Relevant bibliographies by topics / Individual Fairness

Academic literature on the topic 'Individual Fairness'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Individual Fairness"

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Meernik, James, and Kimi King. "The Fairness of International Justice." International Criminal Law Review 21, no. 6 (September 24, 2021): 1167–89. http://dx.doi.org/10.1163/15718123-bja10103.

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Abstract There is an ever-expanding system of international criminal justice in which the concern for fairness is equally as powerful as it is in domestic criminal justice systems, but considerably more fraught with political baggage. The goal of our paper is to develop a model of individual evaluations of fairness of an international court and subject our current understandings of fairness to a challenging test by focusing on those with a tremendous stake in the outcomes of trials—the witnesses. We develop a model that emphasizes three core elements of individual judgments regarding fairness: (1) the identity of the individual—those relatively fixed characteristics that individuals use to explain who they are; (2) the individual encounter with international justice; and (3) general, or more objective indicators of Tribunal effectiveness. We then derive specific hypothesis from these expectations. We utilize data from a unique survey of 300 individuals who testified at the International Criminal Tribunal for the Former Yugoslavia (icty). We find that identity, personal efficacy and to a lesser extent, the perceived appropriateness of punishment for the guilty affect perceptions of fairness.
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Farnadi, Golnoosh, William St-Arnaud, Behrouz Babaki, and Margarida Carvalho. "Individual Fairness in Kidney Exchange Programs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11496–505. http://dx.doi.org/10.1609/aaai.v35i13.17369.

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Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a donor which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.
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Li, Xia, and Linyan Feng. "Impact of donors’ financial fairness perception on donation intention in nonprofit organizations after COVID-19 outbreak." PLOS ONE 16, no. 6 (June 9, 2021): e0251991. http://dx.doi.org/10.1371/journal.pone.0251991.

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Based on the investigation of financial fairness perception and donation intention of individual donors in non-profit organizations (NPOs), this paper uses structural equation model to analyze the impact of individual donors’ financial fairness perception on donation intention. The results show that individual donors’ perceptions on financial result fairness, financial procedure fairness and financial information fairness all have positive impact on donation intention; among which the perception on financial result fairness only has direct impact on individual donation intention, while the perceptions on financial procedure fairness and financial information fairness have direct and indirect impact on individual donation intention.
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Shaham, Sina, Gabriel Ghinita, and Cyrus Shahabi. "Models and mechanisms for spatial data fairness." Proceedings of the VLDB Endowment 16, no. 2 (October 2022): 167–79. http://dx.doi.org/10.14778/3565816.3565820.

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Fairness in data-driven decision-making studies scenarios where individuals from certain population segments may be unfairly treated when being considered for loan or job applications, access to public resources, or other types of services. In location-based applications, decisions are based on individual whereabouts, which often correlate with sensitive attributes such as race, income, and education. While fairness has received significant attention recently, e.g., in machine learning, there is little focus on achieving fairness when dealing with location data. Due to their characteristics and specific type of processing algorithms, location data pose important fairness challenges. We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries. We devise a novel building block to achieve fairness in the form of fair polynomials. Next, we propose two mechanisms based on fair polynomials that achieve individual spatial fairness, corresponding to two common location-based decision-making types: distance-based and zone-based. Extensive experimental results on real data show that the proposed mechanisms achieve spatial fairness without sacrificing utility.
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Lahoti, Preethi, Krishna P. Gummadi, and Gerhard Weikum. "Operationalizing individual fairness with pairwise fair representations." Proceedings of the VLDB Endowment 13, no. 4 (December 9, 2019): 506–18. http://dx.doi.org/10.14778/3372716.3372723.

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Bishop, John A., Haiyong Liu, and Zichong Qu. "Individual Perceptions of Distributional Fairness in China." Comparative Economic Studies 56, no. 1 (December 12, 2013): 25–41. http://dx.doi.org/10.1057/ces.2013.27.

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Cappelen, Alexander W., Erik Ø. Sørensen, and Bertil Tungodden. "Responsibility for what? Fairness and individual responsibility." European Economic Review 54, no. 3 (April 2010): 429–41. http://dx.doi.org/10.1016/j.euroecorev.2009.08.005.

