Fairness in deep learning imbalance and observation bias. However, fairness in deep clustering has not been well addressed so far. Deep learning (DL) reconstruction particularly of MRI has led to improvements in arXiv:2310. This posits the requirement of algorithmic fairness, which holds that automated decisions should be equitable with respect to protected features (e. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative As deep learning imaging algorithms become ubiquitous across smartphones, social networks, MRI scanners and a broad array of other applications, designing the correct metrics and understanding the foundations of deep learning is In recent years, artificial intelligence technology has been widely used in many fields, such as computer vision, natural language processing and autonomous driving. , participants or cells). Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy. 1 Fairness in Deep Learning: A Computational Perspective Mengnan Du, Fan Yang, Na Zou, Xia Hu Abstract—Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Association for Computing The existing research addressing the topic of fairness in machine learning has focused on how to measure and evaluate fairness (or, equivalently, bias) in models. Hence, fairness protection has come to play a pivotal role in machine learning. First, they assume training samples are independent and identically distributed, an assumption often violated in real-world datasets where samples are grouped by shared measurements (e. Specifically, we show that In this paper, we summarized deep learning based image fairness protection studies in three respects: problems, models, and challenges. A lot of applications today work specifically on face-based data, such as Instagram and Snapchat filters. Various notions and measures of fairness have been proposed to ensure that a decision-making system does not disproportionately harm (or benefit) particular subgroups of Request PDF | DeepFair: Deep Learning for Improving Fairness in Recommender Systems | The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Specifically, we show that In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. 1 Introduction Thanks to the progress in artificial intelligence and machine Fairness in Machine Learning (ML) has emerged as a crucial concern as these models increasingly influence critical decisions in various domains, including healthcare, finance, and criminal justice. By Yiqun Xie, Xiaowei Jia, Weiye Chen, Erhu He. In deep metric learning, a neural network learns the similarity between objects by mapping similar Fairness in machine learning can be categorized according to two dimensions, namely, the task and the type of learning. Fairness in Deep Learning: A Survey on Vision and Language Research 138:5 so-calledinductivebiases. Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. 2019. Specifically, we show that interpretability can Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make blatantly unfair predictions. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. 2021. These classes include Adversarial learning-based algorithms, Meta-analysis of algorithms. To establish connections between fairness issues and various issue mitigation approaches, we propose a taxonomy of machine learning fairness issues and map the diverse range of approaches scholars fair to incorporate a fairness metric like dominant resource fairness [14] for apportioning multi-ple resources fairly. The first fairness analysis in a DL-based brain MRI reconstruction model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. The issue of indirect discrimination in deep learning algorithms has garnered substantial research interest because of its profound implications for output fairness. It is therefore unsurprising that attempts to formalise `fairness' in machine learning contain echoes of these old philosophical debates. NeuralReconciler for Hierarchical Time Series Forecasting. Since conference papers tend to be prioritized over journal publications in computer science (and deep learning especially), conference organizers should strive to improve FairIF stands out by offering a plug-and-play solution, obviating the need for changes in model architecture or the loss function, and indicates superior fairness-utility trade-offs compared to other methods, regardless of bias types or architectural variations. Patt. 09635: A Survey on Bias and Fairness in Machine Learning. As is the case with many ethical Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. Soltan 2,3 , David W. Previous. Deep neural networks are being increasingly used in real world applications (e. LG] 2 Jun 2023 What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective ZEYU TANG,Carnegie Mellon University, United States JIJI ZHANG,The Chinese University of Hong Kong, Hong Kong KUN ZHANG,Carnegie Mellon University, United States We review and reflect on fairness notions proposed in machine Failure to adequately address fairness issues could result in a subgroup of patients receiving inaccurate or under-diagnoses [28, 29, 2], potentially leading to deterioration and causing lifelong harm to the patients. , gender, race). These issues touch upon mathematics, statistics, ethics, and societal impact. