Author: "Díaz, Mark" - Searchworks@Jio Institute Digital Library Search Results

35 results on '"Díaz, Mark"'

1. Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups

Author: Rastogi, Charvi, Teh, Tian Huey, Mishra, Pushkar, Patel, Roma, Ashwood, Zoe, Davani, Aida Mostafazadeh, Diaz, Mark, Paganini, Michela, Parrish, Alicia, Wang, Ding, Prabhakaran, Vinodkumar, Aroyo, Lora, and Rieser, Verena
Subjects: Computer Science - Artificial Intelligence
Abstract: AI systems crucially rely on human ratings, but these ratings are often aggregated, obscuring the inherent diversity of perspectives in real-world phenomenon. This is particularly concerning when evaluating the safety of generative AI, where perceptions and associated harms can vary significantly across socio-cultural contexts. While recent research has studied the impact of demographic differences on annotating text, there is limited understanding of how these subjective variations affect multimodal safety in generative AI. To address this, we conduct a large-scale study employing highly-parallel safety ratings of about 1000 text-to-image (T2I) generations from a demographically diverse rater pool of 630 raters balanced across 30 intersectional groups across age, gender, and ethnicity. Our study shows that (1) there are significant differences across demographic groups (including intersectional groups) on how severe they assess the harm to be, and that these differences vary across different types of safety violations, (2) the diverse rater pool captures annotation patterns that are substantially different from expert raters trained on specific set of safety policies, and (3) the differences we observe in T2I safety are distinct from previously documented group level differences in text-based safety tasks. To further understand these varying perspectives, we conduct a qualitative analysis of the open-ended explanations provided by raters. This analysis reveals core differences into the reasons why different groups perceive harms in T2I generations. Our findings underscore the critical need for incorporating diverse perspectives into safety evaluation of generative AI ensuring these systems are truly inclusive and reflect the values of all users., Comment: 20 pages, 7 figures
Published: 2024

2. STAR: SocioTechnical Approach to Red Teaming Language Models

Author: Weidinger, Laura, Mellor, John, Pegueroles, Bernat Guillen, Marchal, Nahema, Kumar, Ravin, Lum, Kristian, Akbulut, Canfer, Diaz, Mark, Bergman, Stevie, Rodriguez, Mikel, Rieser, Verena, and Isaac, William
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Human-Computer Interaction
Abstract: This research introduces STAR, a sociotechnical framework that improves on current best practices for red teaming safety of large language models. STAR makes two key contributions: it enhances steerability by generating parameterised instructions for human red teamers, leading to improved coverage of the risk surface. Parameterised instructions also provide more detailed insights into model failures at no increased cost. Second, STAR improves signal quality by matching demographics to assess harms for specific groups, resulting in more sensitive annotations. STAR further employs a novel step of arbitration to leverage diverse viewpoints and improve label reliability, treating disagreement not as noise but as a valuable contribution to signal quality., Comment: 8 pages, 5 figures, 5 pages appendix. * denotes equal contribution
Published: 2024

3. D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation

Author: Davani, Aida Mostafazadeh, Díaz, Mark, Baker, Dylan, and Prabhakaran, Vinodkumar
Subjects: Computer Science - Computation and Language
Abstract: While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that have critically examined this issue are often situated in the Western context, and solely document differences across age, gender, or racial groups. As a result, NLP research on subjectivity have overlooked the fact that individuals within demographic groups may hold diverse values, which can influence their perceptions beyond their group norms. To effectively incorporate these considerations into NLP pipelines, we need datasets with extensive parallel annotations from various social and cultural groups. In this paper we introduce the \dataset dataset: a large-scale cross-cultural dataset of parallel annotations for offensive language in over 4.5K sentences annotated by a pool of over 4k annotators, balanced across gender and age, from across 21 countries, representing eight geo-cultural regions. The dataset contains annotators' moral values captured along six moral foundations: care, equality, proportionality, authority, loyalty, and purity. Our analyses reveal substantial regional variations in annotators' perceptions that are shaped by individual moral values, offering crucial insights for building pluralistic, culturally sensitive NLP models.
Published: 2024

4. Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation

Author: Smart, Andrew, Wang, Ding, Monk, Ellis, Díaz, Mark, Kasirzadeh, Atoosa, Van Liemt, Erin, and Schmer-Galunder, Sonja
Subjects: Computer Science - Artificial Intelligence
Abstract: Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of research have begun to examine the working conditions for data annotation workers, the impacts and role of annotator subjectivity on labels, and the potential psychological harms from aspects of annotation work. This paper outlines a critical genealogy of data annotation; starting with its psychological and perceptual aspects. We draw on similarities with critiques of the rise of computerized lab-based psychological experiments in the 1970's which question whether these experiments permit the generalization of results beyond the laboratory settings within which these results are typically obtained. Do data annotations permit the generalization of results beyond the settings, or locations, in which they were obtained? Psychology is overly reliant on participants from Western, Educated, Industrialized, Rich, and Democratic societies (WEIRD). Many of the people who work as data annotation platform workers, however, are not from WEIRD countries; most data annotation workers are based in Global South countries. Social categorizations and classifications from WEIRD countries are imposed on non-WEIRD annotators through instructions and tasks, and through them, on data, which is then used to train or evaluate AI models in WEIRD countries. We synthesize evidence from several recent lines of research and argue that data annotation is a form of automated social categorization that risks entrenching outdated and static social categories that are in reality dynamic and changing. We propose a framework for understanding the interplay of the global social conditions of data annotation with the subjective phenomenological experience of data annotation work., Comment: 18 pages
Published: 2024

5. The illusion of artificial inclusion

Author: Agnew, William, Bergman, A. Stevie, Chien, Jennifer, Díaz, Mark, El-Sayed, Seliem, Pittman, Jaylen, Mohamed, Shakir, and McKee, Kevin R.
Subjects: Computer Science - Computers and Society
Abstract: Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research. Recent advances in generative AI have attracted growing interest to the possibility of replacing human participants in these domains with AI surrogates. We survey several such "substitution proposals" to better understand the arguments for and against substituting human participants with modern generative AI. Our scoping review indicates that the recent wave of these proposals is motivated by goals such as reducing the costs of research and development work and increasing the diversity of collected data. However, these proposals ignore and ultimately conflict with foundational values of work with human participants: representation, inclusion, and understanding. This paper critically examines the principles and goals underlying human participation to help chart out paths for future work that truly centers and empowers participants., Comment: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024)
Published: 2024
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6. Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates

Author: Davani, Aida, Díaz, Mark, Baker, Dylan, and Prabhakaran, Vinodkumar
Subjects: Computer Science - Computers and Society, Computer Science - Computation and Language
Abstract: Perception of offensiveness is inherently subjective, shaped by the lived experiences and socio-cultural values of the perceivers. Recent years have seen substantial efforts to build AI-based tools that can detect offensive language at scale, as a means to moderate social media platforms, and to ensure safety of conversational AI technologies such as ChatGPT and Bard. However, existing approaches treat this task as a technical endeavor, built on top of data annotated for offensiveness by a global crowd workforce without any attention to the crowd workers' provenance or the values their perceptions reflect. We argue that cultural and psychological factors play a vital role in the cognitive processing of offensiveness, which is critical to consider in this context. We re-frame the task of determining offensiveness as essentially a matter of moral judgment -- deciding the boundaries of ethically wrong vs. right language within an implied set of socio-cultural norms. Through a large-scale cross-cultural study based on 4309 participants from 21 countries across 8 cultural regions, we demonstrate substantial cross-cultural differences in perceptions of offensiveness. More importantly, we find that individual moral values play a crucial role in shaping these variations: moral concerns about Care and Purity are significant mediating factors driving cross-cultural differences. These insights are of crucial importance as we build AI models for the pluralistic world, where the values they espouse should aim to respect and account for moral values in diverse geo-cultural contexts.
Published: 2023

7. SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata

Author: Díaz, Mark, Dev, Sunipa, Reif, Emily, Denton, Emily, and Prabhakaran, Vinodkumar
Subjects: Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract: The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions. From a Responsible AI perspective, these decisions often rely upon understanding how people are represented in data. We propose a framework designed to guide analysis of human representation in unstructured data and identify downstream risks. We apply the framework in two toy examples using the Common Crawl web text corpus (C4) and LAION-400M. We also propose a set of hypothetical action steps in service of dataset use, development, and documentation.
Published: 2023

8. GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives

Author: Prabhakaran, Vinodkumar, Homan, Christopher, Aroyo, Lora, Davani, Aida Mostafazadeh, Parrish, Alicia, Taylor, Alex, Díaz, Mark, Wang, Ding, and Serapio-García, Gregory
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Human annotation plays a core role in machine learning -- annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning, to cite a few avenues. However, the fact that many of these human annotations are inherently subjective is often overlooked. Recent work has demonstrated that ignoring rater subjectivity (typically resulting in rater disagreement) is problematic within specific tasks and for specific subgroups. Generalizable methods to harness rater disagreement and thus understand the socio-cultural leanings of subjective tasks remain elusive. In this paper, we propose GRASP, a comprehensive disagreement analysis framework to measure group association in perspectives among different rater sub-groups, and demonstrate its utility in assessing the extent of systematic disagreements in two datasets: (1) safety annotations of human-chatbot conversations, and (2) offensiveness annotations of social media posts, both annotated by diverse rater pools across different socio-demographic axes. Our framework (based on disagreement metrics) reveals specific rater groups that have significantly different perspectives than others on certain tasks, and helps identify demographic axes that are crucial to consider in specific task contexts., Comment: Presented as a long paper at NAACL 2024 main conference
Published: 2023

9. Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety

Author: Homan, Christopher M., Serapio-Garcia, Greg, Aroyo, Lora, Diaz, Mark, Parrish, Alicia, Prabhakaran, Vinodkumar, Taylor, Alex S., and Wang, Ding
Subjects: Computer Science - Human-Computer Interaction
Abstract: Conversational AI systems exhibit a level of human-like behavior that promises to have profound impacts on many aspects of daily life -- how people access information, create content, and seek social support. Yet these models have also shown a propensity for biases, offensive language, and conveying false information. Consequently, understanding and moderating safety risks in these models is a critical technical and social challenge. Perception of safety is intrinsically subjective, where many factors -- often intersecting -- could determine why one person may consider a conversation with a chatbot safe and another person could consider the same conversation unsafe. In this work, we focus on demographic factors that could influence such diverse perceptions. To this end, we contribute an analysis using Bayesian multilevel modeling to explore the connection between rater demographics and how raters report safety of conversational AI systems. We study a sample of 252 human raters stratified by gender, age group, race/ethnicity group, and locale. This rater pool provided safety labels for 1,340 human-chatbot conversations. Our results show that intersectional effects involving demographic characteristics such as race/ethnicity, gender, and age, as well as content characteristics, such as degree of harm, all play significant roles in determining the safety of conversational AI systems. For example, race/ethnicity and gender show strong intersectional effects, particularly among South Asian and East Asian women. We also find that conversational degree of harm impacts raters of all race/ethnicity groups, but that Indigenous and South Asian raters are particularly sensitive to this harm. Finally, we observe the effect of education is uniquely intersectional for Indigenous raters, highlighting the utility of multilevel frameworks for uncovering underrepresented social perspectives.
Published: 2023

10. DICES Dataset: Diversity in Conversational AI Evaluation for Safety

Author: Aroyo, Lora, Taylor, Alex S., Diaz, Mark, Homan, Christopher M., Parrish, Alicia, Serapio-Garcia, Greg, Prabhakaran, Vinodkumar, and Wang, Ding
Subjects: Computer Science - Human-Computer Interaction
Abstract: Machine learning approaches often require training and evaluation datasets with a clear separation between positive and negative examples. This risks simplifying and even obscuring the inherent subjectivity present in many tasks. Preserving such variance in content and diversity in datasets is often expensive and laborious. This is especially troubling when building safety datasets for conversational AI systems, as safety is both socially and culturally situated. To demonstrate this crucial aspect of conversational AI safety, and to facilitate in-depth model performance analyses, we introduce the DICES (Diversity In Conversational AI Evaluation for Safety) dataset that contains fine-grained demographic information about raters, high replication of ratings per item to ensure statistical power for analyses, and encodes rater votes as distributions across different demographics to allow for in-depth explorations of different aggregation strategies. In short, the DICES dataset enables the observation and measurement of variance, ambiguity, and diversity in the context of conversational AI safety. We also illustrate how the dataset offers a basis for establishing metrics to show how raters' ratings can intersects with demographic categories such as racial/ethnic groups, age groups, and genders. The goal of DICES is to be used as a shared resource and benchmark that respects diverse perspectives during safety evaluation of conversational AI systems.
Published: 2023

11. PaLM 2 Technical Report

Author: Anil, Rohan, Dai, Andrew M., Firat, Orhan, Johnson, Melvin, Lepikhin, Dmitry, Passos, Alexandre, Shakeri, Siamak, Taropa, Emanuel, Bailey, Paige, Chen, Zhifeng, Chu, Eric, Clark, Jonathan H., Shafey, Laurent El, Huang, Yanping, Meier-Hellstern, Kathy, Mishra, Gaurav, Moreira, Erica, Omernick, Mark, Robinson, Kevin, Ruder, Sebastian, Tay, Yi, Xiao, Kefan, Xu, Yuanzhong, Zhang, Yujing, Abrego, Gustavo Hernandez, Ahn, Junwhan, Austin, Jacob, Barham, Paul, Botha, Jan, Bradbury, James, Brahma, Siddhartha, Brooks, Kevin, Catasta, Michele, Cheng, Yong, Cherry, Colin, Choquette-Choo, Christopher A., Chowdhery, Aakanksha, Crepy, Clément, Dave, Shachi, Dehghani, Mostafa, Dev, Sunipa, Devlin, Jacob, Díaz, Mark, Du, Nan, Dyer, Ethan, Feinberg, Vlad, Feng, Fangxiaoyu, Fienber, Vlad, Freitag, Markus, Garcia, Xavier, Gehrmann, Sebastian, Gonzalez, Lucas, Gur-Ari, Guy, Hand, Steven, Hashemi, Hadi, Hou, Le, Howland, Joshua, Hu, Andrea, Hui, Jeffrey, Hurwitz, Jeremy, Isard, Michael, Ittycheriah, Abe, Jagielski, Matthew, Jia, Wenhao, Kenealy, Kathleen, Krikun, Maxim, Kudugunta, Sneha, Lan, Chang, Lee, Katherine, Lee, Benjamin, Li, Eric, Li, Music, Li, Wei, Li, YaGuang, Li, Jian, Lim, Hyeontaek, Lin, Hanzhao, Liu, Zhongtao, Liu, Frederick, Maggioni, Marcello, Mahendru, Aroma, Maynez, Joshua, Misra, Vedant, Moussalem, Maysam, Nado, Zachary, Nham, John, Ni, Eric, Nystrom, Andrew, Parrish, Alicia, Pellat, Marie, Polacek, Martin, Polozov, Alex, Pope, Reiner, Qiao, Siyuan, Reif, Emily, Richter, Bryan, Riley, Parker, Ros, Alex Castro, Roy, Aurko, Saeta, Brennan, Samuel, Rajkumar, Shelby, Renee, Slone, Ambrose, Smilkov, Daniel, So, David R., Sohn, Daniel, Tokumine, Simon, Valter, Dasha, Vasudevan, Vijay, Vodrahalli, Kiran, Wang, Xuezhi, Wang, Pidong, Wang, Zirui, Wang, Tao, Wieting, John, Wu, Yuhuai, Xu, Kelvin, Xu, Yunhan, Xue, Linting, Yin, Pengcheng, Yu, Jiahui, Zhang, Qiao, Zheng, Steven, Zheng, Ce, Zhou, Weikang, Zhou, Denny, Petrov, Slav, and Wu, Yonghui
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.
Published: 2023

12. (Re)Defining Expertise in Machine Learning Development

Author: Díaz, Mark and Smith, Angela D. R.
Subjects: Computer Science - Machine Learning
Abstract: Domain experts are often engaged in the development of machine learning systems in a variety of ways, such as in data collection and evaluation of system performance. At the same time, who counts as an 'expert' and what constitutes 'expertise' is not always explicitly defined. In this project, we conduct a systematic literature review of machine learning research to understand 1) the bases on which expertise is defined and recognized and 2) the roles experts play in ML development. Our goal is to produce a high-level taxonomy to highlight limits and opportunities in how experts are identified and engaged in ML research., Comment: NeurIPS 2022 Workshop on Data-Centric AI, 2 pages
Published: 2023

13. The Reasonable Effectiveness of Diverse Evaluation Data

Author: Aroyo, Lora, Diaz, Mark, Homan, Christopher, Prabhakaran, Vinodkumar, Taylor, Alex, and Wang, Ding
Subjects: Computer Science - Human-Computer Interaction
Abstract: In this paper, we present findings from an semi-experimental exploration of rater diversity and its influence on safety annotations of conversations generated by humans talking to a generative AI-chat bot. We find significant differences in judgments produced by raters from different geographic regions and annotation platforms, and correlate these perspectives with demographic sub-groups. Our work helps define best practices in model development -- specifically human evaluation of generative models -- on the backdrop of growing work on sociotechnical AI evaluations., Comment: 5 pages
Published: 2023

14. Power to the People? Opportunities and Challenges for Participatory AI

Author: Birhane, Abeba, Isaac, William, Prabhakaran, Vinodkumar, Díaz, Mark, Elish, Madeleine Clare, Gabriel, Iason, and Mohamed, Shakir
Subjects: Computer Science - Computers and Society
Abstract: Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI.Among other benefits, participatory approaches are essential to understanding and adequately representing the needs, desires and perspectives of historically marginalized communities. However, there currently exists lack of clarity on what meaningful participation entails and what it is expected to do. In this paper we first review participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline. We then introduce three case studies in participatory AI.Participation holds the potential for beneficial, emancipatory and empowering technology design, development and deployment while also being at risk for concerns such as cooptation and conflation with other activities. We lay out these limitations and concerns and argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings., Comment: To appear in the proceeding of EAAMO 2022
Published: 2022
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15. CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation

