Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for Predictive Tasks in Education
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Learning Technologies"
DOI: 10.1109/tlt.2022.3196278
Abstract: Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often… read more here.
Keywords: accuracy; class; balancing techniques; algorithmic bias ... See more keywords
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Medical Ethics"
DOI: 10.1136/jme-2022-108850
Abstract: Background There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). Objectives Our objectives are to canvas the range… read more here.
Keywords: bias; artificial intelligence; algorithmic bias; study ... See more keywords
Sexual and geocultural algorithmic imaginaries: Examining approaches of participatory resignation among LGBTQ+ Instagrammers in Berlin and Montreal
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Cultural Studies"
DOI: 10.1177/13678779241267292
Abstract: This study builds on theories of user imaginaries by examining how LGBTQ+ creators in Montreal, Canada and Berlin, Germany respond to perceived algorithmic bias. Through observation and close reading of creators’ Instagram content, the study… read more here.
Keywords: resignation; algorithmic imaginaries; participatory resignation; algorithmic bias ... See more keywords
Evolution and impact of bias in human and machine learning algorithm interaction
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DOI: 10.1371/journal.pone.0235502
Abstract: Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is… read more here.
Keywords: information; machine learning; learning algorithms; algorithmic bias ... See more keywords