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Karni, Edi, Tim Salmon, and Barry Sopher. "Individual sense of fairness: an experimental study." Experimental Economics 11, no. 2 (July 14, 2007): 174–89. http://dx.doi.org/10.1007/s10683-007-9165-1.

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Singer, Ming. "INDIVIDUAL DIFFERENCES IN CATEGORY WIDTH AND FAIRNESS PERCEPTION OF SELECTION DECISIONS." Social Behavior and Personality: an international journal 18, no. 1 (January 1, 1990): 87–93. http://dx.doi.org/10.2224/sbp.1990.18.1.87.

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The study examined the effect of individual differences in category width on fairness perception of selection decisions. The hypothesis was that narrow categorizers would have more exaggerated perceptions of fairness than broad categorizers. Subjects completed Pettigrew's (1958) Category-Width Scale and a questionnaire designed to assess fairness perceptions of selection outcomes. Although the results of individual differences in fairness perception were consistent and were in the same directions as hypothesized, the main effect of “categorizer” did not reach statistical significance. The results also showed that merit-based selections were perceived as fair. Selections involving preferential treatment were perceived as equally unfair as conventional discrimination against minority candidates.
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Merriman, Kimberly K., Atthaphon Mumi, and Lauren A. Turner. "Extending Evidence for Inter-Individual Differences in Social Comparison Orientation to Pay Fairness Evaluations." Psychological Reports 123, no. 4 (May 19, 2019): 1335–54. http://dx.doi.org/10.1177/0033294119849018.

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This research extends the limited support for social comparison tendencies as an individual difference variable and a key moderator of pay fairness perceptions. Through three studies comprised of five data collections, the following adapts a measure of social comparison orientation to pay contexts and examines its association with heightened perceptions of distributive fairness in hypothetical and actual scenarios of pay equity, over-reward, and under-reward. In keeping with Gibbons and Buunk’s construal, our targeted operationalization of social comparison orientation demonstrated inter-individual variation and intra-individual stability, providing corroboration of distinct individual predispositions towards social comparison. Our experimental findings further support this point in that socially relative pay information had a stronger impact on pay fairness evaluations among individuals predisposed to socially compare and a relatively weak impact on those that were not. This investigation is complementary but distinct from the prevalent focus on situational factors as drivers of social comparison. Further, examining this point in the context of pay is timely based on the recent level of public and managerial attention given to the fairness of relative pay differences.
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Dissertations / Theses on the topic "Individual Fairness"

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Ma, Sinong. "Fairness views in social and individual decisions." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/96254/.

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Fairness and efficiency are two classical and connected topics in economics. They have become well known, perhaps due to Adam Smith’s two influential works: The Theory of Moral Sentiments (1759), which highlights a concern for fairness concern as part of morality, and The Wealth of Nations (1776), which underlines a concern for efficiency. However, during the rapid development of economics, fairness has received disproportionately less attention than efficiency. As a result, many people, including some economists, have incorrectly understood that economics as a subject no longer cares about fairness. The primary objective of this thesis is to dispel this misperception. We would argue here that, similar to efficiency, fairness is an important factor for both social and individual decisions, and sometimes its effect can be determined. Written in a three-paper format, this thesis explores fairness from three different angles. These angles cover the broad areas of how theoretical economists model fairness in social choice theory, how the general public perceive distributive fairness, and how people implement their fairness norms in making real-life donations. This multidimensional exploration is believed to be crucial to a comprehensive understanding of fairness.
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Weinreich, Daniel [Verfasser]. "Essays on Norms, Fairness, Individual Risk Taking and Taxation / Daniel Weinreich." Hagen : Fernuniversität Hagen, 2014. http://d-nb.info/1050955978/34.

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Gerlach, Philipp. "The Social Framework of Individual Decisions." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18725.