Author: Diaz, Mark, Kivlichan, Ian D., Rosen, Rachel, Baker, Dylan K., Amironesei, Razvan, Prabhakaran, Vinodkumar, and Denton, Emily
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: Human annotated data plays a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into dataset annotation have not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms, and what that relationship affords them. Finally, we introduce a novel framework, CrowdWorkSheets, for dataset developers to facilitate transparent documentation of key decisions points at various stages of the data annotation pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset release and maintenance., Comment: 11 pages, Accepted at 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT). arXiv admin note: text overlap with arXiv:2112.04554
Published: 2022
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16. PaLM: Scaling Language Modeling with Pathways

Author: Chowdhery, Aakanksha, Narang, Sharan, Devlin, Jacob, Bosma, Maarten, Mishra, Gaurav, Roberts, Adam, Barham, Paul, Chung, Hyung Won, Sutton, Charles, Gehrmann, Sebastian, Schuh, Parker, Shi, Kensen, Tsvyashchenko, Sasha, Maynez, Joshua, Rao, Abhishek, Barnes, Parker, Tay, Yi, Shazeer, Noam, Prabhakaran, Vinodkumar, Reif, Emily, Du, Nan, Hutchinson, Ben, Pope, Reiner, Bradbury, James, Austin, Jacob, Isard, Michael, Gur-Ari, Guy, Yin, Pengcheng, Duke, Toju, Levskaya, Anselm, Ghemawat, Sanjay, Dev, Sunipa, Michalewski, Henryk, Garcia, Xavier, Misra, Vedant, Robinson, Kevin, Fedus, Liam, Zhou, Denny, Ippolito, Daphne, Luan, David, Lim, Hyeontaek, Zoph, Barret, Spiridonov, Alexander, Sepassi, Ryan, Dohan, David, Agrawal, Shivani, Omernick, Mark, Dai, Andrew M., Pillai, Thanumalayan Sankaranarayana, Pellat, Marie, Lewkowycz, Aitor, Moreira, Erica, Child, Rewon, Polozov, Oleksandr, Lee, Katherine, Zhou, Zongwei, Wang, Xuezhi, Saeta, Brennan, Diaz, Mark, Firat, Orhan, Catasta, Michele, Wei, Jason, Meier-Hellstern, Kathy, Eck, Douglas, Dean, Jeff, Petrov, Slav, and Fiedel, Noah
Subjects: Computer Science - Computation and Language
Abstract: Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.
Published: 2022

17. LaMDA: Language Models for Dialog Applications

Author: Thoppilan, Romal, De Freitas, Daniel, Hall, Jamie, Shazeer, Noam, Kulshreshtha, Apoorv, Cheng, Heng-Tze, Jin, Alicia, Bos, Taylor, Baker, Leslie, Du, Yu, Li, YaGuang, Lee, Hongrae, Zheng, Huaixiu Steven, Ghafouri, Amin, Menegali, Marcelo, Huang, Yanping, Krikun, Maxim, Lepikhin, Dmitry, Qin, James, Chen, Dehao, Xu, Yuanzhong, Chen, Zhifeng, Roberts, Adam, Bosma, Maarten, Zhao, Vincent, Zhou, Yanqi, Chang, Chung-Ching, Krivokon, Igor, Rusch, Will, Pickett, Marc, Srinivasan, Pranesh, Man, Laichee, Meier-Hellstern, Kathleen, Morris, Meredith Ringel, Doshi, Tulsee, Santos, Renelito Delos, Duke, Toju, Soraker, Johnny, Zevenbergen, Ben, Prabhakaran, Vinodkumar, Diaz, Mark, Hutchinson, Ben, Olson, Kristen, Molina, Alejandra, Hoffman-John, Erin, Lee, Josh, Aroyo, Lora, Rajakumar, Ravi, Butryna, Alena, Lamm, Matthew, Kuzmina, Viktoriya, Fenton, Joe, Cohen, Aaron, Bernstein, Rachel, Kurzweil, Ray, Aguera-Arcas, Blaise, Cui, Claire, Croak, Marian, Chi, Ed, and Le, Quoc
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.
Published: 2022

18. Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

Author: Denton, Remi, Díaz, Mark, Kivlichan, Ian, Prabhakaran, Vinodkumar, and Rosen, Rachel
Subjects: Computer Science - Machine Learning
Abstract: Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets has not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms and what that relationship affords them. Finally, we put forth a concrete set of recommendations and considerations for dataset developers at various stages of the ML data pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset documentation and release.
Published: 2021

19. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

Author: Davani, Aida Mostafazadeh, Díaz, Mark, and Prabhakaran, Vinodkumar
Subjects: Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract: Majority voting and averaging are common approaches employed to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. Annotator disagreements may capture important nuances in such tasks that are often ignored while aggregating annotations to a single ground truth. In order to address this, we investigate the efficacy of multi-annotator models. In particular, our multi-task based approach treats predicting each annotators' judgements as separate subtasks, while sharing a common learned representation of the task. We show that this approach yields same or better performance than aggregating labels in the data prior to training across seven different binary classification tasks. Our approach also provides a way to estimate uncertainty in predictions, which we demonstrate better correlate with annotation disagreements than traditional methods. Being able to model uncertainty is especially useful in deployment scenarios where knowing when not to make a prediction is important.
Published: 2021

20. On Releasing Annotator-Level Labels and Information in Datasets

Author: Prabhakaran, Vinodkumar, Davani, Aida Mostafazadeh, and Díaz, Mark
Subjects: Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract: A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then flattened to produce a single "ground truth" label or score, through majority voting, averaging, or adjudication. While these approaches may be appropriate in certain annotation tasks, such aggregations overlook the socially constructed nature of human perceptions that annotations for relatively more subjective tasks are meant to capture. In particular, systematic disagreements between annotators owing to their socio-cultural backgrounds and/or lived experiences are often obfuscated through such aggregations. In this paper, we empirically demonstrate that label aggregation may introduce representational biases of individual and group perspectives. Based on this finding, we propose a set of recommendations for increased utility and transparency of datasets for downstream use cases.
Published: 2021

21. Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates

Author: Davani, Aida, primary, Díaz, Mark, additional, Baker, Dylan, additional, and Prabhakaran, Vinodkumar, additional
Published: 2024
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22. Nontuberculous mycobacterial infections of the lower extremities: A 15-year experience

Author: Diaz, Mark Anthony A., Huff, Tamara N., and Libertin, Claudia R.
Published: 2019
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23. A Framework to Assess (Dis)agreement Among Diverse Rater Groups

Author: Prabhakaran, Vinodkumar, Homan, Christopher, Aroyo, Lora, Parrish, Alicia, Taylor, Alex, Díaz, Mark, Wang, Ding, Prabhakaran, Vinodkumar, Homan, Christopher, Aroyo, Lora, Parrish, Alicia, Taylor, Alex, Díaz, Mark, and Wang, Ding
Abstract: Recent advancements in conversational AI have created an urgent need for safety guardrails that prevent users from being exposed to offensive and dangerous content. Much of this work relies on human ratings and feedback, but does not account for the fact that perceptions of offense and safety are inherently subjective and that there may be systematic disagreements between raters that align with their socio-demographic identities. Instead, current machine learning approaches largely ignore rater subjectivity and use gold standards that obscure disagreements (e.g., through majority voting). In order to better understand the socio-cultural leanings of such tasks, we propose a comprehensive disagreement analysis framework to measure systematic diversity in perspectives among different rater subgroups. We then demonstrate its utility by applying this framework to a dataset of human-chatbot conversations rated by a demographically diverse pool of raters. Our analysis reveals specific rater groups that have more diverse perspectives than the rest, and informs demographic axes that are crucial to consider for safety annotations.
Published: 2023

24. Addressing Medicine’s Dark Matter

Author: Rose, Christian, primary, Díaz, Mark, additional, and Díaz, Tomás, additional
Published: 2022
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25. CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation

Author: Díaz, Mark, primary, Kivlichan, Ian, additional, Rosen, Rachel, additional, Baker, Dylan, additional, Amironesei, Razvan, additional, Prabhakaran, Vinodkumar, additional, and Denton, Emily, additional
Published: 2022
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26. Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat

Author: Robertson, Samantha, primary and Díaz, Mark, additional
Published: 2022
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27. Viewpoint: Addressing Medicine’s Dark Matter (Preprint)

Author: Rose, Christian, primary, Díaz, Mark, additional, and Díaz, Tomas, additional
Published: 2022
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28. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

Author: Davani, Aida Mostafazadeh, primary, Díaz, Mark, additional, and Prabhakaran, Vinodkumar, additional
Published: 2022
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29. Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

