian Fairness." Philosophical Issues 15, no. 1 (October 2005): 179–92. http://dx.doi.org/10.1111/j.1533-6077.2005.00060.x.

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20

Pearce, Nick. "Rethinking fairness." Public Policy Research 14, no. 1 (March 2007): 11–22. http://dx.doi.org/10.1111/j.1744-540x.2007.00458.x.

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21

Schmuecker, Katie, and Paul Woods. "Funding fairness." Public Policy Research 18, no. 1 (March 2011): 41–48. http://dx.doi.org/10.1111/j.1744-540x.2011.00639.x.

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22

Mason, David S. "Fairness Matters." World Policy Journal 20, no. 4 (2003): 48–56. http://dx.doi.org/10.1215/07402775-2004-1004.

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23

Doyle, Rodger. "Undercutting Fairness." Scientific American 291, no. 1 (July 2004): 35. http://dx.doi.org/10.1038/scientificamerican0704-35.

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24

Leckey, Robert. "Employing Fairness." Canadian journal of law and society 18, no. 2 (August 2003): 45–67. http://dx.doi.org/10.1017/s0829320100007705.

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RésuméL'auteur, à partir de l'exemple de la pratique des grandes entreprises de payer à leurs employés des primes basées sur leur performance, fait état de deux types de contraintes s'appliquant aux employeurs: d'une part, les règles imposées par la loi; d'autre part, les recommandations des manuels de gestion des ressources humaines. Le droit considère les primes à travers le prisme étroit et traditionnel des règles portant sur les modalités de rémunération dans le cadre de la relation contractuelle avec l'employé. À l'opposé, la science de la gestion les traite comme une forme de communication avec l'employé et établit des recommandations concernant la conception et la mise en œuvre de programmes de primes qui révèlent une certaine notion d'équité administrative. L'auteur s'interroge sur les liens que cette notion d'équité administrative entretient avec le principe d'équité procédurale du droit public. Les différences entre ces deux conceptions de l'équité suggèrent que, contrairement à l'hypothèse mimétique, les entreprises qui adoptent des pratiques se voulant équitables ne font pas que reproduire les normes issues du contexte gouvernemental. Au contraire, l'équité administrative semble s'auto-générer à l'intérieur du champ social semi-autonome des entreprises. S'inspirant des travaux de Teubner sur les systèmes autopoïetiques, l'auteur considère comment le système juridique peut faciliter cette auto-régulation tout en conservant une certaine capacité d'intervention.
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25

Boesch, Lukas, and Roger Berger. "Explaining Fairness." Human Nature 30, no. 4 (November 15, 2019): 398–421. http://dx.doi.org/10.1007/s12110-019-09353-5.

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26

Bartlett, Roger W., and Wade L. Robison. "‘Fairness’ Revisited." Professional Ethics, A Multidisciplinary Journal 5, no. 3 (1996): 17–36. http://dx.doi.org/10.5840/profethics19965311.

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27

Alur, Rajeev, and Thomas A. Henzinger. "Finitary fairness." ACM Transactions on Programming Languages and Systems 20, no. 6 (November 1998): 1171–94. http://dx.doi.org/10.1145/295656.295659.

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28

Bicchieri, Cristina, and Brian Skyrms. "Local Fairness." Philosophy and Phenomenological Research 59, no. 1 (March 1999): 229. http://dx.doi.org/10.2307/2653473.

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29

Pitsiladis, Yannis, Joanna Harper, Jonathan Ospina Betancurt, Maria-Jose Martinez-Patino, Attilio Parisi, Guan Wang, and Fabio Pigozzi. "Beyond Fairness." Current Sports Medicine Reports 15, no. 6 (2016): 386–88. http://dx.doi.org/10.1249/jsr.0000000000000314.

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30

Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. "Algorithmic Fairness." AEA Papers and Proceedings 108 (May 1, 2018): 22–27. http://dx.doi.org/10.1257/pandp.20181018.

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Concerns that algorithms may discriminate against certain groups have led to numerous efforts to ‘blind’ the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.
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31

Cupit, G. "Review: Fairness." Mind 113, no. 450 (April 1, 2004): 387–90. http://dx.doi.org/10.1093/mind/113.450.387.