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Wann und warum verhalten sich Menschen ethisch (in-)korrekt? Die vorliegende Dissertation fasst allgemeine Theorien und experimentelle Befunde (nicht-)kooperativen, (un-)fairen und (un-)ehrlichen Verhaltens zusammen. Hierzu führt Kapitel 1 experimentelle Spiele als rigoroses Instrument zur Untersuchung (un-)ethischen Verhaltens ein. Kapitel 2 zeigt, dass sich kleine Veränderungen in der kontextuellen Rahmung von experimentellen Spielen langanhaltend auf die Kooperationsneigung der Teilnehmer auswirken können. Kontextuelle Rahmungen verändern zudem Verhaltenserwartungen sowie Aufteilungen in nicht-strategischen Situationen. Diese Effekte sind durch Theorien sozialer Normen erklärbar. Kapitel 3 ergründet, warum sich Studierende der Wirtschaftswissenschaften teils egoistischer verhalten als ihre Kommilitonen. Theorien sozialer Normen werden hierbei um die Bereitschaft erweitert, Nonkonformität mittels Sanktionen zu erzwingen. Es wird gezeigt, dass sich Studierende der Wirtschaftswissenschaften und anderer Fächer in ihren Aufteilungsentscheidungen ähnlich häufig mit Fairness beschäftigen und zu ähnlichen Einschätzungen kommen, welche Aufteilung als fair gilt. Sie teilen jedoch weniger großzügig und erwarten dies auch von anderen. Zudem sind sie weniger bereit, als unfair angesehene Aufteilungen zu sanktionieren. Es wird argumentiert, dass sich Studierende der Wirtschaftswissenschaften egoistischer verhalten, weil sie nicht daran glauben, dass sich andere an eine grundsätzlich geteilte Fairnessnorm halten. Kapitel 4 zeigt, dass intrinsische Sanktionen (wie Scham und Schuld) ausreichen, damit sich Menschen ethisch korrekt verhalten. Das Kapitel bietet zahlreiche Antworten zu aktuellen Debatten, wer sich unter welchen Umständen (un-)ehrlich verhält. Es wird gezeigt, dass Ehrlichkeit sowohl von situativen Einflüssen (z.B. Anreizen und Externalitäten) wie von persönlichen Aspekten (z.B. Geschlecht und Alter) und letztlich auch vom experimentellen Paradigma abhängt.
When and why do people engage in (un)ethical behavior? This dissertation summarizes general theories and synthesizes experimental findings on (non)cooperation, (un)fairness, and (dis)honesty. To this end, Chapter 1 introduces experimental games as a rigorous tool for studying (un)ethical behavior. Chapter 2 demonstrates that small changes in the framing of context (e.g., referring to a social dilemma as a competition vs. a team endeavor) can have long-lasting effects on the participants’ propensity to cooperate. Context framing also shapes beliefs about the cooperative behavior of interaction partners and donations in non-strategic allocation decisions. Taken together, the results suggest that social norm theories provide a plausible explanation for cooperation, including its sensitivity to context framing. Chapter 3 investigates why experimental games regularly find that economics students behave more selfishly than their peers. The concept of social norms is thereby extended to include the enforcement of compliance per sanctions. The results indicate that economics students and students of other majors are about equally concerned with fairness and they have similar notions of fairness in the situation. However, economics students make lower allocations, expect others to make lower allocations, and are less willing to sanction allocations seen as unfair. Skepticism mediated their lower allocations, suggesting that economics students behave more selfishly because they expect others not to comply with a shared fairness norm. Chapter 4 shows that intrinsic sanctions (e.g., shame and guilt) can be sufficient for ethical behavior to emerge. The chapter provides answers to many of the ongoing debates on who behaves dishonestly and under what circumstances. The findings suggest that dishonest behavior depends on situational factors (e.g., reward magnitude and externalities), personal factors (e.g., gender and age) as well as on the experimental paradigm itself.
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Rickles, Michael L. Jr. "EXPLORING RACIAL DIFFERENCES IN INDIVIDUAL AND STRUCTURAL ATTRIBUTIONS, SELF-EVALUATIONS AND PERCEPTIONS OF INCOME FAIRNESS." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1365669591.

