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

TL;DR

This work proposes a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion to ensure diverse viewpoints, and applies it to develop a dataset and AI model for evaluating public space quality using street view images.

Abstract

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. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

Figures (6)

• Figure 1: Methodology used to create the dataset and the AI model to evaluate the quality of public spaces.
• Figure 2: Voting Patterns Observed in the Streetview Dataset. It is a histogram of the absolute scores of 3 out of the 22 participants.
• Figure 3: Experiment results
• Figure 4: The identity markers of the participants.
• Figure 5: Criteria used for evaluating the streetview images of public spaces.