Author: Denton, Emily, Díaz, Mark, Kivlichan, Ian, Prabhakaran, Vinodkumar, and Rosen, Rachel
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)
Abstract: Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets has not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms and what that relationship affords them. Finally, we put forth a concrete set of recommendations and considerations for dataset developers at various stages of the ML data pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset documentation and release.
Published: 2021
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30. Biases as Values: Evaluating Algorithms in Context

Author: Díaz, Mark
Published: 2021
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31. Platforming Intersectionality: Networked Solidarity and the Limits of Corporate Social Media

Author: Christian, Aymar Jean, primary, Day, Faithe, additional, Díaz, Mark, additional, and Peterson-Salahuddin, Chelsea, additional
Published: 2020
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32. "The cavalry ain't coming in to save us"

Author: Dickinson, Jessa, primary, Díaz, Mark, additional, Le Dantec, Christopher A., additional, and Erete, Sheena, additional
Published: 2019
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33. Addressing Age-Related Bias in Sentiment Analysis

Author: Díaz, Mark, primary, Johnson, Isaac, additional, Lazar, Amanda, additional, Piper, Anne Marie, additional, and Gergle, Darren, additional
Published: 2019
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34. Abstract #639 Thyroid Storm in an HIV Patient: A Late Manifestation of Immune Reconstitution Syndrome

Author: Diaz, Mark Anthony, Landicho, Marie Angelica, Merchant, Chandni, and Eng, Margaret
Published: 2019
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35. Effects of hospital facilities on patient outcomes after cancer surgery