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Moser, Franziska [Verfasser]. "Social Construction of Gender-(un)fairness : An Analysis of Educational Material and Individual Language Use / Franziska Moser." Berlin : Freie Universität Berlin, 2013. http://d-nb.info/1043198032/34.

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Braga, Raphael Brasileiro. "AltruÃsmo e tolerÃncia em meio ao pluralismo: a proposta de John Rawls em favor de uma sociedade justa." Universidade Federal do CearÃ, 2010. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=19927.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
Para John Rawls, uma sociedade justa Ã, em princÃpio, uma comunidade polÃtica onde prevalecem a cooperaÃÃo, o senso de justiÃa e as virtudes da cidadania. Sua teoria apresenta princÃpios de justiÃa, que servirÃo de fundamento para uma estrutura bÃsica social bem-ordenada, na qual cada cidadÃo age justamente e contribui para a manutenÃÃo de instituiÃÃes justas. Para Robert Nozick, porÃm, um Estado justo em relaÃÃo aos seus cidadÃos nada mais à do que um Estado que respeita a conduta individual. O autor afirma que um Estado nÃo tem o direito de forÃar uma pessoa mais privilegiada a contribuir com um menos favorecido a fim de que este tenha seu bem-estar aumentado. Se vocà for forÃado, seja pelo Estado, seja por alguÃm, a contribuir para o bem-estar de terceiros, seus direitos estarÃo sendo violados. Diante disso, Nozick afirma que o Estado MÃnimo à o mais extenso que se pode justificar. Qualquer outro mais amplo viola os direitos dos cidadÃos.
For John Rawls, a fair society, at first, is a political community where there is the prevalence of cooperation, sense of justice and citizenship virtues. His theory presents the principles of justice, which will be the basis for a well-organized social basic structure in which each citizen acts with justice and contributes for the maintenance of fair institutions. However, for Robert Nozick a fair State with its citizens is a State that respects the individual behavior. The author says that a State does not have right of force a person more privileged to contribute with a less privileged in order to this has increased his well-being. If you are forced, or by State, or by any person, to contribute to well-being of third party, your rights are violated. Like this, Nozick says that the Minimal State is the most extensive that we can justify. Any other wider violate the citizensâ rights.
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Hedberg, Per Henrik. "Interpersonal society : essays on shared beliefs, trust, mnemonic oppression, distributive fairness, and value creation." Doctoral thesis, Handelshögskolan i Stockholm, Institutionen för Marknadsföring och strategi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-1761.

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Zaragoza, Joseph. "The Impact of Individual Perceptions of the Fairness of Public Affirmative Action Policy Statements on Attitudes toward the Organization." Master's thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5588.

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The purpose of this research project was to explore differences in perceptions of organizational justice and related attitudes. Through the use of a 3 x 2 experimental design, participants were randomly assigned to groups in which they were exposed to a fictitious organization's mock recruitment document publicizing different types of affirmative action programs and varying levels of information regarding the mechanics of such programs. Results did not demonstrate statistically significant differences across groups. Project implications, limitations, and suggestions for future research are discussed.
M.S.
Masters
Psychology
Sciences
Industrial Organizational Psychology
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Kasper, Matthias. "How do Institutional, Social, and Individual Factors Shape Tax Compliance Behavior? Evidence from 14 Eastern European Countries." WU Vienna University of Economics and Business, Universität Wien, 2016. http://epub.wu.ac.at/5153/1/SSRN%2Did2825994.pdf.

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This paper uses micro-level data from a nationally representative survey of 22,000 individuals in 14 Eastern European countries to investigate the effects of institutional, social, and individual factors on taxpayers' perceptions of power, motivations to comply, and non-compliant behaviors. The results indicate that institutional, social, and individual aspects shape taxpayer behavior: attitudes of peers, individual compliance norms, and the tax burden impact on non-compliance. Moreover, I find several effects of the subjective appraisal of the interaction with tax administrations. Positive experiences strengthen perceptions of power and intrinsic motivations to comply. They also increase the propensity to report non-compliant behavior in the past, suggesting educational effects of taxpayer services and tax audits. (author's abstract)
Series: WU International Taxation Research Paper Series
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Stråberg, Teresia. "Employee perspectives on individualized pay attitudes and fairness perceptions /." Stockholm : Department of Psychology, Stockholm University, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-38308.

