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How could the United Nations Global Digital Compact prevent cultural imposition and hermeneutical injustice? 联合国全球数字契约如何防止文化强加和诠释学上的不公正？

IF 6.7

Patterns Pub Date : 2024-11-08 DOI: 10.1016/j.patter.2024.101078

Arthur Gwagwa, Warmhold Jan Thomas Mollema

{"title":"How could the United Nations Global Digital Compact prevent cultural imposition and hermeneutical injustice?","authors":"Arthur Gwagwa, Warmhold Jan Thomas Mollema","doi":"10.1016/j.patter.2024.101078","DOIUrl":"10.1016/j.patter.2024.101078","url":null,"abstract":"As the geopolitical superpowers race to regulate the digital realm, their divergent rights-centered, market-driven, and social-control-based approaches require a global compact on digital regulation. If diverse regulatory jurisdictions remain, forms of domination entailed by cultural imposition and hermeneutical injustice related to AI legislation and AI systems will follow. We argue for consensual regulation on shared substantive issues, accompanied by proper standardization and coordination. Failure to attain consensus will fragment global digital regulation, enable regulatory capture by authoritarian powers or bad corporate actors, and deepen the historical geopolitical power asymmetries between the global South and the global North. To prevent an unjust regulatory landscape where the global South's cultural and hermeneutic resources are absent, two principles for the Global Digital Compact to counter these prospective harms are proposed and discussed: (1) \"recognitive consensus on key substantive benefits and harms\" and (2) \"procedural consensus on global coordination and essential standards.\"","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"5 11","pages":"101078"},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmark suites instead of leaderboards for evaluating AI fairness. 用基准套件代替排行榜来评估人工智能的公平性。

IF 6.7

Patterns Pub Date : 2024-11-08 DOI: 10.1016/j.patter.2024.101080

Angelina Wang, Aaron Hertzmann, Olga Russakovsky

{"title":"Benchmark suites instead of leaderboards for evaluating AI fairness.","authors":"Angelina Wang, Aaron Hertzmann, Olga Russakovsky","doi":"10.1016/j.patter.2024.101080","DOIUrl":"10.1016/j.patter.2024.101080","url":null,"abstract":"Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial intelligence (AI) models. Many critics argue against this practice, since it incentivizes optimizing for metrics in an attempt to build the \"most fair\" AI model. However, this is an inherently impossible task since different applications have different considerations. While we agree with the critiques against leaderboards, we believe that the use of benchmarks can be reformed. Thus far, the critiques of leaderboards and benchmarks have become unhelpfully entangled. However, benchmarks, when not used for leaderboards, offer important tools for understanding a model. We advocate for collecting benchmarks into carefully curated \"benchmark suites,\" which can provide researchers and practitioners with tools for understanding the wide range of potential harms and trade-offs among different aspects of fairness. We describe the research needed to build these benchmark suites so that they can better assess different usage modalities, cover potential harms, and reflect diverse perspectives. By moving away from leaderboards and instead thoughtfully designing and compiling benchmark suites, we can better monitor and improve the fairness impacts of AI technology.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"5 11","pages":"101080"},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward a tipping point in federated learning in healthcare and life sciences. 迈向医疗保健和生命科学领域联合学习的临界点。

IF 6.7

Patterns Pub Date : 2024-11-08 DOI: 10.1016/j.patter.2024.101077

Inken Hagestedt, Ian Hales, Eric Boernert, Holger R Roth, Michael A Hoeh, Robin Röhm, Ellie Dobson, José Tomás Prieto

引用次数: 0

An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.

IF 6.7

Patterns Pub Date : 2024-11-04 eCollection Date: 2024-12-13 DOI: 10.1016/j.patter.2024.101081

Shuyu Liu, Jingjing Zhou, Xuequan Zhu, Ya Zhang, Xinzhu Zhou, Shaoting Zhang, Zhi Yang, Ziji Wang, Ruoxi Wang, Yizhe Yuan, Xin Fang, Xiongying Chen, Yanfeng Wang, Ling Zhang, Gang Wang, Cheng Jin

