Rana Tallal Javed

• Nasir, Osama; Javed, Rana Tallal; Gupta, Shivam; Vinuesa, Ricardo & Qadir, Junaid (2023). Artificial Intelligence and Sustainable Development Goals Nexus Via Four Vantage Points. Technology in society. ISSN 0160-791X. 72. doi: 10.1016/j.techsoc.2022.102171. Full text in Research Archive Show summary

  Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets to extract meaningful information for policymakers and researchers alike - shedding light on how AI is being used and can potentially be employed in the future to achieve the SDGs. To this end, we devised a methodology using keyword-matching and keyword-similarity to compute the relevance of the SDGs for a given document. SDG metadata and AI4SDG Projects (Oxford initiative on AI4SDGs) were used to validate our methodology. We find an imbalance of coverage with SDG 9 (Industry Innovation and Infrastructure) having the highest representation (with 50.3% of our data containing references to it) compared to SDGs 5, 6, 14, and 15, which have the lowest representation (5% of observed data). Findings from this study suggest that the development of AI technology is focused on improving the current economic growth, but it might neglect important societal and environmental issues.

• Javed, Rana Tallal; Nasir, Osama; Borit, Melania; Vanhée, Loïs; Zea, Elias & Gupta, Shivam [Show all 8 contributors for this article] (2023). Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract). In Elkind, Edith (Eds.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Macao, SAR, 19-25 August 2023. IJCAI International Joint Conferences on Artificial Intelligence . ISSN 9781956792034. p. 6905–6909. doi: 10.24963/ijcai.2023/780. Full text in Research Archive Show summary

  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.

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