Beatriz Palacios Abad, E. Belding-Royer, Morgan Vigil-Hayes, E. Zegura
The Federal Communications Commission (FCC) has recently released official technical requirements for its Broadband Data Collection (BDC) processes, with the purpose of improving the accuracy of broadband coverage data in the United States. A key process in the BDC establishes the opportunity for communities to crowdsource Internet measurements that may dispute coverage data maintained by Internet service providers. This process outlines complex requirements that may provide a substantial barrier to community participation. In this poster we share the design of a network measurement tool suite and the requirements for a community coordination tool to support community-led efforts to challenge official reports. Our design is based on “counter-data action” principles, which call unethical and authoritative uses of data into question.
{"title":"Note: Towards Community-Empowered Network Data Action","authors":"Beatriz Palacios Abad, E. Belding-Royer, Morgan Vigil-Hayes, E. Zegura","doi":"10.1145/3530190.3534836","DOIUrl":"https://doi.org/10.1145/3530190.3534836","url":null,"abstract":"The Federal Communications Commission (FCC) has recently released official technical requirements for its Broadband Data Collection (BDC) processes, with the purpose of improving the accuracy of broadband coverage data in the United States. A key process in the BDC establishes the opportunity for communities to crowdsource Internet measurements that may dispute coverage data maintained by Internet service providers. This process outlines complex requirements that may provide a substantial barrier to community participation. In this poster we share the design of a network measurement tool suite and the requirements for a community coordination tool to support community-led efforts to challenge official reports. Our design is based on “counter-data action” principles, which call unethical and authoritative uses of data into question.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Mondal, M. Islam, Sarah Zabeen, A. Islam, Jannatun Noor
Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.
{"title":"Note: Plant Leaf Disease Network (PLeaD-Net): Identifying Plant Leaf Diseases through Leveraging Limited-Resource Deep Convolutional Neural Network","authors":"J. Mondal, M. Islam, Sarah Zabeen, A. Islam, Jannatun Noor","doi":"10.1145/3530190.3534844","DOIUrl":"https://doi.org/10.1145/3530190.3534844","url":null,"abstract":"Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130564043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data is an inseparable part of community management. Data openness and transparency has been a driver for change in government accountability and public engagement by providing unprecedented access to information. More prominently, there exists enthusiasm about the possibilities created by new and more extensive sources of data to improve our understanding and management of communities. This work examines a case study in collecting and operationalizing sustainable open data and specifically open government or civic data - information, public or otherwise, which anyone is free to access, analyze and re-use for any purpose - through a platform and community organizing effort in crowdsourcing open pedestrian network data. We outline a number of tensions or challenges in opening data, specifically in a number of realms where public interest stands to benefit from uses of the data, yet no single commercial or governmental entity is either liable or has a clear monetary interest associated with freely opening that data. In these specific cases, collection of these open data becomes a community-based challenge to undertake, which raises a number of additional socio-technical, political, and data provenance considerations. Beyond the technical contributions of our framework (in the open-source tools to support community activities, our case study contributes a number of insights and recommendations regarding community engagement, use of participatory co-design jointly with data collection tools, and planning for sustainable data stewardship in the involved communities.