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. Pages 721 - 730. In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus low utility. This repository is the official implementation of Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics. However, behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. This work proposes a novel approach to increase a Neural Netwo Machine learning models play an important role for decision-making systems in areas such as hiring, insurance, and predictive policing. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these fairness and present this general novel problem in the context of (deep) reinforcement learning, al-though it could possibly be extended to other ma-chine learning tasks. This has resulted in concerns about the fairness of decisions made by these models. Although fairness has been investigated in the vision-only domain the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. Specifically, we studied 13 state-of-the-art existing techniques for improving the fairness of deep learning models as the representatives, all of which are from the most recent research. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. FFL not only enhances privacy safeguards In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. Sometimes it is because of societal biases reflected in the training data and in the decisions made during the development and deployment of these systems. 7544/ISSN1000-1239. It has been The Ethics of Deep Learning: Addressing Bias and Fairness in AI AI Upbeat: Navigating the Future of Artificial Intelligence deep understanding of the multifaceted issues surrounding fairness and bias in machine learning. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. tal learning by experiments; (ii) We presented a simple and effective solution to address catastrophic forgetting in class incremental learning that maintains both the discrimination via KD and the fairness via WA; (iii) Inspired by a prior ob-servation of a non-incremental model, the proposed method 2. Furthermore, in the recommendation stage, this balance does not require an initial knowledge of the users’ demographic information. Specifically, we show that interpretability The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias. Clifton 1,5 arXiv:2206. Guide to Deep Learning Model Training and Quantization; A Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users is proposed. Specifically, we show that interpretability can We provide a comprehensive review covering existing techniques to tackle algorithmic fairness problems from the computational perspective. [28] present deep methods in terms of the bias found in inputs and representations, while Shi [29] looks at issues of unfairness in deep federated learning methods. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed learning environments. Download Citation | Fairness in Deep Learning: A Survey on Vision and Language Research | Despite being responsible for state-of-the-art results in several computer vision and natural language and the learning process and is an important criterion to consider when auditing models for fairness. In NeurIPS, pages 8026--8037. Heterogeneity-Aware Deep Learning in Space: Performance and Fairness. Addressing issues of fairness requires carefully under-standing the scope and limitations of machine learning tools. Clinicians may face difficulties in placing trust and confidently integrating deep learning methods into their routine practices. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain. Google Scholar [11] Faisal Kamiran, Asim Karim, and Xiangliang Zhang. Machine learning models play an important role for decision-making systems in areas such as hiring, insurance, Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. , gender, ethnicity, sexual orientation, or disability). The machine-learning technique the researchers studied is known as deep metric learning, which is a broad form of representation learning. In parallel to bias mitigation methods, a plethora of fairness notions have been proposed; the interested reader is referred Fairness in machine learning (ML) Hryniewska, W. AI systems can behave unfairly for a variety of reasons. LG] 4 Oct 2023 Under review as a conferencepaper at ICLR 2024 FAIRNESS-ENHANCING MIXED EFFECTS DEEP LEARN- ING IMPROVES FAIRNESS ON IN-AND OUT-OF- DISTRIBUTION CLUSTERED (NON-IID) DATA Adam Wang∗, Son Nguyen ∗& Albert Montillo Lyda Hill Departmentof Bioinformatics The trees seek to obtain the best trade-offs in accuracy and fairness by learning the best combination of the Hyper criteria parameters, maximum depth, minimum number of samples to split a node, total number of leaves and the weight of each class, and provides the best feasible solutions through a Pareto front (Valdivia et al. These improvements have been made possible due to advances in Deep learning has been one of the most sought-after technologies in recent years, especially in the field of artificial intelligence (AI). Empirical loss minimization during machine learning training can inadvertently introduce bias, stemming from Fairness has been a critical issue that affects the adoption of deep learning models in real practice. The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. NEXT CHAPTER. