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How machine learning models can amplify inequities in medical diagnosis and treatment

Exploring the implications of AI bias in health care, Marzyeh Ghassemi and her MIT team examine the disparities arising from machine learning models, often negatively affecting underrepresented groups. ....

United States , Sanmi Koyejo , Yuzhe Yang , Zhaoran Zhang , Marzyeh Ghassemi , Stanford University , Department Of Electrical Science , International Conference On Machine Learning In Honolulu , Artificial Intelligence Laboratory , Electrical Science , Computer Science , Dina Katabi , Nicole Pham Professor , International Conference , Machine Learning , Mit Csail , Mit Eecs , I Bias In Health Care , Class Imbalance , Marzyeh Ghassemi , Dina Katabi , Ubpopulation Shift , Orst Group Accuracy , Muzhe Yang , Zhaoran Zhang , Spurious Correlations ,

"DAGAD: Data Augmentation for Graph Anomaly Detection" by Fanzhen Liu, Xiaoxiao Ma et al.

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data aug ....

Data Augmentation Based Graph Anomaly Detection , Anomalous Sample Scarcity , Anomaly Detection , Class Imbalance , Data Augmentation , Graph Mining , Graph Neural Networks , Semi Supervised Learning ,

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al.

Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning on Big data hinders the scalability of IIoT intrusion detection systems. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-lev ....

Industrial Internet , Class Imbalance , Computational Modeling , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Edge Computing , Evolutionary Algorithms , Fog Computing , Industrial Internet Of Things , Industrial Internet Of Things Iiot , Intrusion Detection ,

Fiddler Announces Giga-Scale Model Performance Management with Deeper Understanding of Unstructured Models and Fine Discoverability to Launch New AI Initiatives

Fiddler Announces Giga-Scale Model Performance Management with Deeper Understanding of Unstructured Models and Fine Discoverability to Launch New AI Initiatives
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Krishna Gade , Jonathan Siddharth , Kostenloser Wertpapierhandel , Sree Kamireddy , Communications For Fiddler , Model Performance Management , Adoption Index , Class Imbalance , Product Management ,

"A cost-sensitive deep learning based approach for network traffic clas" by Akbar Telikani, Amir H. Gandomi et al.

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ ....

Class Imbalance , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Ncrypted Traffic Classification , Generative Adversarial Networks , Intrusion Detection , Task Analysis ,