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

Rana Tallal Javed; Osama Nasir; Melania Borit; Loïs Vanhée; Elias Zea; Shivam Gupta; Ricardo Vinuesa; Junaid Qadir

IJCAI 2023poster34 citations

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

Rana Tallal Javed, Osama Nasir, Melania Borit, Loïs Vanhée, Elias Zea, Shivam Gupta, Ricardo Vinuesa, Junaid Qadir

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.

AI Ethics, Trust, Fairness: GeneralData Mining: GeneralData Mining: DM: Exploratory data mining

BibTeX

@inproceedings{ijcai2023p780,
  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 = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {6905--6909},
  year      = {2023},
  month     = {8},
  note      = {Journal Track},
  doi       = {10.24963/ijcai.2023/780},
  url       = {https://doi.org/10.24963/ijcai.2023/780},
}