Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)

Abstract

This study explores the topics and trends of teaching AI ethics in higher education, using Latent Dirichlet Allocation as the analysis tool. The analyses included 166 courses from 105 universities around the world. Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. The study critically assesses the implications of this teaching paradigm and challenges practitioners to reflect on their practices and move beyond stereotypes and biases.

Cite

Text

Javed et al. "Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/780

Markdown

[Javed et al. "Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/javed2023ijcai-get/) doi:10.24963/IJCAI.2023/780

BibTeX

@inproceedings{javed2023ijcai-get,
  title     = {{Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)}},
  author    = {Javed, Rana Tallal and Nasir, Osama and Borit, Melania and Vanhée, Loïs and Zea, Elias and Gupta, Shivam and Vinuesa, Ricardo and Qadir, Junaid},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {6905-6909},
  doi       = {10.24963/IJCAI.2023/780},
  url       = {https://mlanthology.org/ijcai/2023/javed2023ijcai-get/}
}