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32

Kennedy, Helen, Dag Elgesem, and Cristina Miguel. "On fairness." Convergence: The International Journal of Research into New Media Technologies 23, no. 3 (June 28, 2015): 270–88. http://dx.doi.org/10.1177/1354856515592507.

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33

Diller, Herman. "Price fairness." Journal of Product & Brand Management 17, no. 5 (August 22, 2008): 353–55. http://dx.doi.org/10.1108/10610420810896103.

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34

Menzel, Paul T. "Against Fairness." Journal of Bioethical Inquiry 11, no. 1 (January 18, 2014): 95–97. http://dx.doi.org/10.1007/s11673-013-9505-0.

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35

Sun, Weijie, Zihuan Xu, and Lei Chen. "Fairness Matters." Proceedings of the VLDB Endowment 15, no. 13 (September 2022): 4048–61. http://dx.doi.org/10.14778/3565838.3565856.

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The proof-of-work (PoW) based blockchains are more secure nowadays since profit-oriented miners contribute more computing powers in exchange for fair revenues. This virtuous circle only works under an incentive-compatible consensus, which is found to be fragile under selfish mining attacks. Specifically, selfish miners can conceal and reveal blocks strategically to earn unfairly higher revenue compared to honest behaviors. Previous countermeasures either require incompatible modifications or fail to consider the asynchronous network and multiple honest nodes setting in reality. In this paper, we introduce the unfairness measurement based on the KL-divergence from the computing power distribution to the revenue distribution of miners. To improve fairness with the existence of selfish miners, we propose a novel block promotion strategy namely Tit-for-Tat (TFT), for honest miners. In particular, based on a miner's local observation of forks, we design the suspicious probability measurement of other nodes. Rather than promoting a fresh block instantly, miners withhold it for different time periods according to others' suspicious probability before delivery. Meanwhile, to minimize the attacker's unfair revenue, we formulate the delay vector (DV) problem for honest miners to determine the optimal withholding time. We prove that DV problem is nonconvex, and thus propose two approximation algorithms that yield ϵ-suboptimal solutions. In addition, we extend TFT strategy to support dynamic networks. Extensive experiments validate the efficiency and effectiveness of our strategy and algorithms to reduce unfairness by 54.62% within bounded withholding time.
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36

PARK, JUNSIK. "What’s fairness?" Journal of Special Education 40, no. 2 (May 31, 2024): 91–94. http://dx.doi.org/10.31863/jse.2024.05.40.2.91.

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37

Wang, Aizeng, Gang Zhao, and Yong-Dong Li. "Fairness degree based fairness criterion and fairing algorithm." Applied Mathematics and Computation 253 (February 2015): 184–92. http://dx.doi.org/10.1016/j.amc.2014.12.081.

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38

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 (June 26, 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 group fairness criteria over a union of similar sub-populations. We then present a Siamese fairness in-processing approach to minimize the accuracy and fairness losses of a machine learning model under the accurate fairness constraints. To the best of our knowledge, this is the first time that a Siamese approach is adapted for bias mitigation. We also propose fairness confusion matrix-based metrics, fair-precision, fair-recall, and fair-F1 score, to quantify a trade-off between accuracy and individual fairness. Comparative case studies with popular fairness datasets show that our Siamese fairness approach can achieve on average 1.02%-8.78% higher individual fairness (in terms of fairness through awareness) and 8.38%-13.69% higher accuracy, as well as 10.09%-20.57% higher true fair rate, and 5.43%-10.01% higher fair-F1 score, than the state-of-the-art bias mitigation techniques. This demonstrates that our Siamese fairness approach can indeed improve individual fairness without trading accuracy. Finally, the accurate fairness criterion and Siamese fairness approach are applied to mitigate the possible service discrimination with a real Ctrip dataset, by on average fairly serving 112.33% more customers (specifically, 81.29% more customers in an accurately fair way) than baseline models.
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39

Tam, Nicoladie. "Quantification of fairness perception by including other-regarding concerns using a relativistic fairness-equity model." Advances in Social Sciences Research Journal 1, no. 4 (July 25, 2014): 159–68. http://dx.doi.org/10.14738/assrj.14.291.

