The ubiquity of algorithmic tools and services (ATS) in spatial data science has led to increased concerns about the biases they carry. This vision paper explores the biases inherent in ATS, encompassing computational, statistical, human, and systemic biases, and those compounded by multinational corporations. It underscores the imperative to address these biases, advocating a narrative-based approach to counteract them and promote equitable outcomes. This approach not only heightens awareness of embedded biases but also charts a course toward their mitigation.

This paper envisions creating more inclusive communities through accessible urban places for not only those who identify as disabled but all equity-deserving groups. Concentrating on the street scale of the urban places, we propose identifying street scale accessibility features, and then, with the help of spatial data science and geospatial artificial intelligence, collecting and analyzing reliable data on these features to assess the accessibility of the urban places for movement diversity.