{"title":"An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.","authors":"Shuyu Liu, Jingjing Zhou, Xuequan Zhu, Ya Zhang, Xinzhu Zhou, Shaoting Zhang, Zhi Yang, Ziji Wang, Ruoxi Wang, Yizhe Yuan, Xin Fang, Xiongying Chen, Yanfeng Wang, Ling Zhang, Gang Wang, Cheng Jin","doi":"10.1016/j.patter.2024.101081","DOIUrl":"https://doi.org/10.1016/j.patter.2024.101081","url":null,"abstract":"This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.75%, an area under the receiver operating characteristic curve (AUROC) of 80.64%, and correctly identified MDD subtypes. The system further discovered distinct brain connectivity patterns in MDD, including reduced functional connectivity between the left gyrus rectus and right cerebellar lobule VIIB, and increased connectivity between the left Rolandic operculum and right hippocampus. Anatomically, MDD is associated with thickness changes of the gray and white matter interface, indicating potential neuropathological conditions or brain injuries.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"5 12","pages":"101081"},"PeriodicalIF":6.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules. 对健康多组织人类 RNA-seq 数据嵌入的潜在空间运算解码疾病模块。

IF 6.7

Patterns Pub Date : 2024-10-31 eCollection Date: 2024-11-08 DOI: 10.1016/j.patter.2024.101093

Hendrik A de Weerd, Dimitri Guala, Mika Gustafsson, Jane Synnergren, Jesper Tegnér, Zelmina Lubovac-Pilav, Rasmus Magnusson

{"title":"Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.","authors":"Hendrik A de Weerd, Dimitri Guala, Mika Gustafsson, Jane Synnergren, Jesper Tegnér, Zelmina Lubovac-Pilav, Rasmus Magnusson","doi":"10.1016/j.patter.2024.101093","DOIUrl":"10.1016/j.patter.2024.101093","url":null,"abstract":"Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"5 11","pages":"101093"},"PeriodicalIF":6.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection. 用于估计感染 SARS-CoV-2 后医院特定的急性期后医疗保健需求的潜移默化学习方法。

IF 6.7

Patterns Pub Date : 2024-10-24 eCollection Date: 2024-11-08 DOI: 10.1016/j.patter.2024.101079

Qiong Wu, Nathan M Pajor, Yiwen Lu, Charles J Wolock, Jiayi Tong, Vitaly Lorman, Kevin B Johnson, Jason H Moore, Christopher B Forrest, David A Asch, Yong Chen

{"title":"A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.","authors":"Qiong Wu, Nathan M Pajor, Yiwen Lu, Charles J Wolock, Jiayi Tong, Vitaly Lorman, Kevin B Johnson, Jason H Moore, Christopher B Forrest, David A Asch, Yong Chen","doi":"10.1016/j.patter.2024.101079","DOIUrl":"10.1016/j.patter.2024.101079","url":null,"abstract":"The long-term complications of COVID-19, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), significantly burden healthcare resources. Quantifying the demand for post-acute healthcare is essential for understanding patients' needs and optimizing the allocation of valuable medical resources for disease management. Driven by this need, we developed a heterogeneous latent transfer learning framework (Latent-TL) to generate critical insights for individual health systems in a distributed research network. Latent-TL enhances learning in a specific health system by borrowing information from all other health systems in the network in a data-driven fashion. By identifying subpopulations with varying healthcare needs, our Latent-TL framework can provide more effective guidance for decision-making. Applying Latent-TL to electronic health record (EHR) data from eight health systems in PEDSnet, a national learning health system in the US, revealed four distinct patient subpopulations with heterogeneous post-acute healthcare demands following COVID-19 infections, varying across subpopulations and hospitals.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"5 11","pages":"101079"},"PeriodicalIF":6.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RummaGEO: Automatic mining of human and mouse gene sets from GEO. RummaGEO：从 GEO 自动挖掘人类和小鼠基因组。

IF 6.7

Patterns Pub Date : 2024-10-11 DOI: 10.1016/j.patter.2024.101072

Giacomo B Marino, Daniel J B Clarke, Alexander Lachmann, Eden Z Deng, Avi Ma'ayan

引用次数: 0

How our authors are using AI tools in manuscript writing. 我们的作者如何在稿件写作中使用人工智能工具。

IF 6.7

Patterns Pub Date : 2024-10-11 DOI: 10.1016/j.patter.2024.101075

Yinqi Bai, Clayton W Kosonocky, James Z Wang

引用次数: 0

Student skills need to evolve to match our new AI society. 学生的技能需要与时俱进，以适应新的人工智能社会。