Major humanitarian organizations face the crucial challenge of estimating land damage from conflict in developing countries. A lack of on the ground data collection motivates the use of satellite imagery to meet this challenge. However, existing analysis methods involving satellite imagery are time-consuming, require special expertise, or lack automation. To mitigate these obstacles, SatDash was designed using Sentinel-2 images and ACLED data to provide a classification of areas that have undergone land damage due to conflict in northwestern Nigeria and Mali. SatDash was constructed using free and publicly available images and is accompanied by a user-friendly dashboard that allows domain experts to train their own data and export it for future use. The dashboard was created for a humanitarian organization, referred to as the DAAO, Damage Assessment and Aid Organization, and the design process adhered to four primary recommendations for a successful AI for Social Good (AI4SG) partnership that are further detailed in this paper. Within this paper, I draw attention to the context of CHI4Good research, detailing how the deployment phases of such systems often have their own set of potential barriers, along with describing ethical challenges that arise with this type of research. This paper focuses primarily on the design process and responses to both the constraints mentioned in literature and those presented by the DAAO. I acknowledge that AI applications, especially in development contexts, require close attention and context-specific awareness, and this is reflected through the conscious decision to include domain experts and ensure that the tool is only used for its intended purpose. When designing SatDash, the primary aim was to think critically about the involvement of local context and spur the conversation about inclusive design of similar systems in a large organization such as the DAAO. This research affirms that satellite imagery data can be used to assist humanitarian aid organizations with land change detection and demonstrates how human-in-the-loop systems can aid these organizations with identification of communities negatively impacted by hunger and recurring conflict.
主要人道主义组织面临着估算发展中国家冲突造成的土地破坏的关键挑战。缺乏地面数据收集促使使用卫星图像来应对这一挑战。然而，现有的卫星图像分析方法耗时，需要特殊的专业知识，或者缺乏自动化。为了消除这些障碍，SatDash的设计使用了Sentinel-2图像和ACLED数据，为尼日利亚西北部和马里因冲突而遭受土地破坏的地区提供分类。SatDash是使用免费和公开可用的图像构建的，并配有一个用户友好的仪表板，允许领域专家训练自己的数据并将其导出以供将来使用。该仪表板是为人道主义组织创建的，称为DAAO，即损害评估和援助组织，其设计过程遵循了成功的AI for Social Good (AI4SG)伙伴关系的四项主要建议，本文对此进行了进一步详细说明。在本文中，我提请注意CHI4Good研究的背景，详细说明了此类系统的部署阶段通常有其自己的一套潜在障碍，以及描述了这类研究中出现的伦理挑战。本文主要关注设计过程以及对文献中提到的约束和DAAO提出的约束的响应。我承认人工智能应用，特别是在开发环境中，需要密切关注和特定于环境的意识，这反映在有意识地决定包括领域专家，并确保该工具仅用于其预期目的。在设计SatDash时，主要目的是批判性地思考当地环境的参与，并激发关于DAAO等大型组织中类似系统的包容性设计的讨论。本研究证实，卫星图像数据可用于协助人道主义援助组织进行土地变化检测，并展示了人在循环系统如何帮助这些组织识别受饥饿和反复冲突负面影响的社区。
{"title":"SatDash: An Interactive Dashboard for Assessing Land Damage in Nigeria and Mali","authors":"Morgan Briggs","doi":"10.1145/3460112.3471949","DOIUrl":"https://doi.org/10.1145/3460112.3471949","url":null,"abstract":"Major humanitarian organizations face the crucial challenge of estimating land damage from conflict in developing countries. A lack of on the ground data collection motivates the use of satellite imagery to meet this challenge. However, existing analysis methods involving satellite imagery are time-consuming, require special expertise, or lack automation. To mitigate these obstacles, SatDash was designed using Sentinel-2 images and ACLED data to provide a classification of areas that have undergone land damage due to conflict in northwestern Nigeria and Mali. SatDash was constructed using free and publicly available images and is accompanied by a user-friendly dashboard that allows domain experts to train their own data and export it for future use. The dashboard was created for a humanitarian organization, referred to as the DAAO, Damage Assessment and Aid Organization, and the design process adhered to four primary recommendations for a successful AI for Social Good (AI4SG) partnership that are further detailed in this paper. Within this paper, I draw attention to the context of CHI4Good research, detailing how the deployment phases of such systems often have their own set of potential barriers, along with describing ethical challenges that arise with this type of research. This paper focuses primarily on the design process and responses to both the constraints mentioned in literature and those presented by the DAAO. I acknowledge that AI applications, especially in development contexts, require close attention and context-specific awareness, and this is reflected through the conscious decision to include domain experts and ensure that the tool is only used for its intended purpose. When designing SatDash, the primary aim was to think critically about the involvement of local context and spur the conversation about inclusive design of similar systems in a large organization such as the DAAO. This research affirms that satellite imagery data can be used to assist humanitarian aid organizations with land change detection and demonstrates how human-in-the-loop systems can aid these organizations with identification of communities negatively impacted by hunger and recurring conflict.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411804","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}
