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

However, Moreover, the failure to acknowledge the socio-cultural concerns persist regarding the transparency and context context within which data is produced can introduce biases of data collection methodologies, especially when sourced into datasets. For example, algorithms trained on datasets through crowdsourcing platforms. Crowdsourcing often devoid of the historical context of segregation may inadvertently employs low-wage workers with poor working conditions perpetuate biases against certain minority groups and lacks consideration for the representativeness of annotators, [12]. Furthermore, the identities of workers involved in annotations leading to algorithms that fail to represent diverse are frequently overlooked, leading to a lack of diversity views and perpetuate biases against certain groups. To address in viewpoints captured within datasets. This bias is these limitations, we propose a methodology involving compounded by the common practice of aggregating annotations a co-design model that actively engages stakeholders at key through majority voting [5].