Do Machines Replicate Humans? Toward a Unified Understanding of Radicalizing Content on the Open Social Web
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PROVIDER: S-EPMC10647225 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
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Project description:Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term's specificity in the GO DAG.
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Project description:The current rise in the use of open lab notebook techniques means that there are an increasing number of scientists who make chemical information freely and openly available to the entire community as a series of micropublications that are released shortly after the conclusion of each experiment. We propose that this trend be accompanied by a thorough examination of data sharing priorities. We argue that the most significant immediate benefactor of open data is in fact chemical algorithms, which are capable of absorbing vast quantities of data, and using it to present concise insights to working chemists, on a scale that could not be achieved by traditional publication methods. Making this goal practically achievable will require a paradigm shift in the way individual scientists translate their data into digital form, since most contemporary methods of data entry are designed for presentation to humans rather than consumption by machine learning algorithms. We discuss some of the complex issues involved in fixing current methods, as well as some of the immediate benefits that can be gained when open data is published correctly using unambiguous machine readable formats. Graphical AbstractLab notebook entries must target both visualisation by scientists and use by machine learning algorithms.
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Project description:ObjectivesLinking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.Materials and methodsTables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB.ResultsThere was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information.DiscussionIt is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity.ConclusionThe classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.
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Project description:Human perceptions of fairness in (semi-)automated decision-making (ADM) constitute a crucial building block toward developing human-centered ADM solutions. However, measuring fairness perceptions is challenging because various context and design characteristics of ADM systems need to be disentangled. Particularly, ADM applications need to use the right degree of automation and granularity of data input to achieve efficiency and public acceptance. We present results from a large-scale vignette experiment that assessed fairness perceptions and the acceptability of ADM systems. The experiment varied context and design dimensions, with an emphasis on who makes the final decision. We show that automated recommendations in combination with a final human decider are perceived as fair as decisions made by a dominant human decider and as fairer than decisions made only by an algorithm. Our results shed light on the context dependence of fairness assessments and show that semi-automation of decision-making processes is often desirable.
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Project description:BackgroundAn increasing number of patients from diverse demographic groups share and search for health-related information on Web-based social media. However, little is known about the content of the posted information with respect to the users' demographics.ObjectiveThe aims of this study were to analyze the content of Web-based health-related social media based on users' demographics to identify which health topics are discussed in which social media by which demographic groups and to help guide educational and research activities.MethodsWe analyze 3 different types of health-related social media: (1) general Web-based social networks Twitter and Google+; (2) drug review websites; and (3) health Web forums, with a total of about 6 million users and 20 million posts. We analyzed the content of these posts based on the demographic group of their authors, in terms of sentiment and emotion, top distinctive terms, and top medical concepts.ResultsThe results of this study are: (1) Pregnancy is the dominant topic for female users in drug review websites and health Web forums, whereas for male users, it is cardiac problems, HIV, and back pain, but this is not the case for Twitter; (2) younger users (0-17 years) mainly talk about attention-deficit hyperactivity disorder (ADHD) and depression-related drugs, users aged 35-44 years discuss about multiple sclerosis (MS) drugs, and middle-aged users (45-64 years) talk about alcohol and smoking; (3) users from the Northeast United States talk about physical disorders, whereas users from the West United States talk about mental disorders and addictive behaviors; (4) Users with higher writing level express less anger in their posts.ConclusionWe studied the popular topics and the sentiment based on users' demographics in Web-based health-related social media. Our results provide valuable information, which can help create targeted and effective educational campaigns and guide experts to reach the right users on Web-based social chatter.
| S-EPMC4923586 | biostudies-literature
Project description:Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on others that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking of difficulty for our transforms for human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.
| S-EPMC10600046 | biostudies-literature
Project description:This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive science. However, the lack of a standardized definition, empirical evidence, or measurement strategy for intelligence is a hindrance to cross-fertilization between these areas, particularly for semantic-based applications. This paper seeks to equalize the definitions of intelligence from the perspectives of computational intelligence and psychology, and offer an overview of the methods used to measure intelligence. We argue that there is no consensus for intelligence, and the term is interchangeably used with similar, opposed, or even contradictory definitions in many fields. This paper concludes with a summary of its central considerations and contributions, where we state intelligence is an agent's ability to process external and internal information to find an optimum adaptation (decision-making) to the environment according to its ontology and then decode this information as an output action.
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Project description:In the present study, we investigate and compare reasoning in large language models (LLMs) and humans, using a selection of cognitive psychology tools traditionally dedicated to the study of (bounded) rationality. We presented to human participants and an array of pretrained LLMs new variants of classical cognitive experiments, and cross-compared their performances. Our results showed that most of the included models presented reasoning errors akin to those frequently ascribed to error-prone, heuristic-based human reasoning. Notwithstanding this superficial similarity, an in-depth comparison between humans and LLMs indicated important differences with human-like reasoning, with models' limitations disappearing almost entirely in more recent LLMs' releases. Moreover, we show that while it is possible to devise strategies to induce better performance, humans and machines are not equally responsive to the same prompting schemes. We conclude by discussing the epistemological implications and challenges of comparing human and machine behavior for both artificial intelligence and cognitive psychology.
| S-EPMC11332180 | biostudies-literature
Project description:BackgroundDespite the reinstatement of proactive human papillomavirus (HPV) vaccine recommendations in 2022, Japan continues to face persistently low HPV vaccination rates, which pose significant public health challenges. Misinformation, complacency, and accessibility issues have been identified as key factors undermining vaccine uptake.ObjectiveThis study aims to examine the evolution of public attitudes toward HPV vaccination in Japan by analyzing social media content. Specifically, we investigate the role of misinformation, public health events, and cross-vaccine attitudes (eg, COVID-19 vaccines) in shaping vaccine hesitancy over time.MethodsWe collected tweets related to the HPV vaccine from 2011 to 2021. Natural language processing techniques and large language models (LLMs) were used for stance analysis of the collected data. Time series analysis and latent Dirichlet allocation topic modeling were used to identify shifts in public sentiment and topic trends over the decade. Misinformation within opposed-stance tweets was detected using LLMs. Furthermore, we analyzed the relationship between attitudes toward HPV and COVID-19 vaccines through logic analysis.ResultsAmong the tested models, Gemini 1.0 pro (Google) achieved the highest accuracy (0.902) for stance analysis, improving to 0.968 with hyperparameter tuning. Time series analysis identified significant shifts in public stance in 2013, 2016, and 2020, corresponding to key public health events and policy changes. Topic modeling revealed that discussions around vaccine safety peaked in 2015 before declining, while topics concerning vaccine effectiveness exhibited an opposite trend. Misinformation in topic "Scientific Warnings and Public Health Risk" in the sopposed-stance tweets reached a peak of 2.84% (47/1656) in 2012 and stabilized at approximately 0.5% from 2014 onward. The volume of tweets using HPV vaccine experiences to argue stances on COVID-19 vaccines was significantly higher than the reverse.ConclusionsBased on observation on the public attitudes toward HPV vaccination from social media contents over 10 years, our findings highlight the need for targeted public health interventions to address vaccine hesitancy in Japan. Although vaccine confidence has increased slowly, sustained efforts are necessary to ensure long-term improvements. Addressing misinformation, reducing complacency, and enhancing vaccine accessibility are key strategies for improving vaccine uptake. Some evidence suggests that confidence in one vaccine may positively influence perceptions of other vaccines. This study also demonstrated the use of LLMs in providing a comprehensive understanding of public health attitudes. Future public health strategies can benefit from these insights by designing effective interventions to boost vaccine confidence and uptake.
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