{"title":"Towards operationalizing the communal production and management of public (open) data: a pedestrian network case study: A pedestrian network case study in operationalizing communal open data","authors":"N. Bolten, A. Caspi","doi":"10.1145/3530190.3534821","DOIUrl":"https://doi.org/10.1145/3530190.3534821","url":null,"abstract":"Data is an inseparable part of community management. Data openness and transparency has been a driver for change in government accountability and public engagement by providing unprecedented access to information. More prominently, there exists enthusiasm about the possibilities created by new and more extensive sources of data to improve our understanding and management of communities. This work examines a case study in collecting and operationalizing sustainable open data and specifically open government or civic data - information, public or otherwise, which anyone is free to access, analyze and re-use for any purpose - through a platform and community organizing effort in crowdsourcing open pedestrian network data. We outline a number of tensions or challenges in opening data, specifically in a number of realms where public interest stands to benefit from uses of the data, yet no single commercial or governmental entity is either liable or has a clear monetary interest associated with freely opening that data. In these specific cases, collection of these open data becomes a community-based challenge to undertake, which raises a number of additional socio-technical, political, and data provenance considerations. Beyond the technical contributions of our framework (in the open-source tools to support community activities, our case study contributes a number of insights and recommendations regarding community engagement, use of participatory co-design jointly with data collection tools, and planning for sustainable data stewardship in the involved communities.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117298843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anirban Sen, Debanjan Ghatak, Gurjeet Khanuja, K. Rekha, Mehak Gupta, Sanket Dhakate, Kartikeya Sharma, Aaditeshwar Seth
Many citizens consume information on government policies from the mass media. Consequently, biases existing in the policy discourse in media sources may influence citizens’ understanding of the policies, about how they may affect diverse communities. These biases may also get amplified further through social media if it simply echoes the biases of mass media content. We build methods to quantify media bias in terms of preferred treatment given to certain issues corresponding to four economic policies, and alignment observed with the ideological stance of different political parties. We also examine how the social media community of followers of these media houses contribute to the policy discourse. Other than being one of the first large scale studies in the Indian context, our work contributes towards creating a standardized methodology to assess the ideological stance of a news-source, and its alignment with the social media discourse of its follower community. We find that the Indian mass media exhibits bias towards certain aspects or topics related to policy events. It also provides a significantly high coverage to aspects concerning the middle class and to political statements, neglecting the aspects directly relevant to the poor. Additionally, we find evidence of bias also in the representation provided to different political parties in the media. Social media seems to echo these biases rather than mitigate them. The tools and methods developed in this work can be useful for media watchdog institutions to call out biases in the media, and advocate for more complete coverage of issues across different news sources.
{"title":"Analysis of Media Bias in Policy Discourse in India","authors":"Anirban Sen, Debanjan Ghatak, Gurjeet Khanuja, K. Rekha, Mehak Gupta, Sanket Dhakate, Kartikeya Sharma, Aaditeshwar Seth","doi":"10.1145/3530190.3534798","DOIUrl":"https://doi.org/10.1145/3530190.3534798","url":null,"abstract":"Many citizens consume information on government policies from the mass media. Consequently, biases existing in the policy discourse in media sources may influence citizens’ understanding of the policies, about how they may affect diverse communities. These biases may also get amplified further through social media if it simply echoes the biases of mass media content. We build methods to quantify media bias in terms of preferred treatment given to certain issues corresponding to four economic policies, and alignment observed with the ideological stance of different political parties. We also examine how the social media community of followers of these media houses contribute to the policy discourse. Other than being one of the first large scale studies in the Indian context, our work contributes towards creating a standardized methodology to assess the ideological stance of a news-source, and its alignment with the social media discourse of its follower community. We find that the Indian mass media exhibits bias towards certain aspects or topics related to policy events. It also provides a significantly high coverage to aspects concerning the middle class and to political statements, neglecting the aspects directly relevant to the poor. Additionally, we find evidence of bias also in the representation provided to different political parties in the media. Social media seems to echo these biases rather than mitigate them. The tools and methods developed in this work can be useful for media watchdog institutions to call out biases in the media, and advocate for more complete coverage of issues across different news sources.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123444753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anurag Aribandi, Divyanshu Agrawal, D. Chakraborty
Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users’ spoken query and respond accurately.
{"title":"Note: Evaluating Trust in the Context of Conversational Information Systems for new users of the Internet","authors":"Anurag Aribandi, Divyanshu Agrawal, D. Chakraborty","doi":"10.1145/3530190.3534852","DOIUrl":"https://doi.org/10.1145/3530190.3534852","url":null,"abstract":"Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users’ spoken query and respond accurately.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132822459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Udit Paul, Jiamo Liu, David FARIAS-LLERENAS, V. Adarsh, Arpit Gupta, E. Belding-Royer
It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test.