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Kenneth, A McLean, Rachel, Moore, Dion, Morton, Dmitri, Nepogodiev, Faustin, Ntirenganya, Francesco, Pata, Thomas, Pinkney, Ahmad Uzair Qureshi, Antonio Ramos-De la Medina, Aya, Riad, Hosni Khairy Salem, Joana, Simões, Richard, Spence, Neil, Smart, Stephen, Tabiri, Hannah, Thomas, Thomas, G Weiser, Malcolm, West, John, Whitaker, Ewen, M Harrison, Arben, Gjata, Maria Marta Modolo, Sebastian, King, Erick, Chan, Sayeda Nazmun Nahar, Ade, Waterman, Dominique, Vervoort, Alemayehu Ginbo Bedada, Bernardo De Azevedo, Ana Gabriela Figueiredo, Manol, Sokolov, Venerand, Barendegere, Gerald, Ekwen, Arnav, Agarwal, Qinyang, Liu, Juan Camilo Correa, Kalisya Luc Malemo, Jacques, Bake, Jakov, Mihanovic, Kamila, Kunčarová, Julius, Orhalmi, Hosni, Salem, Jyri, Teras, Aristotelis, Kechagias, Alexis, P Arnaud, Judith, Lindert, Vasileios, Kalles, Maria-Lorena, Aguilera-Arevalo, Gustavo, Recinos, Zsolt, Baranyai, Basant, Kumar, Harish Neelamraju Lakshmi, Sanoop Koshy Zachariah, Philip, Alexander, Sunil 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Jonathan, Gani, Yao Kuan Goh, Jun Young Jeong, Brendan, Mcmanus, Prakash, Nagappan, Peter, Pockney, Anya, Rugendyke, Mahsa, Sarrami, Stephen, Smith, Vanessa, Wills, Hsu Ven Wong, Geoffrey, Ye, Geoffrey, Zhang, Ethan, Brooker, Daniel, Feng, Bonnie, Lau, Carlin, Ngai, Sarah, Birks, David, Gyorki, Jaime Otero de Pablos, Ali, Abbosh, Chris, Gillespie, Ahmed, Mahmoud, Bianca, Kwan, Joshua, Lawson, Andrea, Warwick, Janne, Bingham, Andrew, J Cockbain, Nagendra Naidu Dudi-Venkata, Jordan, Ellaby-Hall, Ben, Finlay, Emily, Humphries, Jade, Pisaniello, Monique, Pisaniello, Salma, Salih, Tarik, Sammour, Haidar Hadri Abd Wahab, April De Silva, Nicola, Hayward, Kartik, Iyer, Guy, Maddern, Gian Andrea Prevost, Naga, Annapureddy, Krishna Pranathi Settipalli, Jeremy, Yeo, Lucy, Hempenstall, Lily, Pham, Shaun, Purcell, Cherry, Talavera, Ashish, I Vaska, Gurpreet, Chaggar, Phillip, Chrapko, Annelise, Cocco, Sarah Michelle Crystal Jade Coulter-Nile, Grahame, Ctercteko, James, French, Houchen, Gong, 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Tsandiraki, Damir, Jemendžić, Branislav, Kocman, Oliver, Šuman, Renata, Canic, Darko, Jurišić, Ivana, Karakas, Ana Krizanovic Rupcic, Vlatka, Pitlovic, Josip, Samardžić, Mario, Kopljar, Ivan, Bacic, Edgar, Domini, Robert, Karlo, Danijela, Miljanić, Andrea, Simic, Mariam, Ahmed, Majdi Al Nassrallah, Rabiya, Altaf, Talal, Amjad, Ruba, Eltoum, Heba, Haidar, Alhassan, Hassan, Omar, Khalil, Marwan, Qasem, Rommel, Ramesh, Gautham, Sajith, Maham, Wisal, Jan, Žatecký, Michele, Bujda, Katerina, Jirankova, Ales, Paclik, Aya, Abdallah, Mariam Abdulgawad Almogy, Esraa Ayman El-Sawy, Ahmed Moustafa ElFayoumy, Nourhan, Elghareeb, Nourhan Ahmed Esmat, Ahmed, Fadel, Abdullah, Habater, Heba, Hamdy, Amr, Hefni, Marwa, Kamal, Norhan Mohamed Abobakr, Ahmed, Sayed, Nancy, Shaker, Ehab, Taha, Hoda, Tharwat, Omar, Zakaria, Ibrahem, Abdelmotaleb, Ali, Al-Dhufri, Hamza, S Al-Himyari, Enas El Sheikh, Asmaa, Eldmaty, Aya, Elkhalawy, Ahmed, M Elkhashen, Kithara, Magdy, Safa, Mostafa, Habib Doutoum Sadia, Mohamed 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Fawzy, Hgag, Mohamed, Ibrahim, Mahmoud, Morsy, Mohamed Reda Loaloa, Ahmed, Refaat, Hadeer, Samir, Fatma, Shahien, Mohamed, Sobhy, Fathy, Sroor, Esraa, Abdellatif, Marina, Adel, Amr Abdelghani Afifi, Eman, Afifi, Marco, Antaky, Amr, Dawoud, Naira El Zoghby, Amira, El-Remaily, Ali Abdelazez Elzanfaly, Ahmed, Gadallah, Fatma Alzahraa Gamal, Omar, Hashem, Shrouk Medhat Youssef, Aliaa Muhammad Attyah, Malak, Munir, Omar, Shazly, Esraa, Taha, Karim, Wilson, Sawsan, Adel, Asmaa, Ali, Esraa, Eid, Esraa, Elhelow, Marwa, Elmahdy, Bassant, Elshatby, Amany Hossam El-Din Zakaria, Ahmad, Hossny, Eman, Ibrahim, Ahmed, M Yonis, Maram, Metwalli, Basant, Yousry, Esraa, Zid, Mina, A Yacoub, Ahmed, Abdelhakim, Nervana, Abouelsoad, Mo'Min, Alkhatib, Ahmed, Ashraf, Alaa, Ashraf, Yasmin, Elazab, Mahmoud, Elfanty, Osama, Elkabir, Mai, Elsayed, Ahmed, Elshimy, Hager, Elsobky, John, Eskander, Ahmed, Gad, Ward, Hamsho, Noura Khaled Abdelwahed, Menna, Magdy, Dalia, Moharam, Abeer, Osama, Shereen, Ramadan, Radwa, 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Julius Ng We Yong, Kandasami Palayan, Chi Xuan Sam, Phuah Siow Jin, Jeremy Tan Ern Hwei, Yita Tang, Alvin Zubin Ter, Michael Pak-Kai Wong, Andee Dzulkarnaen Zakaria, Zaidi Zakaria, Fitjerald Henry, Thyivya Kalaiselvan, Muhammad Fairuz Shah Abd Karim, Mohamed Rezal Abdul Aziz, Nora Abdul Aziz, Tak Loon Khong, Peng Choong Lau, Hiong Chin Lim, April Camilla Roslani, Jonathan Chen Ken Seak, Sui-Weng Wong, Lai Fen Wong, Leow Yeen Chin, Mercy Chinemerem Anyanwu, Elaine Borg, Zachary Busuttil, Thomas Calleja, Kurt Lee Chircop, Ruth Cutajar, Andrew Michael Dimech, Sarah Ellul, Joseph Galea, Kiara Gascon Perai, Ruth Gatt, Lisa Kelman, Elizabeth Micallef, Favour Nwolu, Kim Sammut, Joanna Thompson, Sean Warwicker, Matthew Zammit, Fernando Cordera, Efraín Cruz González, Jorge Sánchez-García, Francisco José Barbosa Camacho, Francisco Javier Barrera López, Carlos Jose Zuloaga Fernandez Del Valle, Eric Acosta, Iván Romarico González Espinoza, Perla Moreno, Ana Olivia Cortes-Flores, Clotilde Fuentes Orozco, Alejandro Gonzalez Ojeda, Samantha Corro Díaz González, Laura Martinez, Antonio Ramos-De la Medina, Bonifacio Mosqueda Amador, Armando Novoa, Dennet Arturo Olazo Espejo, Alejandro Jimenez, Federico Lopez Rosales, Elva Gabriela Vanoye, Luis Alberto Garcia Gonzalez, Roberto Carlos Miranda-Ackerman, Manuel Solano-Genesta, Alethia Alvarez-Cano, Hector Hugo Romero-Garza, Heriberto Medina-Franco, Lorelí Mejía-Fernández, Noel Salgado-Nesme, Omar Vergara-Fernandez, Guadalupe Montserrat Gutiérrez-Mota, Francisco Xavier Hernandez Vera, Anabella Llantada Lopez, Gilberto Morgan Villela, Felipe de Jesus Ramirez Padilla, Walezka Tapia Marin, Mónica Martínez Maldonado, Ramses Sánchez Suárez, José Manuel Troche, Chaymae Benyaiche, Oumaima Outani, Souadka Amine, Amine Benkabbou, Anass Mohammed Majbar, Raouf Mohsine, Ali Rafik, Thida Oung, Moe Moe Tin, David W Borowski, Philipp Plarre, David W Borowski, Philipp Plarre, Anna Alberga, Nina Sluiter, Jurriaan Tuynman, Robin Blok, Didem Cömert, Roel Hompes, Marianne Kalff, Merel Elisabeth Stellingwerf, Pieter Tanis, Mark van Berge Henegouwen, Elise Maria van Praag, Daan Wisselink, Michael Gerhards, Josephine Lopes Cardozo, Emma Westerduin, Joske de Jonge, Aaw van Geloven, Kaz van Schilt, Frank den Boer, Simone Stoots, Stijn Vlek, Jamie Adams, Ibrahim S Al-Busaidi, Gabrielle Budd, Seung Il Choi, Michael Jen Jie Chu, Anurag Ganugapati, Lucy McKinstry, Rebecca Pascoe, Simon Richards, Kenrick Rosser, Annie Stevenson, Rebecca White, Shebani Farik, Jin Kwun, Ahmed Murad, Sarah Cowan, Timothy Hall, Michael Hayton, Laminou Malam Sani, Souleymane Oumarou Garba, Harissou Adamou, Ibrahim Amadou Magagi, Oumarou Habou, Halima Aliyu, Muhammad Daniyan, Tunde T Sholadoye, Lawal Abdullahi, Lofty-John Anyanwu, Aminu Mohammad Mohammad, Abubakar Bala Muhammad, Abdurrahman Abba Sheshe, Ibrahim Suleiman, Alaba Adesina, Ajibola Awolowo, Clement Onuoha, Omotayo Salami, Ogechukwu Taiwo, Agboola Taiwo, Stephen Kache, Jerry Godfrey Makama, Danjuma Sale, Olajide Abiola, Akinlabi Ajao, Anthony Ajiboye, Amarachukwu Etonyeaku, Julius Olaogun, Ademola Adebanjo, Opeoluwa Adesanya, Michael Olatunji Afolayan, Olanrewaju Balogun, Ayomide Makanjuola, Samuel Nwokocha, Rufus Wale Ojewola, Thomas Olagboyega Olajide, Adewale Aderounmu, Abdul-Rashid Adesunkanmi, Adewale Adisa, Augustine Agbakwuru, Adeleke Akeem Aderogba, Olusegun Isaac Alatise, Olukayode Arowolo, Oladejo Lawal, Tajudeen Mohammed, Chinedu Ndegbu, Olalekan Olasehinde, Funmilola Wuraola, Akinbolaji Akinkuolie, Amarachukwu Etonyeaku, Arinzechukwu Mosanya, Omobolaji Ayandipo, Peter Elemile, Taiwo Akeem Lawal, Samuel Ali Sani, Stephen Garba, Rebecca Hauwa Sani, Samson Olori, Henry Onyebuashi, Ifeanyi Umoke, Adedire Adenuga, Ademola Adeyeye, Olufemi Habeeb, Bashir Lawal, Abdulrasheed Nasir, Eirik Kjus Aahlin, Didrik Kjønås, Elisabeth Myrseth, Jibran Abbasy, Abdul Alvi, Omair Saleem, Asma Afzal, Anam Nazir, Muhammad Farooq, Ayesha Liaqat, Syed Asghar Naqi, Ali Raza, Muzna Sarfraz, Muhammad Sarwar, Muntaha Banglani, Ambreen Munir, Rahmat Sehrish, Bushra Ayub, Raza Sayyed, Amna Altaf, Saima Ayub, Ahmad Uzair Qureshi, Komal Saeed, Bilal Syed, Sana Amir Akbar, Abdul Wahid Anwer, Ruqayya Naheed Khan, Amina Iqbal Khan, Shahid Khattak, Sameen Mohtasham, Muhammad Asad Parvaiz, Aamir Ali Syed, Abdul Basit Ansari, Noman Shahzad, Tanwir Khaliq, Isbah Rashid, Shahzad Hussain Waqar, Hasan Abu Al-Saleem, Amjad Abu Alqumboz, Mohammad Alqadi, Adham Amro, Rawan Assa, Eman Awesat, Rawan Ayyad, Mohammed Hammad, Ayat Haymony, Bassel Hijazi, Bara Hmeidat, Rowaa Lahaseh, Aseel Qawasmi, Alaa Rajabi, Mohammed Shehada, Sundus Shkokani, Yasmine Yaghi, Nadine Yaghi, Mohammad AlZohour, Mohammad Farid, Yousef Mahmoud Habes, Wesam Juba, Yanal Nubani, Abdelrahman Rabee, Mohammad Sa'deh, Saeed Abed, Iyad Al Basos, Mohammad Alswerki, Dina Ashour, Israa Awad, Samar Diab, Alaa El Jamassi, Sahar El-Kahlout, Somaya Elhout, Ahmed N K Hajjaj, Doaa Hasanain, Baraa Nabil Hajjaj, Mohammed Obaid, Eman Saikaly, Ahmed Salhi, Hiba Al-Tammam, Murad Almasri, Muath Baniowda, Doha Beshtawi, Ali Horoub, Rami Misk, Bayan Mohammad, Rami Qasrawi, Tasnim Sholi, Samar Abu-Nimeh, Abrar Abu-Srour, Sadi A Abukhalaf, Samer Adawi, Barah Alsalameh, Kholoud Ayesh, Muawiyah Elqadi, Ahmad Hammouri, Fatima Karim Mustafa, Natalie Marzouqa, Shatha Melhem, Dima Miqdad, Balqees Mohamad, Mhammed Rawhi, Ayman B Abu Ahammala, Ahmed Abu