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons 3 ways to make machine learning fair and ethical. In this paper, we will study fairness-preserving deep learning with the emphasis of its theoretical guarantee of fairness. Fairness-Aware Learning. The trend in deep learning models has been toward improving fairness for different demographic subgroups. First, unlike Yarn, DOI: 10. Biases in these models related to fac-tors like race, gender, or socioeconomic status can lead to healthcare disparities and adverse patient outcomes Abstract page for arXiv paper 1908. Specifically, we show that interpretability Machine learning based systems are reaching society at large and in many aspects of everyday life. Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. Hence ‘Fairness’ in generative models is an increasingly crucial concept. surveillance, face recognition). Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. BiasinNLPwithdeeplearning Whenwetalkaboutbiasinlanguagemodels,wecanapproachitasarepresentationalprob-lem[25 In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. To provide a comprehensive and fair comparison, we adopted three widely-used benchmarks Fairness has received increasing interest in deep learning in recent years. FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes. As the The question of considering fairness and bias in machine learning models is crucial. Google Scholar [43] Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg and Michael Carl Tschantz. However, these tremendous performances are reported on test-sets which are drawn from distribution Contribute to charan223/FairDeepLearning development by creating an account on GitHub. et al. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed ness in machine learning [12, 36], with a primary emphasis on proposing formal notions of fairness [22, 56] and “de-biasing” tech-niques to achieve these goals [2, 17]. This problem can be addressed by Distributed or Federated Learning (FL) that Deep neural networks are being increasingly used in real world applications (e. emphasizes the need for fairness by providing examples of potential consequences in the actual world, and examines the definitions of fairness and bias offered by researchers in other domains, such as general machine learning, deep learning, and natural language processing, albeit the strategy for selecting the articles was not Request PDF | On Mar 4, 2024, Haonan Wang and others published FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes | Find, read and cite all Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Therefore, fairness in deep learning has attracted tremendous attention recently. Group fairness requires that the DL model should have equal utilities ness in machine learning [12, 36], with a primary emphasis on proposing formal notions of fairness [22, 56] and “de-biasing” tech-niques to achieve these goals [2, 17]. 04101v2 [cs. Framing sources of bias necessitates deep understanding of the application at hand and, typically, can only be identified after a “post-mortem” analysis of the predicted outcome. This is vital, especially in healthcare, where these deep learning models influence diagnoses and treat-ment decisions. Rising acceptance of machine learning driven decision support systems underscores the need for ensuring fairness for all stakeholders. Request PDF | On Dec 1, 2020, Wentao Xie and others published Fairness Testing of Machine Learning Models Using Deep Reinforcement Learning | Find, read and cite all the research you need on Deep learning is an important field of machine learning research, which is widely used in industry for its powerful feature extraction capabilities and advanced performance in many applications. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. An in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research is provided and a novel taxonomy that builds upon previous proposals but is tailored for deep learning research is proposed. , they disproportionately benet or harm certain subgroups (often a sub-population that shares a fair to incorporate a fairness metric like dominant resource fairness [14] for apportioning multi-ple resources fairly. The performance of automatic face recognition has been boosted during the last decade, achieving very competitive accuracies in the most challenging scenarios [7]. In Proceedings of the 30th Annual Conference on Neural Information Processing Systems, pages 325-333, 2016. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. For those working in data science and artificial intelligence with algorithms, there are a few ways to make sure that machine learning is fair and ethical. Du et al. To deal with the problem, we propose a deep-Q reinforcement learning The proposed technique achieves near optimal values of fairness while offering primary outage probability of less than 4%. It doesn’t Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate local model updates. 1 Related Work Fairness in ML. Understanding Bias in Deep Learning. However, direct applications of deep learning often result in spatially biased predictions due to common data . JOURNAL OF LATEX CLASS FILES, VOL. Traditional deep learning (DL) models face two key challenges. Addressing it directly, such as removing sensitive attributes during training, is inadequate to guarantee equality due to its intricate causes (Castelnovo et al . However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. 