{"title":"Characterizing Internet Access and Quality Inequities in California M-Lab Measurements","authors":"Udit Paul, Jiamo Liu, David FARIAS-LLERENAS, V. Adarsh, Arpit Gupta, E. Belding-Royer","doi":"10.1145/3530190.3534813","DOIUrl":"https://doi.org/10.1145/3530190.3534813","url":null,"abstract":"It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116671950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isolation contributes to deteriorating health outcomes during the first 1000 days of a child's life (the period from conception to two years). Mothers and their growing babies are at risk of pregnancy-related complications and malnutrition during this sensitive period due to inadequate information. This study describes how a faith-based organization (FBO) in Cape Town leverages available resources in both physical and virtual spaces to support mothers through antenatal classes. We observed seven small groups in their physical spaces, interviewed seven mothers and analyzed fifteen WhatsApp chat groups to understand the group structure, dynamics, and interactions. When the model was introduced to the mothers in the physical and virtual spaces simultaneously, cohesion was achieved and sustained. However, during the COVID-19 pandemic, where strong indications of stress and isolation were evident, a strange paradox was noted: all groups showed weak ties (with minimal communication among members). It was hard to explain the non-commitment despite efforts from the moderators to encourage sharing among mothers. We identified two underlying causes: a minimal sense of belonging to the group and bandwidth constraints. Further analysis showed that bandwidth constraints digitally excluded some mothers from active participation. These findings indicated the need for HCI and technology designers to design less bandwidth-intensive interactive platforms for inclusivity.
{"title":"A Descriptive Analysis of Cohesion within Virtual and Physical Small Groups of Mothers in Bandwidth-Constrained Communities in Cape Town.","authors":"Sarah Dsane, Melissa Densmore, Y. Joolay","doi":"10.1145/3530190.3534793","DOIUrl":"https://doi.org/10.1145/3530190.3534793","url":null,"abstract":"Isolation contributes to deteriorating health outcomes during the first 1000 days of a child's life (the period from conception to two years). Mothers and their growing babies are at risk of pregnancy-related complications and malnutrition during this sensitive period due to inadequate information. This study describes how a faith-based organization (FBO) in Cape Town leverages available resources in both physical and virtual spaces to support mothers through antenatal classes. We observed seven small groups in their physical spaces, interviewed seven mothers and analyzed fifteen WhatsApp chat groups to understand the group structure, dynamics, and interactions. When the model was introduced to the mothers in the physical and virtual spaces simultaneously, cohesion was achieved and sustained. However, during the COVID-19 pandemic, where strong indications of stress and isolation were evident, a strange paradox was noted: all groups showed weak ties (with minimal communication among members). It was hard to explain the non-commitment despite efforts from the moderators to encourage sharing among mothers. We identified two underlying causes: a minimal sense of belonging to the group and bandwidth constraints. Further analysis showed that bandwidth constraints digitally excluded some mothers from active participation. These findings indicated the need for HCI and technology designers to design less bandwidth-intensive interactive platforms for inclusivity.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126158297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karm Patel, Rishiraj Adhikary, Zeel B Patel, Nipun Batra, S. Guttikunda
Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.
{"title":"Samachar: Print News Media on Air Pollution in India","authors":"Karm Patel, Rishiraj Adhikary, Zeel B Patel, Nipun Batra, S. Guttikunda","doi":"10.1145/3530190.3534812","DOIUrl":"https://doi.org/10.1145/3530190.3534812","url":null,"abstract":"Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joy Ming, Srujana Kamath, Elizabeth Kuo, M. Sterling, Nicola Dell, Aditya Vashistha
Frontline health workers provide essential services for their communities, but much of their work remains invisible—undervalued and underappreciated. Examining this invisible work ensures new technologies do not amplify or reinforce inequitable power structures, especially as governments and organizations push to digitize health work processes. We build on a burgeoning conversation by studying how invisible work manifests and how this invisibility can be challenged in two contexts of frontline health: home health aides in New York City, USA and Accredited Social Health Activists (ASHAs) in Uttar Pradesh, India. We highlight three shared manifestations of invisible work: (1) work done outside of the workers’ boundaries (2) work done to gain and share knowledge and (3) work done to manage relationships. These common categories are experienced differently in the two contexts, raising nuances to consider when designing technology for frontline health workers. We discuss these nuances and other tensions through concrete examples of how workers can escalate feedback and conflicts, quantify implicit expertise about patients, or build more awareness of their situation. Our paper guides the creation of technologies that take into account a more comprehensive understanding of the frontline health workers’ processes and highlight more of their contributions.