Ataya, Israa Abu Jayyab, Samar Al-Shwaikh, Othman Alagha, Mohammed Alasttal, Haneen Awadallah, Mahmood Elblbessy, Jehad Fares, Akram Jarbou, Ibtisam Mahfouz, Moath A Albahnasawi, Asmaa' Abo Mahadi, Hasan Abuelhatal, Ayham Abuelqomboz, Abdelrahman Almoqayyad, Abdallah Alwali, Reem Balaawi, Mahmoud Hamouda, Mohammed Humeid, Abdullah Jedyan, Tasneem Mahmoud Abu Hamam, Ghadeer Matar, Ali Salem, Tahani Samra, Nureddin Shaheen, Karam Shihada, Ayoob A Nemer, Mahmoud Abu Al Amrain, Abdulwhhab Abu Alamrain, Najlaa Abu Jamie, Mohammed R Abu-Rous, Nada Alfarra, Mohammed AlTaweel, Noor Alwhaidi, Ramadan Hamed, Bader Saqqa, Ahmad Shaheen, Dana Aljaber, Loay Aljaberi, Malak Alwaheidi, Assef Jawaada, Hani Khaldi, Rami Qahoush, Jalil Qari, Rana Saadeh, Ahlam Salim, Aseel Yacoub, Abbas Abbas, Rana Abu Shua'ib, Baraa Abu Zainah, Mahmoud AbuSirrees, Basheer Babaa, Ola Barhoush, Asef Belal Qadomi, Laith Daraghmeh, Reema Haji, Alaa Khatatbeh, Lana Khatib, Salsabeel Qarariah, Yara Quzmar, Khalil Safadi, Roqaya Salameh, Mohammad Hassan, Shifaa Herzallah, Loai Massad, Ahmed Nazzal, Ranin Nazzal, Dennis Escobar, Gustavo Miguel Machain V, Agustin Rodriguez Gonzalez, Jorge Emerson Chachaima Mar, Nathaly Olga Chinchihualpa Paredes, Vicente Cuba, Walter Lopez, Maria Milagros Niquen Jimenez, Nestor Alberto Sanchez Bartra, Olenka Sapallanay Ojeda, Diego Sequeiros, Andrea Toscano Pacheco, María Vergara, Sol Abarca, Rodrigo Alcorta, Giuliano Borda-Luque, Ivan Edward Eusebio Zegarra, Claudia Luján López, Mirella Marrufo, Cinthya Mogrovejo, Andrea Nomura, Yamile Rodríguez Angeles, Maitza Rosario Vidal Meza, Gabriela Zavala, José Neiser Castillo Arrascue, Jomara Caroline Hidrogo Cabrera, José Julio Mariano Larrea Vera, Miguel Osorio, Edgar Alcides Ylatoma Díaz, Mark Anthony Fontanilla, Joseph Roy Fuentes, Anna Leah Salazar, Genieve Dominguez, Marc Paul Lopez, Shiela Macalindong, Mark Augustine Onglao, Arjel Ramirez, Marie Dione Sacdalan, Mayou Martin Tampo, Gemma Leonora Uy, Jeremiah Mangahas, Kenneth Yabut, Joannes Paul Cañete, Bernalynn Eris Cansana, Ernes John Castro, Maria Kaiserin Lipana, Manuel Francisco Roxas, Vlu Jean Zara, Maciej Chroł, Paula Franczak, Michał Orłowski, Piotr Budzyński, Andrzej Budzyński, Pawel Bury, Agata Czerwińska, Jadwiga Dworak, Jacek Dziedzic, Michał Kisielewski, Jan Kulawik, Anna Lasek, Piotr Major, Piotr Małczak, Marcin Migaczewski, Michał Pędziwiatr, Magdalena Pisarska, Dorota Radkowiak, Mateusz Rubinkiewicz, Anna Rzepa, Tomasz Skoczylas, Maciej Stanek, Katarzyna Truszkiewicz, Mateusz Wierdak, Marek Winiarski, Piotr Zarzycki, Anna Zub-Pokrowiecka, Piotr Kowalewski, Rafał Roszkowski, Maciej Walędziak, Miguel Tomé, Sara Patrocinio, Ines Guerreiro, Filipe Almeida, Xavier de Sousa, Nuno Monteiro, Maria Teresa Costa Santos, Daniela de Oliveira, Marta Lopes Serra, Daniela Morgado, Christian Neves, Ana Carolina Oliveira, Alice Pimentel, Sofia Silva, Márcia Carvalho, Lúcia Carvalho, Joana Magalhães, Leonor Matos, Tânia Monteiro, Carlota Ramos, Vanessa Santos, José Barbosa, Jose Costa-Maia, Vítor Devezas, Ana Fareleira, Cristina Fernandes, Diana Gonçalves, Henrique Mora, Marina Morais, Fabiana Silva de Sousa, Sara Catarino Santos, Ana Logrado, André Tojal, Edgar Amorim, Miguel F Cunha, Ana Fazenda, João Pedro Melo Neves, Inês Isabel Sampaio da Nóvoa Gomes Miguel, Diogo Veiga, José Azevedo, Hugo Cardoso Louro, Mariana Leite, José Azevedo, Maria Bairos Menezes, Bárbara Gama, Diana Brito, Marta Cristina Cruz Martins, André Graça E Magalhães, Ana Catarina Longras, Rita Lourenço, Diana Matos, Luis Castro, Filipa Policarpo, Joana Romano, Mariana Leite, Cristina Monteiro, Diogo Pinto, Marina Duarte, Sónia Fortuna Martins, Mariline Oliveira, Diogo Galvão, Lisandra Martins, Anaisa Silva, Viorel Taranu, Bárbara Vieira, Jessica Neves, Simone Oliveira, Hugo Ribeiro, Margarida Cinza, Rosa Felix, Arnaldo Machado, Joana Oliveira, Joana Patrício, Rita Pedroso de Lima, Mário Pereira, Miguel Rocha Melo, Cristina Velez, Alberto Abreu da Silva, Mariana Claro, Daniel Costa Santos, Andreia Ferreira, Hugo Capote, Daniela Rosado, Filipa Taré, Oriana Nogueira, Miguel Ângelo, José Miguel Baiao, Andreia Guimarães, João Marques, Miguel Nico Albano, Marta Silva, Ana Valente da Costa, Teresa Vieira Caroço, Sara Almeida Braga, Ines Capunge, Marta Fragoso, João Guimarães, Bruno Pinto, João Ribeiro, Miguel Angel, Guilherme Fialho, Monica Guerrero, Filipa Campos Costa, Diogo Cardoso, Vasco Cardoso, Magda Alves, Inês Estalagem, Tiago Louro, Cláudia Marques, Rita Martelo, Miguel Morgado, Rita Canotilho, Ana Margarida Correia, Pedro 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Noor, Reantha Pillay, Leandi Steynfaardt, Lucía González González, José Miguel Marín Santos, Paula Martín-Borregón, Javier Martínez Caballero, Cristina Nevado García, Pastora Rodriguez Fraga, Gonzalo De Castro Parga, Maria Pilar Fernández Veiga, Lucía Garrido López, Hugo Infante Pino, Irene Lages Cal, Marta López Otero, Manuel Nogueira Sixto, Marta Paniagua García Señorans, Laura Rodríguez Fernández, Alejandro Ruano Poblador, Erika Rufo Crespo, Raquel Sanchez-Santos, Vincenzo Vigorita, Ester Alonso Batanero, Dorisme Asnel, Isabel Cifrian Canales, Elisa Contreras Saiz, Irene De Santiago Alvarez, Tamara Díaz Vico, Sebastian Fernandez Arias, Daniel Fernández Martínez, Carmen García Bernardo, Luis Joaquín García Flórez, Carmen Garcia Gutierrez, Manuel García Munar, Carlos Alberto Márquez Zorrilla Molina, Marta Merayo, José Luis Michi Campos, Maria Moreno Gijon, Jorge L Otero-Diez, Jose Luis Rodicio Miravalles, Lorena Solar-Garcia, Aida Suárez Sánchez, Nuria Truan, Cristina Alejandre 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P., Nsengimana, V. J. P., Uwakunda, C., Abbas, M. M., Akeel, N., Aljiffry, M., Awaji, K., Farsi, A., Jamjoum, G., Khoja, A., Maghrabi, A., Malibary, N., Nassif, M., Saleem, A., Sultan, A., Tashkandi, W., Tashkandi, H., Trabulsi, N., Ba, M. B., Diallo, A. C., Ndong, A., Cuk, V., Jankovic, U., Koh, S. Z., Koh, F., Lee, K. C., Lee, K. Y., Lee, S., Leong, W. Q., Lui, S. A., Prakash, P., Grosek, J., Norcic, G., Tomazic, A., Fitchat, N., Jaich, R., Wineberg, D., Koto, M. Z., Baiocchi, D., Clarke, D., Steenkamp, C. J., Bannister, S., Boutall, A., Chinnery, G., Coccia, A., Dell, A., Karjiker, P., Kloppers, C., Loxton, N., Mabogoane, T., Malherbe, F., Panieri, E., Rayamajhi, S., van Wyngaard, T., Warden, C., Madiba, T. E., Pillay, N., Brooks, S., Kruger, C., Van Der Merwe, L. H., Gool, F., Kariem, M., Bougard, H., Kariem, N., Noor, F., Pillay, R., Steynfaardt, L., Gonzalez Gonzalez, L., Marin Santos, J. M., Martin-Borregon, P., Martinez Caballero, J., Nevado Garcia, C., Rodriguez Fraga, P., De Castro Parga, G., Fernandez Veiga, M. P., Garrido Lopez, L., Infante Pino, H., Lages Cal, I., Lopez Otero, M., Nogueira Sixto, M., Paniagua Garcia Senorans, M., Rodriguez Fernandez, L., Ruano Poblador, A., Rufo Crespo, E., Sanchez-Santos, R., Vigorita, V., Alonso Batanero, E., Asnel, D., Cifrian Canales, I., Contreras Saiz, E., De Santiago Alvarez, I., Diaz Vico, T., Fernandez Arias, S., Fernandez Martinez, D., Garcia Bernardo, C., Garcia Florez, L. J., Garcia Gutierrez, C., Garcia Munar, M., Marquez Zorrilla Molina, C. A., Merayo, M., Michi Campos, J. L., Moreno Gijon, M., Otero-Diez, J. L., Rodicio Miravalles, J. L., Solar-Garcia, L., Suarez Sanchez, A., Truan, N., Alejandre Villalobos, C., Caballero Diaz, Y., Jimenez, M., Montesdeoca, D., Navarro-Sanchez, A., Vega, V., Beltran de Heredia, J., Gomez, Z., Jezieniecki, C., Legido Moran, A. P., Montes-Manrique, M., Rodriguez-Lopez, M., Ruiz Soriano, M., Trujillo Diaz, J., Vazquez Fernandez, A., Argudo, N., Pera, M., Torrent Jansa, L., Garcia Dominguez, M., Goded, I., Roldon Golet, M., Talal El-Abur, I., Utrilla Fornals, A., Zambrana Campos, V., Aguilar Martinez, M. D. M., Bosch, M., Garcia-Catala, L., Sanchez-Guillen, L., Artigau, E., Gomez Romeu, N., Julia Bergkvist, D., Espina Perez, B., Morato, O., Olona, C., Dieguez, B., Forero-Torres, A., Losada, M., Gomez-Abril, S., Gonzalvez, P., Martinez, R., Navarro Martinez, S., Paya-Llorente, C., Perez Rubio, A., Santarrufina Martinez, S., Sebastian Tomas, J. C., Trullenque Juan, R., Gegundez Simon, A., Mate, P., Prieto-Nieto, M. I., Rubio-Perez, I., Urbieta, A., Vicario Bravo, M., Abello, D., Frasson, M., Garcia-Granero, A., Abad Gurumeta, A., Abad-Motos, A., Lucena-de Pablo, E., Nozal, B., Ripolles-Melchor, J., Salvachua, R., Ferrero, E., Garcia-Sancho Tellez, L., Picardo, A. L., Rojo Lopez, J. A., Zorrilla Matilla, L. P., Cagigas Fernandez, C., Castanedo Bezanilla, S., Estevez Tesouro, J., Fernandez-Diaz, M. J., Garcia Cardo, J., Gomez Ruiz, M., Gonzalez-Tolaretxipi, E., Jimeno Fraile, J., Poch, C., Rodriguez-Aguirre, M., Troche Pesqueira, N., Trugeda-Carrera, M. S., de la Torre, J., Blanco-Colino, R., Espin-Basany, E., Espinosa-Bravo, M., Morales Comas, C., Reyes Afonso, E., Rivero Deniz, J., Siso Raber, C., Verdaguer Tremolosa, M., Chandrasinghe, P., Kumarage, S., Wijekoon Arachchilage, N., Abdalla Ahmed Elkamel, A., A. Adam, M., Blomme, N., Thorell, A., Wogensen, F., Alga, A., Ansarei, D., Celebioglu, F., Heinius, G., Nigard, L., Pieniowski, E., Ahlqvist, S., Bjorklund, I., Franberg, A., Hakansson, M., Adamo, K., Franklin, O., Sund, M., Wiberg, R., Andersson, Y., Chabok, A., Nikberg, M., Kugelberg, A., Canonica, C., Christoforidis, D., Fasolini, F., Gaffuri, P., Giuliani, M., Meani, F., Popeskou, S. G., Pozza, S., Wandschneider, W., Peterer, L., Widmer, L. W., Zimmermann, B., Bakoleas, P., Chanousi, I., Charalampidou, L., Grochola, L. F., Heid, F., Ntaoulas, S., Outos, M., Peros, G., Podolska-Skoczek, H., Reinisch, K. B., Zielasek, C., Demartines, N., Gilgien, J., Kefleyesus, A., St-Amour, P., Toussaint, A., Alhimyar, M., Alsaid, B., Alyafi, A., Alkhaledi, A., Kouz, B., Omarain, A., Al-Sabbagh, Y., Alkhatib, H., Sara, S., Alhaj, A., Danial, A., Kadoura, L., Maa