The paper develops an artificial neural network that predicts the presence of petroleum fields within ethnic country regions across sub-Saharan Africa using rich socioeconomic microdata. Using data from around 300,000 households from 1997 to 2014, the model accurately predicts the presence of petroleum fields in ethnic regions with an overall accuracy of 89.7%. Furthermore, the accuracy of the test and validation were found to be 89.9%. The slightly-increased accuracy in predicting petroleum fields suggests that socioeconomic data may be complementary to standard petroleum studies approaches in unpacking the social context of oil. The paper also explores dimensionality reductions to optimally characterize, organize, and visualize the data. Social science data may have a helpful role to play for oil resources and sustainable development
{"title":"Predicting Petroleum Fields in Ethnic Regions with Social and Economic Data: Evidence from Africa (Poster)","authors":"K. Opoku-Agyemang","doi":"10.1145/3460112.3471971","DOIUrl":"https://doi.org/10.1145/3460112.3471971","url":null,"abstract":"The paper develops an artificial neural network that predicts the presence of petroleum fields within ethnic country regions across sub-Saharan Africa using rich socioeconomic microdata. Using data from around 300,000 households from 1997 to 2014, the model accurately predicts the presence of petroleum fields in ethnic regions with an overall accuracy of 89.7%. Furthermore, the accuracy of the test and validation were found to be 89.9%. The slightly-increased accuracy in predicting petroleum fields suggests that socioeconomic data may be complementary to standard petroleum studies approaches in unpacking the social context of oil. The paper also explores dimensionality reductions to optimally characterize, organize, and visualize the data. Social science data may have a helpful role to play for oil resources and sustainable development","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682841","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}
INTRODUCTION Video cameras are poised to play a pivotal role in providing advanced analytics in smart cities. Although cameras today are used for surveillance purposes and are required to be in “always-on" mode, video-based analytics go beyond surveillance and offer rich analytics such as business intelligence, environment conservation, and infrastructure management. Recent estimates indicate that millions of cameras are deployed in very diverse environments, and operating cameras in always-on mode unnecessarily increase the energy footprint and cost. An effective way to reduce energy consumption is to operate in standby mode in conjunction with using energy-efficient devices. This allows the device to consume minimal energy, enough to respond to any wakeup event. At the same time, this also reduces the amount of generated data, reducing the overall computational burden. Studies show that these can lead to significant energy savings over time [2]. As such, recent efforts have investigated techniques to reduce energy by switching to standby mode based on device usage prediction [3]. We note that most cameras for video analytics need not necessarily operate in a continuous-on mode, and thus, there is significant potential in reducing energy use [2]. For example, a parking bay’s video analysis can determine vacant spots, but such systems need not always be on as long as it provides parking information in a timely manner. If the parking lot is near full, a driver may need assistance in locating a spot, as the average driver spends 17 hours per year searching for vacant parking bays [4]. On the other hand, if the parking lot is near empty, a vacant parking bay’s exact location may be irrelevant since it should be easy to find a spot to park. As such, if we turn off parking video-analytics when parking space is ample, we can tradeoff utility for energy. Our focus is to develop a reinforcement learning (RL) technique to learn a standby management policy that increases the overall
