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Abstract This study explores the outcomes and impacts of sanitary sewer overflows (SSOs) and basement backups in underserved communities in Baltimore, Maryland. The larger effort is an environmental and community-driven mixed-methods project, however, the research in this manuscript focuses on the household survey portion with residents who have experienced SSOs or sewage backups. Based on the snowball sampling method applied, the resulting residents engaged are predominantly African-American individuals, females, homeowners, and residents between the ages of 50 and 69. Strikingly, 70% of respondents reported that their frequency of SSOs is between moderate to frequent. The findings reveal that SSOs are a pervasive issue affecting residents’ physical and mental health and overall quality of life. Despite residents’ perceptions that their household infrastructure is in good condition, the recurring nature of SSOs highlights systemic problems within the city’s aging sewer systems, urging a deeper understanding of the social and structural vulnerabilities involved. This research calls attention to the importance of comprehensive interventions, including effective risk communication strategies and substantial investment in infrastructure rehabilitation, to mitigate the risks posed by SSOs and promote long-term resilience in urban environments. Additionally, it emphasizes the importance of community-driven research in addressing engineering, urban planning, and public health challenges with particular support for the most affected populations.
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Attention Please: Your Attention Check Questions in Survey Studies Can Be Automatically Answered
Attention check questions have become commonly used in online surveys published on popular crowdsourcing platforms as a key mechanism to filter out inattentive respondents and improve data quality. However, little research considers the vulnerabilities of this important quality control mechanism that can allow attackers including irresponsible and malicious respondents to automatically answer attention check questions for efficiently achieving their goals. In this paper, we perform the first study to investigate such vulnerabilities, and demonstrate that attackers can leverage deep learning techniques to pass attention check questions automatically. We propose AC-EasyPass, an attack framework with a concrete model, that combines convolutional neural network and weighted feature reconstruction to easily pass attention check questions. We construct the first attention check question dataset that consists of both original and augmented questions, and demonstrate the effectiveness of AC-EasyPass. We explore two simple defense methods, adding adversarial sentences and adding typos, for survey designers to mitigate the risks posed by AC-EasyPass; however, these methods are fragile due to their limitations from both technical and usability perspectives, underlining the challenging nature of defense. We hope our work will raise sufficient attention of the research community towards developing more robust attention check mechanisms. More broadly, our work intends to prompt the research community to seriously consider the emerging risks posed by the malicious use of machine learning techniques to the quality, validity, and trustworthiness of crowdsourcing and social computing.
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- Award ID(s):
- 1936968
- PAR ID:
- 10175639
- Date Published:
- Journal Name:
- The Web Conference
- Page Range / eLocation ID:
- 1182 to 1193
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3366423.3380195
