Data4Good: Designing for Diversity and Development
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.
Año de publicación:
2020
Keywords:
- multilingual/multicultural contexts
- DIVERSITY
- AI for social good
- data literacies
- interdisciplinary computing
Fuente:
scopusTipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Estudios culturales
Áreas temáticas de Dewey:
- Procesos sociales
Objetivos de Desarrollo Sostenible:
- ODS 10: Reducción de las desigualdades
- ODS 16: Paz, justicia e instituciones sólidas
- ODS 4: Educación de calidad