{"title":"Deep Reinforcement Learning for Energy-efficient Parking Video Analytics Platform (Poster Version)","authors":"Yoones Rezaei, Stephen Lee, D. Mossé","doi":"10.1145/3460112.3471980","DOIUrl":"https://doi.org/10.1145/3460112.3471980","url":null,"abstract":"INTRODUCTION Video cameras are poised to play a pivotal role in providing advanced analytics in smart cities. Although cameras today are used for surveillance purposes and are required to be in “always-on\" mode, video-based analytics go beyond surveillance and offer rich analytics such as business intelligence, environment conservation, and infrastructure management. Recent estimates indicate that millions of cameras are deployed in very diverse environments, and operating cameras in always-on mode unnecessarily increase the energy footprint and cost. An effective way to reduce energy consumption is to operate in standby mode in conjunction with using energy-efficient devices. This allows the device to consume minimal energy, enough to respond to any wakeup event. At the same time, this also reduces the amount of generated data, reducing the overall computational burden. Studies show that these can lead to significant energy savings over time [2]. As such, recent efforts have investigated techniques to reduce energy by switching to standby mode based on device usage prediction [3]. We note that most cameras for video analytics need not necessarily operate in a continuous-on mode, and thus, there is significant potential in reducing energy use [2]. For example, a parking bay’s video analysis can determine vacant spots, but such systems need not always be on as long as it provides parking information in a timely manner. If the parking lot is near full, a driver may need assistance in locating a spot, as the average driver spends 17 hours per year searching for vacant parking bays [4]. On the other hand, if the parking lot is near empty, a vacant parking bay’s exact location may be irrelevant since it should be easy to find a spot to park. As such, if we turn off parking video-analytics when parking space is ample, we can tradeoff utility for energy. Our focus is to develop a reinforcement learning (RL) technique to learn a standby management policy that increases the overall","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599968","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}
Fears of climate change and the escalating impacts of environmental damage are growing, and recent papers in the area of Sustainable HCI have called for urgent, non-linear solutions to these problems. Speculative design, along with related approaches including design fiction, have been taken up as means of navigating the "wicked problems" that structure contemporary nature/society relations. We conduct a survey of speculative design papers published in ACM venues between 2008 and 2021, assessing fundamental questions such as who is involved in the process, how is sustainability framed, and how is speculation used. Our evaluation of this body of work yielded mixed results; we find both promising trends as well as notable and problematic limitations in how the HCI community is taking up speculative practice in this domain. We build upon this evaluation to offer four provocations to designers seeking to use speculative practice in support of sustainability goals.
{"title":"What We Speculate About When We Speculate About Sustainable HCI","authors":"R. Soden, Pradnaya S Pathak, Olivia Doggett","doi":"10.1145/3460112.3471956","DOIUrl":"https://doi.org/10.1145/3460112.3471956","url":null,"abstract":"Fears of climate change and the escalating impacts of environmental damage are growing, and recent papers in the area of Sustainable HCI have called for urgent, non-linear solutions to these problems. Speculative design, along with related approaches including design fiction, have been taken up as means of navigating the \"wicked problems\" that structure contemporary nature/society relations. We conduct a survey of speculative design papers published in ACM venues between 2008 and 2021, assessing fundamental questions such as who is involved in the process, how is sustainability framed, and how is speculation used. Our evaluation of this body of work yielded mixed results; we find both promising trends as well as notable and problematic limitations in how the HCI community is taking up speculative practice in this domain. We build upon this evaluation to offer four provocations to designers seeking to use speculative practice in support of sustainability goals.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115366654","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}
It has become common for governments and practitioners to measure mobility using data from smartphones, especially during the COVID-19 pandemic. Yet in countries where few people have smartphones, or use mobile internet, the movement of smartphones may not be a good indicator of the movement of the population. This paper develops a framework for approaching potential bias that can arise when measuring mobility with smartphones. Using mobile phone operator records in Uganda, we compare the mobility of smartphones and the basic and feature phones that are more common. Smartphones have different travel patterns, and decrease mobility substantially more in response to a COVID-19 lockdown. This suggests caution when interpreting smartphone mobility estimates in contexts with low adoption.