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Books on the topic "Individual Fairness"

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Hao, Jianye, and Ho-fung Leung. Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49470-7.

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Ellemers, Naomi, ed. World of Difference. Translated by Gioia Marini. NL Amsterdam: Amsterdam University Press, 2017. http://dx.doi.org/10.5117/9789462984028.

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Public debates tend to see social inequality as resulting from individual decisions people make, for instance with respect to their education or lifestyle. Solutions are often sought in supporting individuals to make better choices. This neglects the importance of social groups and communities in determining individual outcomes. A moral perspective on social inequality questions the fairness of insisting on individual responsibilities, when members of some groups systematically receive fewer opportunities than others. The essays in this book have been prepared by experts from different disciplines, ranging from philosophy to engineering, and from economics to epidemiology. On the basis of recent scientific insights, World of Difference examines how group memberships impact on individual outcomes in four key domains: health, education and work, migration, and the environment. This offers a new moral perspective on social inequality, which policy makers tend to neglect.
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Little, Blaine. Individual Team: How Fairness Wrecked the Workplace. Independently Published, 2020.

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Leung, Ho-fung, and Jianye Hao. Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality. Springer Berlin / Heidelberg, 2016.

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Leung, Ho-fung, and Jianye Hao. Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality. Springer, 2018.

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Leung, Ho-fung, and Jianye Hao. Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality. Springer London, Limited, 2016.

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Summers, Sarah, and John Jackson. Obstacles to Fairness in Criminal Proceedings: Individual Rights and Institutional Forms. Bloomsbury Publishing Plc, 2018.

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Jackson, John D., and Sarah J. Summers. Obstacles to Fairness in Criminal Proceedings: Individual Rights and Institutional Forms. Bloomsbury Publishing Plc, 2020.

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Don't judge a book by its cover: Stories about fairness and good judgment. New York: Disney Press, 2005.

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Ferreira, Francisco H. G., and Vito Peragine. Individual Responsibility and Equality of Opportunity. Edited by Matthew D. Adler and Marc Fleurbaey. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199325818.013.24.

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Building on earlier work by political philosophers, economists have recently sought to define a concept of equity that accommodates the fairness of reward to individual responsibility and effort, while allowing for the existence of some inequalities that are unfair and should be compensated. This chapter provides a critical review of the economic literature on equality and inequality of opportunity. A simple “canonical model” of equal opportunity is proposed, and used to explore the two fundamental concepts in this (relatively) new theory of social justice: the principles of compensation and reward. Ex ante and ex post versions of the compensation principle are presented, and the tensions between them are discussed. Different approaches to the measurement of inequality of opportunity—and empirical applications—are reviewed, and implications for the measurement of poverty and of the rate of economic development are discussed.
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Book chapters on the topic "Individual Fairness"

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Bal, Michèlle, and Kees van den Bos. "Fairness." In Encyclopedia of Personality and Individual Differences, 1549–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_2096.

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Bal, Michèlle, and Kees van den Bos. "Fairness." In Encyclopedia of Personality and Individual Differences, 1–4. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_2096-1.

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Aldogan Eklund, Mehtap. "Compensation According to Characteristics, Competence, and Individual Performance." In Fairness of CEO Compensation, 89–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33554-0_7.

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Ionescu, Stefania, Nicolò Pagan, and Anikó Hannák. "Individual Fairness for Social Media Influencers." In Complex Networks and Their Applications XI, 162–75. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21127-0_14.

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Grari, Vincent, Oualid El Hajouji, Sylvain Lamprier, and Marcin Detyniecki. "Enforcing Individual Fairness via Rényi Variational Inference." In Communications in Computer and Information Science, 608–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92307-5_71.

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Hao, Jianye, and Ho-fung Leung. "Fairness in Cooperative Multiagent Systems." In Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality, 27–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49470-7_3.

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Burke, Robin. "Personalization, Fairness, and Post-Userism." In Perspectives on Digital Humanism, 145–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86144-5_20.

