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Towards Fair Deep Clustering With Multi-State Protected Variables
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<span title="2019-01-29">2019</span>
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To this end, we propose Deep Fair Clustering, which learns a discriminative but fair cluster assignment function. ...
Fair clustering under the disparate impact doctrine requires that population of each protected group should be approximately equal in every cluster. ...
Conclusion In this work, we define the fair clustering problem with multi-state protected variable under group fairness and disparate impact doctrines. ...
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Algorithm Fairness in AI for Medicine and Healthcare
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<span title="2022-03-24">2022</span>
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arXiv
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, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. ...
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. ...
Similar to LAFTR are methods in unsupervised fair clustering, which further evaluates the fairness of debiased representations (via adversarial learning) in achieving attribute-invariant cluster assignments ...
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2020 Index IEEE Transactions on Smart Grid Vol. 11
<span title="">2020</span>
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., Small-Signal Stability Analysis and Active Damping Control of DC Microgrids Integrated With Distributed Electric Springs; 3737-3747 Hou, K., see Liu, X., TSG Nov. 2020 5431-5441 Hou, Y., see Liang ...
., +, TSG Jan. 2020 429-439 Towards Plug-and-Play Protection for Meshed Distribution Systems With DG. ...
., +, TSG March 2020 1296-1306 Towards Plug-and-Play Protection for Meshed Distribution Systems With DG. ...
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Testing Data Feminism in India
<span title="2021-05-25">2021</span>
<i title="SASPR Edu International Pvt. Ltd">
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Urban The second cluster has the highest variability. ...
Moreover, multi-dimensional data i.e. inclusive of images, songs, and textual content have layers of data points, making it hard to comprehensively label every variable. ...
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Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics
<span title="2021-02-02">2021</span>
<i title="Springer Science and Business Media LLC">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rcs76v6h7jf2lnaoy2wi3gg4ny" style="color: black;">Neural Processing Letters</a>
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Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data ...
For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to ...
contributed for diagnosis of Dengue Fever ( Fig. 5 and 6) , infectious diseases having Covid-19 (Fig. 8) or Diabetes and its comorbidities (as in Fig. 11 , 13 and 14) holds a lot of worth to come up with ...
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An Overview of Fairness in Clustering
<span title="">2021</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
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In case labels are available for the groundtruth clusters, deep clustering has been shown to achieve state-of-the-art performance when compared with traditional clustering approaches such as k-means [ ...
Anagnostopoulos et al [131] [132] extended the work of [100] , to the densest subgraph problem.
4) Deep Clustering The first work combining deep clustering with fairness was proposed by Wang and Davidson ...
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Global Transformation Towards Sustainability—Clusters of Current Scholarly Discourse
[chapter]
<span title="2020-12-01">2020</span>
<i title="Springer Fachmedien Wiesbaden">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qj75ecgmkbacfjypbsvfnwtj5e" style="color: black;">Die Chinapolitik der Bundesrepublik Deutschland nach der Wiedervereinigung</a>
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AbstractStudying global transformation towards sustainability is a colossal task. ...
The difficulties in determining the scope, boundaries and levels of the analysis have ramifications for the conceptual value of attempts to understand global transformation towards sustainability. ...
There is still, however, the need to move one step 'sideways' and link this newly acquired power with a debate about justice and fairness. ...
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Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition
<span title="2021-08-14">2021</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a>
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For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. ...
fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. ...
For this purpose, in this work, we show that a particularly simple yet effective constraint for learning fair representation [64, 77] can be combined with deep models and HE to deliver deep, fair and ...
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A Survey on Bias and Fairness in Machine Learning
[article]
<span title="2022-01-25">2022</span>
<i >
arXiv
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In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to ...
With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. ...
Here s denotes the protected or the sensitive variable, and we adapted the same notation as in [156] .
Fair NLP 5.4.1 Word Embedding. ...
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Harnessing value from data science in business: ensuring explainability and fairness of solutions
[article]
<span title="2021-08-10">2021</span>
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arXiv
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For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations. ...
The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. ...
Calders and Verwer (2010) proposed a Two Naive Bayes technique, which trains distinct models for each protected attribute and iteratively modifies the observed probabilities towards the selected fairness ...
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Experiments on Generalizability of User-Oriented Fairness in Recommender Systems
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<span title="2022-05-17">2022</span>
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arXiv
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Relevant work on user-oriented fairness highlights the discriminative behavior of fairness-unaware recommendation algorithms towards a certain user group, defined based on users' activity level. ...
Typical solutions include proposing a user-centered fairness re-ranking framework applied on top of a base ranking model to mitigate its unfair behavior towards a certain user group i.e., disadvantaged ...
In contrast, fairness on two-sided markets [39, 41, 42, 45] aims to protect not only the protected users, but also some item groups, leading to a fairer approach towards certain content providers (e.g ...
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Metadata Normalization
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<span title="2021-05-05">2021</span>
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While these techniques normalize feature distributions by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. ...
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. ...
, 54] introducing methods to apply fair and invariant representation learning to continuous protected variables. ...
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Bias and unfairness in machine learning models: a systematic literature review
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<span title="2022-03-18">2022</span>
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One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. ...
The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. ...
gremlin: Social networking, machine learning and the global war on al-qaida-and is-inspired terrorism [7] 3 Fairness research on deep learning [23] 4 Mdfa: Multi-differential fairness auditor for ...
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Differential Evolution Algorithm with Hierarchical Fair Competition Model
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This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). ...
After analyzing the results, the two variants of HFC-DE are proposed, named hierarchical fair competition model in differential evolution algorithm with replacement, I,e, HFCDE-R and hierarchical fair ...
Hence, two new algorithms name hierarchical fair competition model in differential evolution algorithm with replacement, I,e, HFCDE-R for multi-model functions and hierarchical fair competition model in ...
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Table of Contents
<span title="2020-10-30">2020</span>
<i title="IEEE">
2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2)
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in Renewable Power Grid
1204
Po01-07
Research on Clustering Algorithm of Load Decomposition Considering Harmonic
Characteristics in Power Safety Monitoring
1210
Po01-08
Indicator State Recognition ...
on Deep Deterministic Policy Gradient
476
Pa14-06
Dispatching Methods and Strategies for Controllable Charging Pile Clusters To Participate in
Grid Ancillary Services
482
Pa15-01
Robust Dispatch ...
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