{"title":"Invisible Work in Two Frontline Health Contexts","authors":"Joy Ming, Srujana Kamath, Elizabeth Kuo, M. Sterling, Nicola Dell, Aditya Vashistha","doi":"10.1145/3530190.3534814","DOIUrl":"https://doi.org/10.1145/3530190.3534814","url":null,"abstract":"Frontline health workers provide essential services for their communities, but much of their work remains invisible—undervalued and underappreciated. Examining this invisible work ensures new technologies do not amplify or reinforce inequitable power structures, especially as governments and organizations push to digitize health work processes. We build on a burgeoning conversation by studying how invisible work manifests and how this invisibility can be challenged in two contexts of frontline health: home health aides in New York City, USA and Accredited Social Health Activists (ASHAs) in Uttar Pradesh, India. We highlight three shared manifestations of invisible work: (1) work done outside of the workers’ boundaries (2) work done to gain and share knowledge and (3) work done to manage relationships. These common categories are experienced differently in the two contexts, raising nuances to consider when designing technology for frontline health workers. We discuss these nuances and other tensions through concrete examples of how workers can escalate feedback and conflicts, quantify implicit expertise about patients, or build more awareness of their situation. Our paper guides the creation of technologies that take into account a more comprehensive understanding of the frontline health workers’ processes and highlight more of their contributions.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128212988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motivated by a desire for waste reduction through surplus redistribution, we explore the paradox of overproduction of resources that are wasted at several levels of the supply chain and the concurrent lack of access to, in most cases, overproduced basic resources by low income socioeconomic classes to whom resource access is normally only available through donation centers. To that end, we contrast two surplus redistribution solutions to this paradox. (1) Local independent donations between producers and donation centers. (2) Redistribution by way of a global redistributor (what we will call a core redistributor) who collects donations from all available producers and redistributes the surplus to all donation centers respective of their demanded quantities. We mathematically show that an optimal allocation of the surplus that minimizes waste and maximizes social welfare is only possible with a core redistributor. As this is a deeply social and economic problem rather than mathematical, we also qualitatively study two cases; (1) food waste and food insecurity in the UK, and (2) Los Angeles County’s project RoomKey: a pandemic effort to house covid-vulnerable unhoused persons in vacant hotels and motels. Both case studies give more support for a core redistribution as a solution to waste from overproduction and lack of access to essential resources.
{"title":"Algorithmic Waste Reduction","authors":"J. C. Aguma, Michael Demirev","doi":"10.1145/3530190.3534815","DOIUrl":"https://doi.org/10.1145/3530190.3534815","url":null,"abstract":"Motivated by a desire for waste reduction through surplus redistribution, we explore the paradox of overproduction of resources that are wasted at several levels of the supply chain and the concurrent lack of access to, in most cases, overproduced basic resources by low income socioeconomic classes to whom resource access is normally only available through donation centers. To that end, we contrast two surplus redistribution solutions to this paradox. (1) Local independent donations between producers and donation centers. (2) Redistribution by way of a global redistributor (what we will call a core redistributor) who collects donations from all available producers and redistributes the surplus to all donation centers respective of their demanded quantities. We mathematically show that an optimal allocation of the surplus that minimizes waste and maximizes social welfare is only possible with a core redistributor. As this is a deeply social and economic problem rather than mathematical, we also qualitatively study two cases; (1) food waste and food insecurity in the UK, and (2) Los Angeles County’s project RoomKey: a pandemic effort to house covid-vulnerable unhoused persons in vacant hotels and motels. Both case studies give more support for a core redistribution as a solution to waste from overproduction and lack of access to essential resources.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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