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40

Ferguson, Jodie L., Pam Scholder Ellen, and William O. Bearden. "Procedural and Distributive Fairness: Determinants of Overall Price Fairness." Journal of Business Ethics 121, no. 2 (April 11, 2013): 217–31. http://dx.doi.org/10.1007/s10551-013-1694-2.

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41

Dennunzio, Alberto, Enrico Formenti, Luca Manzoni, and Giancarlo Mauri. "m-Asynchronous cellular automata: from fairness to quasi-fairness." Natural Computing 12, no. 4 (July 24, 2013): 561–72. http://dx.doi.org/10.1007/s11047-013-9386-5.

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42

Kurz, Verena, Andreas Orland, and Kinga Posadzy. "Fairness versus efficiency: how procedural fairness concerns affect coordination." Experimental Economics 21, no. 3 (September 4, 2017): 601–26. http://dx.doi.org/10.1007/s10683-017-9540-5.

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43

Steinacker, Jürgen M. "Sportrecht – zwischen Gerechtigkeit und Fairness?" Deutsche Zeitschrift für Sportmedizin 2013, no. 05 (May 1, 2013): 119. http://dx.doi.org/10.5960/dzsm.2012.071.

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44

Weeks, Virginia. "Fairness in the Exceptions: Trusting Juries on Matters of Race." Michigan Journal of Race & Law, no. 23.1 (2018): 189. http://dx.doi.org/10.36643/mjrl.23.1.fairness.

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Implicit bias research indicates that despite our expressly endorsed values, Americans share a pervasive bias disfavoring Black Americans and favoring White Americans. This bias permeates legislative as well as judicial decision-making, leading to the possibility of verdicts against Black defendants that are tainted with racial bias. The Supreme Court’s 2017 decision in Peña-Rodriguez v. Colorado provides an ex post remedy for blatant racism that impacts jury verdicts, while jury nullification provides an ex ante remedy by empowering jurors to reject convicting Black defendants when to do so would reinforce racially biased laws. Both remedies exist alongside a trend limiting the role of the jury and ultimately indicate that we trust juries to keep racism out of the courtroom in the exceptions to our normal procedures.
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45

Liu, Guangdong, Jingxiu Song, Jinggui Chen, Ziyang Li, and Huagui Zhu. "Ordering Decisions of Supply Chain with Competition and Dual-Fairness Concern." Discrete Dynamics in Nature and Society 2022 (August 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/9811993.

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This study studies a supply chain consisting of one supplier and two retailers and analyzes the optimal decisions of ordering quantity in four types of no fairness concern, horizontal fairness concern, vertical fairness concern, and dual-fairness concern and the impacts of fairness concern on supply chain. The results show that (1) vertical fairness concern can decrease the ordering quantity of the retailer with vertical fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern; (2) horizontal fairness concern can increase the ordering quantity of the retailer with horizontal fairness concern and improve the ordering quantity of the retailer without fairness concern, but does not influence the wholesale price; and (3) dual-fairness concern can decrease the ordering quantity of the retailer with dual-fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern as a whole. The numerical analysis also proves the findings.
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46

Hertweck, Corinna, and Tim Räz. "Gradual (In)Compatibility of Fairness Criteria." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11926–34. http://dx.doi.org/10.1609/aaai.v36i11.21450.

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Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.
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47

Brzezinski, Steve, Myra Sadker, and David Sadker. "Failing at Fairness." Antioch Review 52, no. 3 (1994): 532. http://dx.doi.org/10.2307/4613017.

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48

Moreno Okuno, Alejandro Tatsuo, and Alejandro Mosiño. "Group fairness equilibria." econoquantum 17, no. 1 (December 15, 2019): 29–46. http://dx.doi.org/10.18381/eq.v17i1.6554.

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49

Sheehan, Micheal. "Striving for fairness." Nursing Standard 3, no. 42 (July 15, 1989): 47. http://dx.doi.org/10.7748/ns.3.42.47.s57.

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50

Burkhard, Hans-Dieter. "Fairness and control." Banach Center Publications 21, no. 1 (1988): 115–28. http://dx.doi.org/10.4064/-21-1-115-128.

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