Summary of Ai-edi-space: a Co-designed Dataset For Evaluating the Quality Of Public Spaces, by Shreeyash Gowaikar et al.
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
by Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani, Emmanuel Beaudry Marchand, Toumadher Ammar, Shin Koseki
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of large-scale datasets used in AI training by proposing a co-design model that ensures diversity, equity, and inclusion. The authors highlight concerns about crowdsourced annotation methods, which often rely on low-wage workers with poor working conditions, leading to biased algorithms. To mitigate these issues, they develop a methodology that engages stakeholders at key stages, integrating principles of EDI. The paper demonstrates the effectiveness of this approach by creating a dataset and AI model for evaluating public space quality using street view images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure the data used to train artificial intelligence (AI) systems is fair and represents all people. Right now, a lot of data is collected through crowdsourcing, which can be problematic because it relies on low-wage workers who may not have good working conditions or represent diverse viewpoints. This can lead to AI algorithms that are biased against certain groups. The authors propose a new way of collecting data that involves working with stakeholders and making sure the data represents different perspectives. They test this approach by creating a dataset and AI model for evaluating public space quality using street view images. |
