Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00108
Jun Huang, Yu Yan, Xiao Zheng, Xiwen Qu, Xudong Hong
A multi-label learning (MLL) method can simul-taneously process the instances with multiple labels, and many well-known methods have been proposed to solve various MLL-related problems. The existing MLL methods are mainly applied under the assumption of a fixed label set, i.e., the class labels are all observed for the training data. However, in many real-world applications, there may be some unknown labels outside of this set, especially for large-scale and complex datasets. In this paper, a multi-label classification model based on deep learning is proposed to discover the unknown labels for multi-label image classification. It can simultaneously predict known and unknown labels for unseen images. Besides, an attention mechanism is introduced into the model, where the attention maps of unknown labels can be used to observe the corresponding objects of an image and to get the semantic information of these unknown labels.
{"title":"Discovering Unknown Labels for Multi-Label Image Classification","authors":"Jun Huang, Yu Yan, Xiao Zheng, Xiwen Qu, Xudong Hong","doi":"10.1109/ICDMW58026.2022.00108","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00108","url":null,"abstract":"A multi-label learning (MLL) method can simul-taneously process the instances with multiple labels, and many well-known methods have been proposed to solve various MLL-related problems. The existing MLL methods are mainly applied under the assumption of a fixed label set, i.e., the class labels are all observed for the training data. However, in many real-world applications, there may be some unknown labels outside of this set, especially for large-scale and complex datasets. In this paper, a multi-label classification model based on deep learning is proposed to discover the unknown labels for multi-label image classification. It can simultaneously predict known and unknown labels for unseen images. Besides, an attention mechanism is introduced into the model, where the attention maps of unknown labels can be used to observe the corresponding objects of an image and to get the semantic information of these unknown labels.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126573765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00064
A. A. Neloy, M. Turgeon
Deep learning (DL) based natural language processing (NLP) has recently grown as one the fastest research domain and retained remarkable improvement in many applications. Due to the significant amount of data, the adaptation of feature learning and symmetric data efficiency is a critical underlying task in such applications. However, their ability to extract features is limited due to a lack of proper model formation. Moreover, the use of these methods on smaller datasets is unexplored and underdeveloped compared to more popular research areas. This work introduces a two-stage modeling approach to combine classical statistical analysis with NLP problems in a real-world dataset. We effectively layout a combination of the classical statistical model incorporating a stacked ensemble classifier and a DL framework of convolutional neural network (CNN) and Bidirectional Recurrent Neural Networks (Bi-RNN) to structure a more decomposed architecture with lower computational complexity. Additionally, the experimental results illustrating 96.69 % training and 70.56 % testing accuracy and hypothesis testing from our DL models followed by an ablation study empirically demonstrate the validation of our proposed combined modeling technique.
{"title":"Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling","authors":"A. A. Neloy, M. Turgeon","doi":"10.1109/ICDMW58026.2022.00064","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00064","url":null,"abstract":"Deep learning (DL) based natural language processing (NLP) has recently grown as one the fastest research domain and retained remarkable improvement in many applications. Due to the significant amount of data, the adaptation of feature learning and symmetric data efficiency is a critical underlying task in such applications. However, their ability to extract features is limited due to a lack of proper model formation. Moreover, the use of these methods on smaller datasets is unexplored and underdeveloped compared to more popular research areas. This work introduces a two-stage modeling approach to combine classical statistical analysis with NLP problems in a real-world dataset. We effectively layout a combination of the classical statistical model incorporating a stacked ensemble classifier and a DL framework of convolutional neural network (CNN) and Bidirectional Recurrent Neural Networks (Bi-RNN) to structure a more decomposed architecture with lower computational complexity. Additionally, the experimental results illustrating 96.69 % training and 70.56 % testing accuracy and hypothesis testing from our DL models followed by an ablation study empirically demonstrate the validation of our proposed combined modeling technique.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116606562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00074
F. Piccialli, F. Giampaolo, Vincenzo Schiano Di Cola, Federico Gatta, Diletta Chiaro, E. Prezioso, Stefano Izzo, S. Cuomo
Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.
