Paper Metadata: 2024.gebnlp-1.5, closes #3898.

data/xml/2024.gebnlp.xml

@@ -64,7 +64,7 @@
       <title>A Fairness Analysis of Human and <fixed-case>AI</fixed-case>-Generated Student Reflection Summaries</title>
       <author><first>Bhiman</first><last>Baghel</last><affiliation>University of Pittsburgh</affiliation></author>
       <author><first>Arun Balajiee</first><last>Lekshmi Narayanan</last><affiliation>University of Pittsburgh</affiliation></author>
-      <author><first>Michael</first><last>Yoder</last><affiliation>School of Computer Science, Carnegie Mellon University</affiliation></author>
+      <author><first>Michael Miller</first><last>Yoder</last><affiliation>School of Computer Science, Carnegie Mellon University</affiliation></author>
       <pages>60-77</pages>
       <abstract>This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.</abstract>
       <url hash="4da27e8b">2024.gebnlp-1.5</url>