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9th ACM Web Science Conference (WebSci)

作者： Vallejos, Elvira Perez Koene, Ansgar Portillo, Virginia Dowthwaite, Liz Cano, Monica Univ Nottingham Horizon Digital Econ Res Inst Triumph Rd Nottingham NG7 2TU England

ISBN: (纸本)9781450348966

This paper explores the policy recommendations made by young people regarding algorithm fairness. It describes a piece of ongoing research developed to bring children and young people to the front line of the debate regarding children's digital rights. We employed the Youth Juries methodology which was designed to facilitate learning through discussions. The juries capture the deliberation process on a specific digital right, the right to know how algorithms govern and influence the Web and its users. Preliminary results show that young people demand to know more about algorithms, they want more transparency, more options, and more control about the way algorithms use their personal data.

关键词： Youth jury algorithm fairness youth opinion deliberation digital literacy digital citizenship privacy policy

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A sociotechnical perspective for explicit unfairness mitigation techniques for algorithm fairness

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International Journal of Information Management Data Insights 2024年第2期4卷

作者： Singh, Nimisha Kapoor, Amita Soni, Neha Symbiosis Centre for Management and Human Resource Development Symbiosis International (Deemed University) Pune India University of Oxford United Kingdom Department of Physics MMEC Maharishi Markandeshwar (Deemed to be University) Haryana Mullana-Ambala India

With the increasing use of artificial intelligence (AI) applications in decision making, there are heightened concerns about the fairness of such decisions. Initiatives like Responsible AI, Fair ML, Ethics in AI have provided guidelines for developing AI as an attempt to address these challenges. These approaches have been criticized for taking a top down approach by applying abstract principles to practice without taking into account the context and particularities of the algorithm development. Using the sociotechnical lens, we propose a framework for developing Fair algorithm. We apply this framework to mitigate unfairness in three distinct datasets: COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), Crimes and Community, and a synthetic dataset. Our methodology involves nonconvex optimization for regression with fairness constraints. The experimentation examines the correlation coefficient, Area Under the Curve (AUC), and Root Mean Square Error (RMSE) in relation to a fairness parameter, epsilon. Our findings suggest three objectively testable propositions namely, 1) fairness Constraint and Predictive power, 2) fairness Constraints and Discriminatory Ability, 3) fairness Constraints and Prediction Accuracy. © 2024

关键词： algorithm fairness Artificial intelligence Explicit mitigation technique In-processing mitigation Regression Socio-technical system

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BMFT: Achieving fairness via Bias-Based Weight Masking Fine-Tuning 2nd

BMFT: Achieving Fairness via Bias-Based Weight Masking Fine...

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2nd International Workshop on fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI 2024

作者： Xue, Yuyang Yan, Junyu Dutt, Raman Haider, Fasih Liu, Jingshuai McDonagh, Steven Tsaftaris, Sotirios A. School of Engineering The University of Edinburgh EdinburghEH9 3FG United Kingdom School of Informatics The University of Edinburgh EdinburghEH8 9AB United Kingdom

ISBN: (纸本)9783031727863

Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and depend on model retraining, which may not be practical in real-world scenarios. To mitigate these challenges, we propose Bias-based Weight Masking Fine-Tuning (BMFT), a novel post-processing method that enhances the fairness of a trained model in significantly fewer epochs without requiring access to the original training data. BMFT produces a mask over model parameters, which efficiently identifies the weights contributing the most towards biased predictions. Furthermore, we propose a two-step debiasing strategy, wherein the feature extractor undergoes initial fine-tuning on the identified bias-influenced weights, succeeded by a fine-tuning phase on a reinitialised classification layer to uphold discriminative performance. Extensive experiments across four dermatological datasets and two sensitive attributes demonstrate that BMFT outperforms existing state-of-the-art (SOTA) techniques in both diagnostic accuracy and fairness metrics. Our findings underscore the efficacy and robustness of BMFT in advancing fairness across various out-of-distribution (OOD) settings. Our code is available at: https://***/vios-s/BMFT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： algorithm fairness Bias Identification Bias Removal

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algorithm Bias and Perceived fairness: A Comprehensive Scoping Review 24

Algorithm Bias and Perceived Fairness: A Comprehensive Scopi...