8, AUGUST 2024 1 Impact of Data Distribution on Fairness Guarantees in Equitable Deep Learning Yan Luo*, Member, IEEE, Congcong Wen*, Member, IEEE, Min Shi, Member, IEEE, Hao Huang, Yi Fang†, Member, IEEE, and Mengyu Wang†, Member, IEEE Abstract—We present a comprehensive theoretical Request PDF | On Mar 3, 2021, Vedant Nanda and others published Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning | Find, read and cite all the research you need on Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. Our Mehrabi et al. Book Handbook of Geospatial Artificial Intelligence. Measuring non-expert comprehension of machine learning fairness metrics. There has been much work on improving generalization of DML in settings like zero-shot retrieval, Improving the Fairness of Chest X-ray Classifiers; Improving Subgroup Robustness via Data Selection; Test-Time Debiasing of Vision-Language Embeddings; Challenges of Differentially Private Prediction in Healthcare Settings; S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Nowadays, recommendations come to us from a Estimated module length: 110 minutes Evaluating a machine learning model (ML) responsibly requires doing more than just calculating overall loss metrics. Despite being responsible for state-of-the-art results in several computer vision and natural language Due to the complexity of deep learning models, interpretability research has developed diversely, and many methods have been used to interpret how a deep learning model works from various aspects Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Decision theory for discrimination-aware classification. Thus, in this section, we present the basics of group fairness for better comprehension. Training datasets can contain both class imbalance and To tackle this issue, we advocate for the use of social welfare functions that encode fairness and present this general novel problem in the context of (deep) reinforcement learning, although it could possibly be and the learning process and is an important criterion to consider when auditing models for fairness. , 2021). This trade-off makes it difficult for AI systems to achieve true intelligence and is connected to generalization, robustness, and fairness in deep learning. fairness or a quick technical fix to society’s concerns with automated decisions. AI-generated images are now widely used in various industries, including the Keywords: Recommender Systems, Collaborative Filtering, Deep Learning, Fairness, Social Equality. However, underlying fairness issues in machine learning systems can pose risks to individual The issue of indirect discrimination in deep learning algorithms has garnered substantial research interest because of its profound implications for output fairness. To improve model fairness, many existing methods have been proposed and evaluated to be effective in their own contexts. In recent years, it has been revealed that machine learning models can produce discriminatory predictions. , 2022 ) . Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. This has resulted in concerns about the fairness of decisions made by these models. This leads to performance degradation, limited generalization, and confounding issues, This paper presents a systematic approach of testing the fairness of a machine learning model, with individual discriminatory inputs generated automatically in an adaptive manner based on the state-of-the-art deep reinforcement learning techniques. While big-data schedulers like Yarn [38] also support user fairness, there are two key differences between big-data jobs and DLT jobs that make big-data schedulers unsuitable for the deep learning setting. However, FL falls short in terms of fairness, as each client receives the same model regardless of their individual contributions. Further, increasing the number of STs results in a linear increase in the computational complexity of the proposed framework. Fairness is a critical concern in deep learning especially in healthcare where these models influence diagnoses and treatment decisions. Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum Fairness in deep learning models trained with high-dimensional inputs and subjective labels remains a complex and understudied area. It is important to ensure that the negative biases, and prejudices that plague our society, which are otherwise irrelevant to the decision-making process, should not get transferred into the machine learning systems. In this study, we introduced a method for training fair, unbiased ML models, based on a deep RL framework. You can: Examine the algorithms’ ability to influence human behavior and decide whether it is biased. . The presence of bias in ML systems can lead to unfair and discriminatory outcomes, undermining the reliability and ethical standards of these technologies. S. Among them, group fairness is used by most of the studies in DL-based MedIA. Secondly, we strive For example, deep learning models used to detect 14 common diseases from chest X-rays