Luigi De Russis
Neha Kumar
ABSTRACT
We are witnessing unprecedented datafication of the society we live in, alongside rapid advances in the fields of Artificial Intelligence and Machine Learning. However, emergent data-driven applications are systematically discriminating against many diverse populations. A major driver of the bias are the data, which typically align with predominantly Western definitions and lack representation from multilingually diverse and resource-constrained regions across the world. Therefore, data-driven approaches can benefit from integration of a more human-centred orientation before being used to inform the design, deployment, and evaluation of technologies in various contexts. This workshop seeks to advance these and similar conversations, by inviting researchers and practitioners in interdisciplinary domains to engage in conversation around how appropriate human-centred design can contribute to addressing data-related challenges among marginalised and under-represented/underserved groups.
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. https://doi.org/10.1109/cvpr.2009.5206848Google Scholar
Cross Ref
- James Zou and Londa Schiebinger. 2018. AI can be sexist and racist --- it's time to make it fair. Nature 559, 7714 (jul 2018), 324--326. https://doi.org/10.1038/d41586-018-05707-8Google ScholarCross Ref
Index Terms
- Data4Good: Designing for Diversity and Development
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