Albared, S., Monawar, Y., Nahas, L., Abd, B., Saad, A., Wakkaf, H., Bouzaiene, H., Ghalleb, M., Akaydin, E., Akbaba, A. C., Atakul, O., Baltaci, E., Besli, S., Burgu, G., Cenal, U., de Muijnck, C., Demirkaya, H. C., Dogruoz, A., Gezer, Z. I., Gundogdu, Y., Kara, M., Korkmaz, H. K., Kurtoglu, G. K., Ozben, V., Ozmen, B. B., Pektas, A. M., Sel, E. K., Yenidunya, N., Bengur, F. B., Oral, B. M., Yozgatli, T. K., Abdullayev, S., Gunes, M. E., Sahbaz, N. A., Banaz, T., Kargici, K., Kuyumcu, O. F., Yanikoglu, E., Yesilsancak, M., Yilmaz, D., Aktas, M. K., Rencuzogullari, A., Isik, A., Leventoglu, S., Yalcinkaya, A., Yuksel, O., Kalayci, M. U., Kara, Y., Sarici, I. S., Akin, A., Alemdag, G. N., Arslan, E., Baki, B. E., Bodur, M. S., Calik, A., Candas Altinbas, B., Cihanyurdu, I., Erkul, O., Gul, B., Guner, A., Kose, B., Semiz, A., Sevim, Tayar, S., Tomas, K., Tufek, O. Y., Turkyilmaz, S., Ulusahin, M., Usta, A., Yildirim, R., Guler, S. A., Tatar, O. C., Varol, E., Kirimtay, B., Uysal, M., Yildiz, A., Kose, E., Ciftci, A. B., Colak, E., Eraslan, H., Kucuk, G. O., Yemez, K., Lule, H., Bienfait, M., Bua, E., Okalany, N., Basarab, M., Bielosludtsev, O., Kolhanova, K., Perepelytsia, K., Romanukha, K., Savenkov, D., Siryi, S., Tereshchenko, M., Viacheslav, N., Volovetskyi, A., Kebkalo, A., Tryliskyy, Y., Tyselskiy, V., Bruce, E., Chow, B. L., Iddles, E., Mcguckin, S., Newall, N., Ramsay, G., Sharma, P., Stewart, C., Wong, J., Badran, A., Bath, M., Belais, F., Butt, E., Joshi, K., Kapur, M., Shaw, M., Townson, A., Williams, C. Y. K., Gray, T., Greig, R., Husain, M., Murray, E., Mustafa, A., Asif, A., Gokul, A., Shah, M., Akitikori, M. T., Charalabopoulos, A., Davidson, S., Mcnally, S., Rupani, S., Juma, F., Mills, S. C., Muirhead, L., Sellars, K., Walsh, U., Warren, O., Chambers, A., Hunt, R., Boyce, S., Cornwall, H., Tol, I., Argyriou, E. O., Eardley, N., Povey, M., Aithie, J. M. S., Irfan, A., Mcguigan, M. -C., Starr, R., Warren, C. R., Archibald, J., Kirby, G., Kisyov, I., Khoo, C. K., Lee, R., Photiou, D., Davis, R., Prasad, U., Yang, P. Z., Bird, J., Leung, E., Summerour, V., Currow, C., Kiam, J., Tan, G. J. S., Muthusami, A., Pegba-Otemolu, I., Urbonas, T., Nunoo-Mensah, J., Smolskas, E., Boddy, A., Gravante, G., Hunter, D., Andrew, D., Koh, A., Thompson, A., Adams, L., Clements, H. A., De Silva, K., Ekpete, O., Haque, S., Henderson, S., Ibrahim, B., Jayasinghe, T., Livie, J., Mailley, K., Nair, G., Tan, D., Baggaley, C., Dawidziuk, A., Szyszka, B., Barter, C., Gandhi, N., Hassell, K., Hitchin, S., Kelsall, J., Nagy, E., Nessa, A., Whisker, L., Yanni, F., Ali, M., Arora, D., Hediwattege, S., Kumarasinghe, N., Rathore, M., Tennakoon, A., Ali Ahmad, S. M., Bajomo, O., Nadira, F., Celentano, V., Griffiths, E., Karri, R. S., Mak, J. K. C., Pipe, M., Bhatti, M. I., Rabie, M., Boyle, C., Hamilton, D., Mihuna, A., Ng, J. C. K., Nicholson, G., Oliwa, A., Pearson, R., Rose, A., Yong, S. Q., Boereboom, C., Hanna, M., Walter, C., Greensmith, T. S., Mitchell, R., Monaghan, E., Crawford, J., Moug, S., Blackwell, J., Boyd-Carson, H., Herrod, P., Al-Allaf, O., Beattie, M., Bullock, C., Burman, S., Clark, G., Flamey, N., Flannery, O., Harding, A., Kodiatt, B., Lawday, S., Mahapatra, S., Mukundu Nagesh, N., Ng, M., Rye, D., Yoong, A., Clark, L., Deans, C., Edirisooriya, M., Carrington, E. V., Wong, T. L. E., Yusuf, B., Chamberlain, C., Duke, K., Kmiotek, E., Botes, A., Condie, N., Schrire, T., Shah, R., Thomas-Jones, I., Yates, C., Anthony, N., Matthews, E., Sahnan, K., Tankel, J., Tucker, S., Winter Beatty, J., Ziprin, P., Duggan, W., Kantartzi, A., Sridhar, S., Khaw, R. A., Srivastava, P., Underwood, C., Alves do Canto Brum, H., Chopra, S., Davis, L., Hughes, R., Tulley, J., Alberts, J., Athisayaraj, T., Olugbemi, M., Ahmad, K., Chan, C., Chapman, G., Fleming, H., Fox, B., Grewar, J., Hulse, K., Rutherford, D., Sinead, M., Speake, D., Vaughan-Shaw, P. G., Christodoulides, N., Kudhail, S., Welch, M., Husaini, S. M., Lambracos, S., Anyanwu, C., Suresh, R., Thomas, J. S., Gleeson, E., Platoff, R., Saif, A., Enumah, Z., Etchill, E., Gabre-Kidan, A., Bernstein, M., Carrano, F. M., Connors, J., Lynn, P., Melis, M., Newman, E., Foster, D. S., Perrone, K., Titan, A., Ahmad, S., Bafford, A. C. M. D., Dal Molin, M., Hanna, N., Zafar, S. N., Hemmila, M., Napolitano, L., Wong, J. J., Chandler, J., Wood, L., Wren, S., Ottesen, T., You, L., Yu, K., Arcienega Yanez, M. D. P., Ferreira Fernandes, M., Gonzalez, D., Cubas, S., Gonzalez, M. C., Zubiaurre, V., Demolin, R., Giroff, N., Sciuto, P., Campos, M., Rodriguez Cantera, G., Deepika, G., Simuchimba, E., Bulaya, A., Chibuye, C., Chirengendure, B., Kabale, M. -R., Kabongo, K., Munthali, J., Mweso, O., Pikiti, F., Otieno, J., Lai, L. T., Blackman, B., Subramaniam, S., Karim, R., Kok, N., Lee, Y. D., Ali, S., Sinha, A., Corrigan, R., Barnes, N., Wong, F., Dennis, G., Jedamzik, J., Phillips, E., Piette, W., Van hentenryck, M., Koco, H., Lawani, S., Kassa, M. W., Santos Bezerra, T., Gribnev, P., Dimitrov, D., Krastev, P., Oum, S., Bonghaseh, D. T., Al Farsi, M., Alsharqawi, N., Acevedo, V., Castillo Barbosa, A. C., Giron, F., Leon Rodriguez, J. P., Kucan, D., Rosko, D., Barsic, N., Zupan, D., Hegazi, A., Truncikova, V., Fryba, V., Mohamed, M., Nagi, A., Rashad Temerik, A., Elshawy, M. E., Mahmoud, M. I., Anwar, M., Rageh, T., Elmokadem, A., Gaballa, K., Teppo, S., Pengerma, P., Ballouhey, Q., Bergeat, D., Weyl, A., Hain, E., Gyedu, A., Yenli, E., Osei-Poku, D., Rompou, V. -A., Zoikas, A., Gaitanidis, A., Koukis, G., Perivoliotis, K., Tavlas, P., Galanos-Demiris, K., Zografos, G., Karavokyros, I., Xanthopoulou, G., Iordanidou, E., Ayau, F., Garcia, A., Damjan, P., Wason, D., B L, A., Rangganata, E., Kamath, P., O'Connor, D. B., Pinto, M., Perrone, F., Tropeano, F. P., Troilo, F., Bossi, D., Scala, D., Pulitano, L., Carella, M., Pietrabissa, A., Gori, A., Giraudo, G., De Simone, V., Russo, A. A., Braccio, B., Al-Taher, R., Athamneh, S., Parker, A., Sawiee, A., Kattia, A., Tababa, O., Shaeeb, Z., Syminas, V., Jurgaitis, J., Damuleviciene, G., Svagzdys, S., Razafimanjato, N. N. M., Chieng Loo, L., Tiong, I. C., Wan Muhmad, W. F., Vijeyan, H., Li Ying, T., Grech, G., Arrangoiz, R., Jimenez Ley, V. B., Arizpe, D., Lagunes Lara, E., Castro Lopez, E. V., Eaazim, J., Gordinou de Gouberville, M., Bastiaenen, V., Rottier, S., Nahab, F., Ji, M. Y., Seyoji, M., Nwachukwu, C., Emeghara, O., Muhammed, S. E., Idowu, A., Sowemimo, O., Ogundoyin, O., Akande, O., Lott, A., Nadeem, M., Laghari, A. A., Loya, A., Mushtaq, H., Abdullah, M. T., Abuhilal, B., Atawneh, M., Hamdan, H., Alhabil, B., Srour, A., Mousa, I., Da Silva Medina, L., Bartosiak, K., Ferreira, P., Francisco, V., Lemos, R., Frutuoso, L., Fernandes, S., Fonseca, T., Pereira, J., Rachadell, J., Torre, A., Madeira Martins, F., Carvalho, A. C., Rodrigues Ferreira, J., Ribeiro da Silva, B., Devesa, H., Vieira, A., Monica, I., Amaro, M., Sousa, D., Reia, M., Louro, J., Martins, A., Dominguez, J., Santos, I., Freitas Oliveira, N. M., Pereira, J. C., Silva-Vaz, P., Freire, L., Escrevente, R., Negoita, V. M., Shakhmatov, D., Nezerwa, Y., Radulovic, R., Obery, G., Viljoen, F., Mendes, T., Suarez, A., Moncada, E., Fernandez-Hevia, M., Curtis Martinez, C., Gil Garcia, J. M., Gonzalez Zunzarren, M., Idris, T., Eklov, K., Grahn, O., Amin, L., Blomqvist, M., Ajani, C., Kraus, R., Seeger, N., Willemin, M., Rayya, F., Ayash, M., Msouti, R., Kannas, I., Abazid, E., Esper, A., Slim, S., Kavcar, A. S., Aytac, E., Dural, A. C., Ilker, A., Eray, I. C., Kurnaz, E., Altiner, S., Tepe, M. D., Sahin, C., Savli, E., Innocent, A., Babirye, L., Diachenko, A., Hordoskiy, V., Curry, H., Chau, C. Y. C., Robertson, H., Lennon, H., Loi, L., Kirkham, E., Mccann, C., Watts, D., Gurung, B., Wilson, M., Tribedi, T., Garofalo, E., Zahra, B., Macdonald, S., Daniels, I., Ng, N., Khosla, S., Olivier, J., Yue, S. Y. P., Suresh, G., Wellington, J., Lorejo, E., Mossaad, M., Crutcher, M., Alimi, M., Baiu, I., Abdou, H., Conway, A., Peck, C., Perdomo Perez, M. A., Zulu, S., Nakazwe, M., Burger, S., Davies, J., Donaldson, R., Ede, C., Garden, O. J., Lesetedi, C., Mabedi, C., Magill, L., Makinde Alakaloko, F., Makupe, A., Monahan, M., Mulira, S., Muller, E., Musowoyo, J., Olory-Togbe, J. L., Roberts, T., Smith, M., Tayler, V., Windsor, J., Yepez, R., Sundar, S., Runigamugabo, E., Verjee, A., Chen, J., Daya, L., El Aroussi, N., Farina, V., Gnintedeme Olivier, T., Gonzales Nacarino, M., Hammani, A., Honjo, S., Jacobs, R., Kimura, H., Nkoronko, M., Oscullo Yepez, J. J., Pin Hung, W., Raj, A., Romani Pozo, A., Rommaneh, M., Sassamela Fabiano, S. C., Shiroma Gago, C. M., Srinivas, A., Sung, C. -Y., Tai, A., Valle Aranda, Y. C., Venturini, S., Wilguens Lartigue, J., Public and occupational health, AMS - Sports, APH - Health Behaviors & Chronic Diseases, CCA - Cancer Treatment and quality of life, Amsterdam Gastroenterology Endocrinology Metabolism, Cancer Center Amsterdam, and Albarella, Sara
Subjects: global surgery, hospitals, humans, income, neoplasms, postoperative period, poverty, prospective studies, no keywords listed, General Medicine, Prospective Studie, Hospital, Settore MED/18 - Chirurgia Generale, outcome, Hospitals, Humans, Income, Neoplasms, surgery, Postoperative Period, Poverty, Prospective Studies, cancer surgery, Human, hospital facilities
Abstract: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p
Published: 2022

