PUBBLICAZIONE/PUBLICATION

Permanent URI for this communityhttps://gendermore.unimore.it/handle/123456789/1575

Browse

Settings

Subject: search.filters.subject.Artificial Intelligence

Search Results

Now showing 1 - 3 of 3
  • Item type:Publication,
    Gender-specific patterns in the artificial intelligence scientific ecosystem Open PDF 
    (2022)
    Hajibabaei, A.
    ;
    Schiffauerova, A.
    ;
    Ebadi, A.
    Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000–2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field. © 2022
  • Item type:Publication,
    Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China
    (Institute of Electrical and Electronics Engineers Inc., 2018)
    Zhao, H.
    ;
    Kamareddine, F.
    Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China. © 2017 IEEE.
  • Item type:Publication,
    Female Authorship Trends Among Articles About Artificial Intelligence in North American Radiology Journals Open PDF 
    (2023)
    Yan, T.D.
    ;
    Yuan, P.H.
    ;
    Saha, T.
    ;
    Lebel, K.
    ;
    Spalluto, L.
    Purpose: To examine trends in female authorship of peer-reviewed North American radiology articles centred around artificial intelligence (AI). Method: A bibliographic search was conducted for all AI-related articles published in four North American radiology journals. Collected data included the genders of the first and last (senior) authors, year and country. We compared the trends of female authorship using Pearson chi-square, Fisher exact tests and logistic regression models. Results: 453 articles met the inclusion criteria. Among these, 107 (22.3%) had a female first author and 97 (27.3%) had a female senior author. Female first authors were over three times more likely to publish with a female senior author. Among the four journals, the CARJ had the highest proportion of female senior authors at 45.5%. The only significant temporal trend identified was an increase over the years in female senior authors in Radiology. Twenty-four countries contributed to the included articles, with the largest contributors being the United States (n = 290) and Canada (n = 30). Of the countries contributing more than 15 articles, there were none with above 50% female authorship. Conclusions: Female authors are underrepresented in AI-related radiology literature. However, there has been an encouraging recent increase in female authorship in AI-related radiology articles trending towards significance. There is a great opportunity to improve female representation in AI with intentional mentorship and recruitment. We urge more platforms for female voices in radiology as AI becomes increasingly integrated into the radiology community. © The Author(s) 2022.