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62nd Computers and People Research Conference (ACM SIGMIS-CPR) - Trust and Legitimacy in Emerging Technologies - Organizational and Societal Implications for People, Places and Power

作者： Amirhossein, Hajigholam Saryazdi Concordia Univ John Molson Sch Business Montreal PQ Canada

ISBN: (纸本)9798400704772

Artificial intelligence (AI)-based algorithms are playing an increasingly prominent role in shaping daily life. However, these algorithms can exhibit biases that exacerbate societal injustices. Such biases have a substantial impact on people's perceptions of algorithmic fairness, yet the precise mechanisms and scope of this phenomenon remain relatively understudied. To address this research gap, a comprehensive scoping literature review is conducted, providing an overview of current research in the field. Subsequently, a novel theoretical model is developed that synthesizes key themes, including algorithm bias, algorithm fairness, perceived fairness, individual characteristics, social characteristics, task characteristics, and technology characteristics. The paper contributes proposing a set of propositions that underscore the critical gaps in the existing literature, contribute to a deeper comprehension of the relationships among the identified themes and their constituent elements, and offer a roadmap for future research in the domain.

关键词： algorithmic bias algorithm fairness Perceived fairness Responsible AI AI Ethics Trustworthy AI

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A Harm-Reduction Framework for algorithmic fairness

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IEEE SECURITY & PRIVACY 2018年第3期16卷 34-45页

作者： Altman, Micah Wood, Alexandra Vayena, Effy MIT Res Cambridge MA 02139 USA MIT MIT Lib Program Informat Sci Cambridge MA 02139 USA Harvard Univ Berkman Klein Ctr Internet & Soc Cambridge MA 02138 USA Swiss Fed Inst Technol Zurich Switzerland

In this article, we recognize the profound effects that algorithmic decision-making can have on people"s lives and propose a harm-reduction framework for algorithmic fairness. We argue that any evaluation of algorithmic fairness must consider a counterfactual analysis of the effects that algorithmic design, implementation, and use have on the well-being of individuals.

关键词： Classification algorithms Prediction algorithms Decision making Risk management Law Machine learning algorithms Inference algorithms Ethics Artificial intelligence algorithm design and analysis INSPEC: Controlled Indexing decision making decision support systems ethical aspects INSPEC: Non-Controlled Indexing harm-reduction framework algorithm fairness algorithm decision-making individual well-being Author Keywords data privacy informational harm fairness accountability privacy AI Ethics

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fairness constraint of Fuzzy C-means Clustering improves clustering fairness 13

Fairness constraint of Fuzzy C-means Clustering improves clu...

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13th Asian Conference on Machine Learning (ACML)

作者： Xu, Xia Zhang, Hui Yang, Chunming Zhao, Xujian Li, Bo Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China

Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.

关键词： fair clustering algorithm fairness Fuzzy C-Means

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Efficient fairness Testing Through Hash-Based Sampling 14th

Efficient Fairness Testing Through Hash-Based Sampling

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14th International Symposium on Search-Based Software Engineering (SSBSE)

作者： Zhao, Zhenjiang Toda, Takahisa Kitamura, Takashi Univ Elect Commun Grad Sch Informat & Engn Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9783031212505;9783031212512

There is a growing concern on algorithm fairness, according to wider adoption of machine learning techniques in our daily life. Testing of individual fairness is an approach to algorithm fairness concern. Verification Based Testing (V-BT) is a state-of-the-art testing technique for individual fairness, that leverages verification techniques using constraint solving. In this paper, we develop a black-box individual fairness testing technique V-BT-X, which applies hash-based sampling techniques to the test case generation part of V-BT, aiming to improve its testing ability. Our evaluation by experiments confirms that V-BT-X improves the testing ability of V-BT by 2.92 times in average.