{"title":"A machine learning-based approach for mercury detection in marine waters","authors":"F. Piccialli, F. Giampaolo, Vincenzo Schiano Di Cola, Federico Gatta, Diletta Chiaro, E. Prezioso, Stefano Izzo, S. Cuomo","doi":"10.1109/ICDMW58026.2022.00074","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00074","url":null,"abstract":"Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00096
Udesh Kumarasinghe, Mohamed Nabeel, K. de Zoysa, K. Gunawardana, Charitha Elvitigala
Graph neural networks (GNNs) have achieved re-markable success in many application domains including drug discovery, program analysis, social networks, and cyber security. However, it has been shown that they are not robust against adversarial attacks. In the recent past, many adversarial attacks against homogeneous GNNs and defenses have been proposed. However, most of these attacks and defenses are ineffective on heterogeneous graphs as these algorithms optimize under the assumption that all edge and node types are of the same and further they introduce semantically incorrect edges to perturbed graphs. Here, we first develop, HetePR-BCD, a training time (i.e. poisoning) adversarial attack on heterogeneous graphs that outperforms the start of the art attacks proposed in the literature. Our experimental results on three benchmark heterogeneous graphs show that our attack, with a small perturbation budget of 15 %, degrades the performance up to 32 % (Fl score) compared to existing ones. It is concerning to mention that existing defenses are not robust against our attack. These defenses primarily modify the GNN's neural message passing operators assuming that adversarial attacks tend to connect nodes with dissimilar features, but this assumption does not hold in heterogeneous graphs. We construct HeteroGuard, an effective defense against training time attacks including HetePR-BCD on heterogeneous models. HeteroGuard outperforms the existing defenses by 3–8 % on Fl score depending on the benchmark dataset.
{"title":"HeteroGuard: Defending Heterogeneous Graph Neural Networks against Adversarial Attacks","authors":"Udesh Kumarasinghe, Mohamed Nabeel, K. de Zoysa, K. Gunawardana, Charitha Elvitigala","doi":"10.1109/ICDMW58026.2022.00096","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00096","url":null,"abstract":"Graph neural networks (GNNs) have achieved re-markable success in many application domains including drug discovery, program analysis, social networks, and cyber security. However, it has been shown that they are not robust against adversarial attacks. In the recent past, many adversarial attacks against homogeneous GNNs and defenses have been proposed. However, most of these attacks and defenses are ineffective on heterogeneous graphs as these algorithms optimize under the assumption that all edge and node types are of the same and further they introduce semantically incorrect edges to perturbed graphs. Here, we first develop, HetePR-BCD, a training time (i.e. poisoning) adversarial attack on heterogeneous graphs that outperforms the start of the art attacks proposed in the literature. Our experimental results on three benchmark heterogeneous graphs show that our attack, with a small perturbation budget of 15 %, degrades the performance up to 32 % (Fl score) compared to existing ones. It is concerning to mention that existing defenses are not robust against our attack. These defenses primarily modify the GNN's neural message passing operators assuming that adversarial attacks tend to connect nodes with dissimilar features, but this assumption does not hold in heterogeneous graphs. We construct HeteroGuard, an effective defense against training time attacks including HetePR-BCD on heterogeneous models. HeteroGuard outperforms the existing defenses by 3–8 % on Fl score depending on the benchmark dataset.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00103
Yu Wang, Tyler Derr
Link prediction is a fundamental problem for network-structured data and has achieved unprecedented success in many real-world applications. Despite the significant progress being made towards improving its performance by characterizing underlined topological patterns or leveraging representation learning, few works have focused on the imbalanced performance among nodes of different degrees. In this paper, we propose a novel problem, degree-related bias and evaluation bias, on link prediction with an emphasis on recommender system applications. We first empirically demonstrate the performance differ-ence among nodes with different degrees and then theoretically prove that Recall is an unbiased evaluation metric compared with Fl, NDCG and Precision. Furthermore, we show that under the unbiased evaluation metric Recall, low-degree nodes tend to have higher performance than high-degree nodes in link prediction.