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35 results on '"Díaz, Mark"'

1. Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups

2. STAR: SocioTechnical Approach to Red Teaming Language Models

3. D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation

4. Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation

5. The illusion of artificial inclusion

6. Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates

7. SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata

8. GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives

9. Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety

10. DICES Dataset: Diversity in Conversational AI Evaluation for Safety

11. PaLM 2 Technical Report

12. (Re)Defining Expertise in Machine Learning Development

13. The Reasonable Effectiveness of Diverse Evaluation Data

14. Power to the People? Opportunities and Challenges for Participatory AI

15. CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation

16. PaLM: Scaling Language Modeling with Pathways

17. LaMDA: Language Models for Dialog Applications

18. Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

19. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

20. On Releasing Annotator-Level Labels and Information in Datasets

21. Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates

22. Nontuberculous mycobacterial infections of the lower extremities: A 15-year experience

23. A Framework to Assess (Dis)agreement Among Diverse Rater Groups

24. Addressing Medicine’s Dark Matter

25. CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation

26. Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat

27. Viewpoint: Addressing Medicine’s Dark Matter (Preprint)

28. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

29. Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

30. Biases as Values: Evaluating Algorithms in Context

31. Platforming Intersectionality: Networked Solidarity and the Limits of Corporate Social Media

32. "The cavalry ain't coming in to save us"

33. Addressing Age-Related Bias in Sentiment Analysis

34. Abstract #639 Thyroid Storm in an HIV Patient: A Late Manifestation of Immune Reconstitution Syndrome

35. Effects of hospital facilities on patient outcomes after cancer surgery

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35 results on '"Díaz, Mark"'

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