关键词： algorithm fairness fairness testing SAT/SMT solving

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When AI Is Wrong: Addressing Liability Challenges in Women's Healthcare

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JOURNAL OF COMPUTER INFORMATION SYSTEMS 2022年第6期62卷 1310-1319页

作者： Marotta, Angelica MIT Sloan Sch Management 245 First St Cambridge MA 02142 USA

Healthcare professionals can leverage Artificial intelligence (AI) to provide better care for their patients. However, it is also necessary to consider that AI algorithms operate according to historical diagnostic data, which often include evidence gathered from men. The biases of prior practices and the perpetuation of exclusionary processes toward women can lead to inaccurate medical decisions. The ramifications of such errors show that the incorrect use of AI raises several critical questions regarding who should be responsible for potential incidents. This study aims to provide an analysis of the role of AI in affecting women's healthcare and an overview of the liability implications caused by AI mistakes. Finally, this work presents a framework for algorithmic auditing to ensure that AI data are collected and stored according to secure, legal, and fair practices.

关键词： algorithm fairness artificial intelligence liability healthcare

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Detecting Risk of Biased Output with Balance Measures

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ACM JOURNAL OF DATA AND INFORMATION QUALITY 2022年第4期14卷 1-7页

作者： Mecati, Mariachiara Vetro, Antonio Torchiano, Marco Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy

Data have become a fundamental element of the management and productive infrastructures of our society, fuelling digitization of organizational and decision-making processes at an impressive speed. This transition shows lights and shadows, and the "bias in-bias out" problem is one of the most relevant issues, which encompasses technical, ethical, and social perspectives. We address this field of research by investigating how the balance of protected attributes in training data can be used to assess the risk of algorithmic unfairness. We identify four balance measures and test their ability to detect the risk of discriminatory classification by applying them to the training set. The results of this proof of concept show that the indexes can properly detect unfairness of software output. However, we found the choice of the balance measure has a relevant impact on the threshold to consider as risky;further work is necessary to deepen knowledge on this aspect.

关键词： Data quality data bias data ethics algorithm fairness

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CRAFT: Identifying Key Nodes in the Science Communication Community via Counterfactual fairness

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年

作者： Jiang, Wenkang Tang, Qirui Lin, Lei Han, Ye Wang, Runqiang He, Hongbo Chinese Acad Sci Comp Network Informat Ctr Beijing 100083 Peoples R China Univ Chinese Acad Sci Beijing 100083 Peoples R China

The advent of the new media era has greatly changed the original pattern of science communication, and social networks have become a new front to promote the Public Understanding of Science. The integration of social networks and scientific communication has dissolved the central focus of scientific communication, moving toward a more diversified subject centered around the public networks. Due to the special nature of science communication, the key nodes in the community network often play a crucial role. Specifically, by identifying key nodes and analyzing the trend of grouping them into clusters, the stability and robustness of the complex network of science communication can be significantly improved. This is of great significance for expanding the influence of science communication and attracting relevant practitioners to join. However, the sensitive attributes and potential influence factors of community network nodes can lead to deviations in key node mining using existing algorithms. From the perspective of algorithm fairness, we combine graph neural networks and counterfactual theory to address this issue in this work and propose a framework for key node mining for the characteristics of science communication, CounteRfActual fairness discriminaTor (CRAFT). Experimental results on real data demonstrate the prediction and fairness results and the efficacy of CRAFT in learning causal representations of latent factors.

关键词： Cause effect analysis Feature extraction Predictive models Media Knowledge engineering Ethics Prediction algorithms Measurement Machine learning algorithms Data models algorithm fairness causality machine learning science communication social computing

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