{"title":"Degree-Related Bias in Link Prediction","authors":"Yu Wang, Tyler Derr","doi":"10.1109/ICDMW58026.2022.00103","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00103","url":null,"abstract":"Link prediction is a fundamental problem for network-structured data and has achieved unprecedented success in many real-world applications. Despite the significant progress being made towards improving its performance by characterizing underlined topological patterns or leveraging representation learning, few works have focused on the imbalanced performance among nodes of different degrees. In this paper, we propose a novel problem, degree-related bias and evaluation bias, on link prediction with an emphasis on recommender system applications. We first empirically demonstrate the performance differ-ence among nodes with different degrees and then theoretically prove that Recall is an unbiased evaluation metric compared with Fl, NDCG and Precision. Furthermore, we show that under the unbiased evaluation metric Recall, low-degree nodes tend to have higher performance than high-degree nodes in link prediction.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133810451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00153
Avi Chawla, Nidhi Mulay, M. Bahrami, Vikas Bishnoi, Yatin Katyal, Esteban Moro Egido, Ankur Saraswat, A. Pentland
The COVID-19 pandemic has impacted economic activity not only in the United States, but across the globe. Lockdown and travel restrictions imposed by local authorities have led to change in customer preferences and thus transformation of economic activity from traditional areas to new regions. While most changes have been temporary and short term, some of them have been observed to be of permanent nature. Using large-scale aggregated and anonymized transaction data across various socio-economic groups, we analyse and discuss such temporary relocation of citizens' economic activities in metropolitan areas of 15 states in the US. The results of this study have extensive implications for urban planners and business owners, and can provide insights into the temporary relocation of economic activities resulting from an extreme exogenous shock like the COVID-19 pandemic.
{"title":"Post-pandemic Economic Transformations in the United States of America","authors":"Avi Chawla, Nidhi Mulay, M. Bahrami, Vikas Bishnoi, Yatin Katyal, Esteban Moro Egido, Ankur Saraswat, A. Pentland","doi":"10.1109/ICDMW58026.2022.00153","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00153","url":null,"abstract":"The COVID-19 pandemic has impacted economic activity not only in the United States, but across the globe. Lockdown and travel restrictions imposed by local authorities have led to change in customer preferences and thus transformation of economic activity from traditional areas to new regions. While most changes have been temporary and short term, some of them have been observed to be of permanent nature. Using large-scale aggregated and anonymized transaction data across various socio-economic groups, we analyse and discuss such temporary relocation of citizens' economic activities in metropolitan areas of 15 states in the US. The results of this study have extensive implications for urban planners and business owners, and can provide insights into the temporary relocation of economic activities resulting from an extreme exogenous shock like the COVID-19 pandemic.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134496827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00052
Honggang Zhao, Wenlu Wang
Many factors impact trait prediction from genotype data. One of the major confounding factors comes from the presence of population structure among sampled individuals, namely population stratification. When exists, it will lead to biased quantitative phenotype prediction, therefore hampering the unambiguous conclusions about prediction and limiting the downstream usage like disease evaluation or epidemiology survey. Population stratification is an implicit bias that can not be easily removed by data preprocessing. With the purpose of training a phenotype prediction model, we propose an adversarial training framework that ensures the genomics encoder is agnostic to sample populations. For better generalization, our adversarial training framework is orthogonal to the genomics encoder and phenotype prediction model. We experimentally ascertain our debiasing framework by testing on a real-world yield (phenotype) prediction dataset with soybean genomics. The developed frame-work is designed for general genomic data (e.g., human, livestock, and crops) while the phenotype can be either continuous or categorical variables.