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AbstractThe incorporation of fairness-aware machine learning presents a challenge for creators of personalized systems, such as recommender systems found in e-commerce, social media, and elsewhere. These systems are designed and promulgated as providing services tailored to each individual user’s unique needs. However, fairness may require that other objectives, possibly in conflict with personalization, also be satisfied. The theoretical framework of post-userism, which broadens the focus of design in HCI settings beyond the individual end user, provides an avenue for this integration. However, in adopting this approach, developers will need to offer new, more complex narratives of what personalized systems do and whose needs they serve.
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Hao, Jianye, and Ho-fung Leung. "Individual Rationality in Competitive Multiagent Systems." In Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality, 115–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49470-7_5.

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Sun, Yufei, Yong Li, and Zhen Cui. "NFW: Towards National and Individual Fairness in Face Recognition." In Lecture Notes in Computer Science, 540–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0_40.

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Schwenkenbecher, Anne. "Antimicrobial Footprints, Fairness, and Collective Harm." In Ethics and Drug Resistance: Collective Responsibility for Global Public Health, 377–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27874-8_23.

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Abstract This chapter explores the question of whether or not individual agents are under a moral obligation to reduce their ‘antimicrobial footprint’. An agent’s antimicrobial footprint measures the extent to which her actions are causally linked to the use of antibiotics. As such, it is not necessarily a measure of her contribution to antimicrobial resistance. Talking about people’s antimicrobial footprint in a way we talk about our carbon footprint may be helpful for drawing attention to the global effects of individual behaviour and for highlighting that our choices can collectively make a real difference. But can we be morally obligated to make a contribution to resolving a collective action problem when our individual contributions by themselves make no discernible difference? I will focus on two lines of argument in favour of such obligations: whether a failure to reduce one’s antimicrobial footprint is unfair and whether it constitutes wrongdoing because it is harmful. I conclude by suggesting that the argument from collective harm is ultimately more successful.
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Conference papers on the topic "Individual Fairness"

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Gupta, Swati, and Vijay Kamble. "Individual Fairness in Hindsight." In EC '19: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328526.3329605.

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Fleisher, Will. "What's Fair about Individual Fairness?" In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462621.

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Arsenis, Makis, and Robert Kleinberg. "Individual Fairness in Prophet Inequalities." In EC '22: The 23rd ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490486.3538301.

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Benussi, Elias, Andrea Patane', Matthew Wicker, Luca Laurenti, and Marta Kwiatkowska. "Individual Fairness Guarantees for Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/92.

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We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the epsilon-delta-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the output difference between any pair of epsilon-similar individuals is bounded by a maximum decision tolerance delta >= 0. Working with a range of metrics, including the Mahalanobis distance, we propose a method to overapproximate the resulting optimisation problem using piecewise-linear functions to lower and upper bound the NN's non-linearities globally over the input space. We encode this computation as the solution of a Mixed-Integer Linear Programming problem and demonstrate that it can be used to compute IF guarantees on four datasets widely used for fairness benchmarking. We show how this formulation can be used to encourage models' fairness at training time by modifying the NN loss, and empirically confirm our approach yields NNs that are orders of magnitude fairer than state-of-the-art methods.
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Yeom, Samuel, and Matt Fredrikson. "Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/61.

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We turn the definition of individual fairness on its head - rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold: First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.
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Kang, Jian, Jingrui He, Ross Maciejewski, and Hanghang Tong. "InFoRM: Individual Fairness on Graph Mining." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403080.

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Dong, Yushun, Jian Kang, Hanghang Tong, and Jundong Li. "Individual Fairness for Graph Neural Networks." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467266.

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Binns, Reuben. "On the apparent conflict between individual and group fairness." In FAT* '20: Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3351095.3372864.

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Hu, Qian, and Huzefa Rangwala. "Metric-Free Individual Fairness with Cooperative Contextual Bandits." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00027.

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Wang, Xiuling, and Wendy Hui Wang. "Providing Item-side Individual Fairness for Deep Recommender Systems." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3533079.

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