substantially were found to under-diagnose intersectional under-served subgroups, such as Hispanic female patients, potentially resulting in treatment delays if deployed in practice [seyyed-kalantari_underdiagnosis_2021]. Bias in deep learning can arise from various sources and manifest in different forms, each affecting machine Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. First, unlike Yarn, Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Therefore, fairness in deep learning has attracted tremendous Addressing fairness issues in deep learning-based medical image analysis: a systematic review Check for updates Zikang Xu 1,2,JunLi3, Qingsong Yao3,HanLi1,2,MingyueZhao1,2 & S. • Computing methodologies →Machine learning algorithms. Introduction Fairness in Recommender System (RS) is a very important issue, since it is part of the path to get a fair society. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. As increasingly more attention and importance is given to algorithmic fairness by machine learning researchers and practitioners, with new for improving the fairness of deep learning models. In International Conference on Machine Learning (ICML), 2020 Machine vision in general and face recognition algorithms, in particular, are good examples of recent advances in AI [3], [4], [5], [6]. 0 2,153 5 minutes read. Models that learn from historic data have been shown to exhibit unfairness, i. Fairness in Deep Learning Juma Karoli Send an email September 29, 2024 Last Updated: December 22, 2024. Therefore, fairness in deep learning has attracted tremendous However, there are few implemented protocols concerning fair research in mainstream deep learning conferences, and deep learning papers often do not discuss such limitations. This book offers a critical take on current practice of machine learning as well as proposed technical fixes for achieving fairness. The work in [ 5 ] introduced the definition of individual fairness, which contrasts with group-based notions of fairness [ 3 , 10 ] that require demographic groups to be treated similarly on average. RS fairness has been even less covered in Deep Learning (DL) than in machine learning; as an example, in this current survey of RS based on DL [24] the fairness goal is not mentioned, not even in its “possible research directions” section. The same happens with the current review paper [25] where fairness is not Fairness in learning: Classic and contextual bandits. 03146v1 [cs. Recent advances in deep learning have shown promise in discovering complex patterns from spatial datasets and greatly enhanced our ability to address some of the most critical societal problems, such as agricultural monitoring and natural disaster detection. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Therefore, fairness in deep learning has attracted tremendous Fairness in machine learning is essential to ensure that algorithms treat all groups equitably and do not perpetuate or exacerbate existing biases. We will also evaluate and validate our theoretical using benchmark dataset for fairness machine learning. in WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining. However, there has been limited work on investigating how unfair performance the fairness issues underlying in those applications are yet to be addressed extensively by machine learning community. The biases and their types that percolate the deep learning models and are reflected in their results are discussed, including FairGAN and FairGAN+, two recently proposed generative models that aim to generate fair, accurate and diverse data, including their strengths and limitations. Fairness is one of the most critical properties of these machine learning models, while individual discriminatory cases may break the trustworthiness of these systems severely. 08/23/19 - Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Click here to Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. We Pytorch: An imperative style, high-performance deep learning library. This module looks at different types of human biases that can manifest in training data. , they disproportionately benefit or harm certain subgroups (often a sub-population that shares a Real-world Requirements for Fairness: Deep learning algorithms have a major impact on our day-to-day lives, and this impact will surely grow in the coming years. By understanding the types of biases that can occur, their causes, and the strategies to mitigate them, we can work towards developing more fair and inclusive machine learning systems. This review paper discusses the biases and their types that percolate the deep learning models and are reflected in their results. , they disproportionately benefit or harm certain subgroups (often a sub-population that shares a Machine learning based systems are reaching society at large and in many aspects of everyday life. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Inconvolutionalneuralnetworks,forinstance,theimplementedinduc- Fairness focuses on treating different groups equally. Fairness in Deep Learning. Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies. In March 2018, we convened a group of experts as part of a CCC visioning workshop to Recent developments of deep learning have demonstrated promising results for challenging tasks in computer vision, natural language Performance and Fairness. The paper then presents a comprehensive overview of FairGAN and FairGAN+, fairness protection with deep models, Malik and Singh [27] discuss general deep learning technology, offering an introduction to unfair interpretation. Fairness in machine learning is an increasingly important topic as machine learning models are being used in a wide range of applications, from lending and hiring decisions to criminal justice systems. Facial emotion recognition, Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e. Mentioning: 74 - Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Facebook X LinkedIn Tumblr Pinterest Reddit WhatsApp. The same happens with the current review paper [25] where fairness is not Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the training stage. Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general for clinical machine learning with deep reinforcement learning Jenny Yang 1 , Andrew A. Specifically, we show that interpretability can Wang, H, Wu, Z & He, J 2024, FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes. Since image inputs are different 1 Fairness in Deep Learning: A Computational Perspective Mengnan Du, Fan Yang, Na Zou, Xia Hu Abstract—Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Federated learning (FL) has emerged as a promising framework for collaborative machine learning, allowing the training of machine learning models on distributed devices without centralizing sensitive data. e. KEYWORDS Fairness; Deep Learning; Influence Function ∗Both authors contributed equally to this research. In this paper, we take the first steps towards evaluating a more holistic approach by testing for fairness both before and after model training. Deep learning has significantly improved classification accuracy for supervised image recognition tasks. g. Kevin Zhou1,2,3,4 into individual fairness [33], group fairness [36], max-min fairness [37], counterfactual fairness [38], etc. The proposed techniques are primarily constituted by deep learning techniques, which are subdivided into popular classes of algorithms. To provide a comprehensive and fair comparison, we adopted three widely-used benchmarks that involve diverse image classification tasks related to different sensitive attributes. This survey provides an overview of the triangular trade-off among robustness, accuracy, and fairness in neural networks. However, due to the bias in training data labeling and model design, research shows that deep learning may aggravate human bias and discrimination in some applications, which results in ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics Beyond accuracy, deep learning models are emerging to perform some innovative tasks, such as improving fairness, where the DeepFair model [19] gets a trade-off between equity and precision; green Fairness in Machine Learning# Fairness of AI systems#. Eyre 4 & David A. 20200758 Corpus ID: 234076696; Fairness Research on Deep Learning @article{Jinyin2021FairnessRO, title={Fairness Research on Deep Learning}, author={Chen Jinyin and Chen Yipeng and Chen Yiming and Zheng Haibin and Ji Shouling and Shi Jie and Cheng Yao}, journal={Journal of Computer Research and Development}, imbalance and observation bias. ness in machine learning [12, 36], with a primary emphasis on proposing formal notions of fairness [22, 56] and “de-biasing” tech-niques to achieve these goals [2, 17]. However, there is still no systematic evaluation among them for a comprehensive comparison under the same context, which makes it hard to and the learning process and is an important criterion to consider when auditing models for fairness. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. 1. PREVIOUS CHAPTER. 14, NO. However, it still remains a challenge to guarantee their trustworthiness. Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Various notions and Defining fairness. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair pair-wise similarity deep clustering [6], deep learning with spectral clustering [20], minimizing relative entropy [11], deep transfer clustering [13], and deep clustering with gen-erative model [26], adversarial learning [30], and Gaussian mixture model [35]. We evaluated it on two complex, real-world tasks—screening for COVID-19 and predicting Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. Digital Library. The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. A growing body of work has been proposed over the last years to address the problem of fairness and algorithmic discrimination [] against demographic groups defined on the basis of protected attributes like gender or race. Machine learning algorithms, as the core technique of AI, have significantly facilitated people’s lives. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning To prevent models from incurring unfair decision-making, the AI community has concentrated efforts on correcting algorithmic biases, giving rise to the research area now widely known as We provide a comprehensive review covering existing techniques to tackle algorithmic fairness problems from the computational perspective.
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