{"title":"Adversarial Removal of Population Bias in Genomics Phenotype Prediction","authors":"Honggang Zhao, Wenlu Wang","doi":"10.1109/ICDMW58026.2022.00052","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00052","url":null,"abstract":"Many factors impact trait prediction from genotype data. One of the major confounding factors comes from the presence of population structure among sampled individuals, namely population stratification. When exists, it will lead to biased quantitative phenotype prediction, therefore hampering the unambiguous conclusions about prediction and limiting the downstream usage like disease evaluation or epidemiology survey. Population stratification is an implicit bias that can not be easily removed by data preprocessing. With the purpose of training a phenotype prediction model, we propose an adversarial training framework that ensures the genomics encoder is agnostic to sample populations. For better generalization, our adversarial training framework is orthogonal to the genomics encoder and phenotype prediction model. We experimentally ascertain our debiasing framework by testing on a real-world yield (phenotype) prediction dataset with soybean genomics. The developed frame-work is designed for general genomic data (e.g., human, livestock, and crops) while the phenotype can be either continuous or categorical variables.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133327903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00156
Hao Tian, Shengmin Jin, R. Zafarani
With the demand to model the relationships among three or more entities, higher-order networks are now more widespread across various domains. Relationships such as multiauthor collaborations, co-appearance of keywords, and copurchases can be naturally modeled as higher-order networks. However, due to (1) computational complexity and (2) insufficient higher-order data, exploring higher-order networks is often limited to order-3 motifs (or triangles). To address these problems, we explore and quantify similarites among various network orders. Our goal is to build relationships between different network orders and to solve higher-order problems using lower-order information. Similarities between different orders are not comparable directly. Hence, we introduce a set of general cross-order similarities, and a measure: subedge rate. Our experiments on multiple real-world datasets demonstrate that most higher-order networks have considerable consistency as we move from higher-orders to lower-orders. Utilizing this discovery, we develop a new cross-order framework for higher-order link prediction method. These methods can predict higher-order links from lower-order edges, which cannot be attained by current higher-order methods that rely on data from a single order.
{"title":"Exploiting Cross-Order Patterns and Link Prediction in Higher-Order Networks","authors":"Hao Tian, Shengmin Jin, R. Zafarani","doi":"10.1109/ICDMW58026.2022.00156","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00156","url":null,"abstract":"With the demand to model the relationships among three or more entities, higher-order networks are now more widespread across various domains. Relationships such as multiauthor collaborations, co-appearance of keywords, and copurchases can be naturally modeled as higher-order networks. However, due to (1) computational complexity and (2) insufficient higher-order data, exploring higher-order networks is often limited to order-3 motifs (or triangles). To address these problems, we explore and quantify similarites among various network orders. Our goal is to build relationships between different network orders and to solve higher-order problems using lower-order information. Similarities between different orders are not comparable directly. Hence, we introduce a set of general cross-order similarities, and a measure: subedge rate. Our experiments on multiple real-world datasets demonstrate that most higher-order networks have considerable consistency as we move from higher-orders to lower-orders. Utilizing this discovery, we develop a new cross-order framework for higher-order link prediction method. These methods can predict higher-order links from lower-order edges, which cannot be attained by current higher-order methods that rely on data from a single order.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132817409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00027
Furkan Gursoy, I. Kakadiaris
As artificial intelligence plays an increasingly substantial role in decisions affecting humans and society, the accountability of automated decision systems has been receiving increasing attention from researchers and practitioners. Fairness, which is concerned with eliminating unjust treatment and discrimination against individuals or sensitive groups, is a critical aspect of accountability. Yet, for evaluating fairness, there is a plethora of fairness metrics in the literature that employ different perspectives and assumptions that are often incompatible. This work focuses on group fairness. Most group fairness metrics desire a parity between selected statistics computed from confusion matrices belonging to different sensitive groups. Generalizing this intuition, this paper proposes a new equal confusion fairness test to check an automated decision system for fairness and a new confusion parity error to quantify the extent of any unfairness. To further analyze the source of potential unfairness, an appropriate post hoc analysis methodology is also presented. The usefulness of the test, metric, and post hoc analysis is demonstrated via a case study on the controversial case of COMPAS, an automated decision system employed in the US to assist judges with assessing recidivism risks. Overall, the methods and metrics provided here may assess automated decision systems' fairness as part of a more extensive accountability assessment, such as those based on the system accountability benchmark.
