Exclusion in Tech Design & Decision-Making
Beyond work-related and educational use, digital technologies are quietly reshaping social
practices. The digital divide and simultaneous surge of Artificial Intelligence and Large language models in everyday life have kept exclusion deeper, which should not be a binary of inclusion versus exclusion but larger economic structures.
Factors Keeping Marginalised Outside the Tech Design & Decision-Making
- Lack of diverse voices at the design table— Communities absent; systems don’t serve unique needs
- Insufficient data from marginalised populations — creates blind spots in model performance
- Historical exclusion from tech development— corporations/governments excluded groups from deployment/oversight
- Concentration of AI infrastructure— infrastructure concentrated in Global North; South excluded from rule-setting
- Data extracted from Global South without consent; economic value accrues to Global North companies
- Unclear transparency— ‘black box’ models limit accountability for bias correction
Cases and regional examples
- Asia: Researchers and civil-society groups in South Asia and Southeast Asia have documented LLM outputs that reinforce occupational and cultural stereotypes about women and certain ethnic groups.
- Africa: Studies from African researchers and NGOs show that many LLMs trained primarily on Western text produce inaccurate or biased characterisations of African women and underrepresent local languages and gender identities, leading to misgendering and exclusion in local languages.
Latin America: Researchers in Latin America have reported that AI systems sometimes replicate patriarchal language patterns and stereotypes, and regional advocacy has pushed for gender-inclusive corpora and participatory evaluation with local feminist organisations.
