AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916466825101312 |
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| author | Gowaikar, Shreeyash Berard, Hugo Mushkani, Rashid Marchand, Emmanuel Beaudry Ammar, Toumadher Koseki, Shin |
| author_facet | Gowaikar, Shreeyash Berard, Hugo Mushkani, Rashid Marchand, Emmanuel Beaudry Ammar, Toumadher Koseki, Shin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces Gowaikar, Shreeyash Berard, Hugo Mushkani, Rashid Marchand, Emmanuel Beaudry Ammar, Toumadher Koseki, Shin Machine Learning Computer Vision and Pattern Recognition Computers and Society 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. |
| title | AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces |
| topic | Machine Learning Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2411.00956 |