{"title":"Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems","authors":"Furkan Gursoy, I. Kakadiaris","doi":"10.1109/ICDMW58026.2022.00027","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00027","url":null,"abstract":"As artificial intelligence plays an increasingly substantial role in decisions affecting humans and society, the accountability of automated decision systems has been receiving increasing attention from researchers and practitioners. Fairness, which is concerned with eliminating unjust treatment and discrimination against individuals or sensitive groups, is a critical aspect of accountability. Yet, for evaluating fairness, there is a plethora of fairness metrics in the literature that employ different perspectives and assumptions that are often incompatible. This work focuses on group fairness. Most group fairness metrics desire a parity between selected statistics computed from confusion matrices belonging to different sensitive groups. Generalizing this intuition, this paper proposes a new equal confusion fairness test to check an automated decision system for fairness and a new confusion parity error to quantify the extent of any unfairness. To further analyze the source of potential unfairness, an appropriate post hoc analysis methodology is also presented. The usefulness of the test, metric, and post hoc analysis is demonstrated via a case study on the controversial case of COMPAS, an automated decision system employed in the US to assist judges with assessing recidivism risks. Overall, the methods and metrics provided here may assess automated decision systems' fairness as part of a more extensive accountability assessment, such as those based on the system accountability benchmark.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116381773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1109/ICDMW58026.2022.00049
Fahed Elourajini, Esma Aïmeur
Personality and preferences are essential variables in computational sociology and social science. They describe differences between people at both individual and group levels. In recent years, automated approaches that detect personality traits have received much attention due to the massive availability of individuals' digital footprints. Furthermore, researchers have demonstrated a strong link between personality traits and various downstream tasks such as personalized filtering, profile categorization, and profile embedding. Therefore, the detection of individuals' preferences has become a critical process for improving the performance of different tasks. In this paper, we build on the importance of the individual's behaviour and propose a novel multitask modeling approach that understands and models the users' personalities based on their textual posts and comments within a multimedia framework. The novelties of our work compared to state-of-the-art personality prediction models are: improving the performance of the Big five-factor model (Big5) personality test using shared information from the Myers Briggs Type Indicator (MBTI) test, and proposing a one personality detection framework that accurately predicts both MBTI and Big5 tests simultaneously. Predicting both tests simultaneously improves the personality detection framework's flexibility to be used for different goals instead of being used only for a unique purpose (whether for the MBTI test or for the Big5 test separately). Experiments and results demonstrate that our solution outperforms state-of-the-art models across multiple famous personality datasets.
{"title":"AWS-EP: A Multi-Task Prediction Approach for MBTI/Big5 Personality Tests","authors":"Fahed Elourajini, Esma Aïmeur","doi":"10.1109/ICDMW58026.2022.00049","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00049","url":null,"abstract":"Personality and preferences are essential variables in computational sociology and social science. They describe differences between people at both individual and group levels. In recent years, automated approaches that detect personality traits have received much attention due to the massive availability of individuals' digital footprints. Furthermore, researchers have demonstrated a strong link between personality traits and various downstream tasks such as personalized filtering, profile categorization, and profile embedding. Therefore, the detection of individuals' preferences has become a critical process for improving the performance of different tasks. In this paper, we build on the importance of the individual's behaviour and propose a novel multitask modeling approach that understands and models the users' personalities based on their textual posts and comments within a multimedia framework. The novelties of our work compared to state-of-the-art personality prediction models are: improving the performance of the Big five-factor model (Big5) personality test using shared information from the Myers Briggs Type Indicator (MBTI) test, and proposing a one personality detection framework that accurately predicts both MBTI and Big5 tests simultaneously. Predicting both tests simultaneously improves the personality detection framework's flexibility to be used for different goals instead of being used only for a unique purpose (whether for the MBTI test or for the Big5 test separately). Experiments and results demonstrate that our solution outperforms state-of-the-art models across multiple famous personality datasets.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116998730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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