AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

Understand

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training.

• However, concerns persist regarding the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms.
• Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups.
• To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints.