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The potential side-effects and fairness implications of such approaches are unknown, however. Using a large-scale bibliographic data set of <jats:italic>N<\/jats:italic> = 111,156 Computer Science researchers active from 1993 to 2016, I build and evaluate a realistic scientific impact prediction model. Given the persistent under-representation of women in Computer Science, the model is audited for disparate impact based on gender. Random forests and Gradient Boosting Machines are used to predict researchers\u2019 <jats:italic>h<\/jats:italic>-index in 2010 from their bibliographic profiles in 2005. Based on model predictions, it is determined whether the researcher will become a high-performer with an <jats:italic>h<\/jats:italic>-index in the top-25% of the discipline-specific <jats:italic>h<\/jats:italic>-index distribution. The models predict the future <jats:italic>h<\/jats:italic>-index with an accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$R^2 = 0.875$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mi>R<\/mml:mi>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mn>0.875<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and correctly classify 91.0% of researchers as high-performers and low-performers. Overall accuracy does not vary strongly across researcher gender. Nevertheless, there is indication of disparate impact against women. The models under-estimate the true <jats:italic>h<\/jats:italic>-index of female researchers more strongly than the <jats:italic>h<\/jats:italic>-index of male researchers. Further, women are 8.6% less likely to be predicted to become high-performers than men. In practice, hiring, tenure, and funding decisions that are based on model predictions risk to perpetuate the under-representation of women in Computer Science.<\/jats:p>","DOI":"10.1007\/s11192-022-04337-2","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T18:03:51Z","timestamp":1649354631000},"page":"6695-6732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predicting the future impact of Computer Science researchers: Is there a gender bias?"],"prefix":"10.1007","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7324-4722","authenticated-orcid":false,"given":"Matthias","family":"Kuppler","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"issue":"1","key":"4337_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.joi.2014.11.002","volume":"9","author":"G Abramo","year":"2015","unstructured":"Abramo, G., Cicero, T., & D\u2019Angelo, C. 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