{"title":"Assessing Bias in Smartphone Mobility Estimates in Low Income Countries","authors":"S. Milusheva, Daniel Björkegren, Leonardo Viotti","doi":"10.1145/3460112.3471968","DOIUrl":"https://doi.org/10.1145/3460112.3471968","url":null,"abstract":"It has become common for governments and practitioners to measure mobility using data from smartphones, especially during the COVID-19 pandemic. Yet in countries where few people have smartphones, or use mobile internet, the movement of smartphones may not be a good indicator of the movement of the population. This paper develops a framework for approaching potential bias that can arise when measuring mobility with smartphones. Using mobile phone operator records in Uganda, we compare the mobility of smartphones and the basic and feature phones that are more common. Smartphones have different travel patterns, and decrease mobility substantially more in response to a COVID-19 lockdown. This suggests caution when interpreting smartphone mobility estimates in contexts with low adoption.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"20 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992378","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}
Task oriented virtual assistants or dialogue systems are being popular for different domains such as restaurant booking, weather update, flight booking etc. The efforts are supported by availability of large scale annotated conversational datasets for such domains. However, the same is not true for transport domain dialogue systems. Moreover, for such systems to be useful, they should be able to handle natural queries submitted by users. For countries like India where most of the people communicate in regional languages, it is important to have such systems support the regional languages. The existing datasets for transport domain are mostly monolingual in nature and support only English language. For countries like India, where people tend to speak multiple languages and have code-mixed conversations the existing systems and the datasets won’t be of much use. To the best of our knowledge, there is no multilingual code-mixed dataset available for designing public transport related conversation systems. In this paper, we propose a code-mixed English-Hindi dataset to accelerate the development of transport domain conversational systems suitable for countries like India. Our dataset has multiple intents like: route finding, bus/train/cab finding, nearby place search, traffic alert queries, out of domain queries. We also provide initial baseline results for user intent identification using existing state of the art models on our dataset and a prototype to show the usability of the work. Extended version for this paper can be found at https://iith.ac.in/~maunendra/papers/COMPASS21-mTransDial.pdf
{"title":"mTransDial: Multilingual Dataset for Transport Domain Dialog Systems (Poster)","authors":"Priyambada Ambastha, M. Desarkar","doi":"10.1145/3460112.3471977","DOIUrl":"https://doi.org/10.1145/3460112.3471977","url":null,"abstract":"Task oriented virtual assistants or dialogue systems are being popular for different domains such as restaurant booking, weather update, flight booking etc. The efforts are supported by availability of large scale annotated conversational datasets for such domains. However, the same is not true for transport domain dialogue systems. Moreover, for such systems to be useful, they should be able to handle natural queries submitted by users. For countries like India where most of the people communicate in regional languages, it is important to have such systems support the regional languages. The existing datasets for transport domain are mostly monolingual in nature and support only English language. For countries like India, where people tend to speak multiple languages and have code-mixed conversations the existing systems and the datasets won’t be of much use. To the best of our knowledge, there is no multilingual code-mixed dataset available for designing public transport related conversation systems. In this paper, we propose a code-mixed English-Hindi dataset to accelerate the development of transport domain conversational systems suitable for countries like India. Our dataset has multiple intents like: route finding, bus/train/cab finding, nearby place search, traffic alert queries, out of domain queries. We also provide initial baseline results for user intent identification using existing state of the art models on our dataset and a prototype to show the usability of the work. Extended version for this paper can be found at https://iith.ac.in/~maunendra/papers/COMPASS21-mTransDial.pdf","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124951935","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}
Aman Khullar, M. Santosh, Praveen Kumar, Shoaib Rahman, Rajeshwari Tripathi, Deepak Kumar, Sangeeta Saini, Rachit Pandey, Aaditeshwar Seth
Question-answering systems where users can ask questions based on emergent needs which are then answered by experts or peers, have emerged as an important information seeking modality on digital platforms. Automating this process has been an active area of research since many years, to identify relevant answers from pre-existing question-answer databases. We report on the feasibility of running automated question-answering systems in the context of rural and less-literate users in India, accessed through IVR (Interactive Voice Response) systems. We use commercial speech recognition APIs to convert audio questions asked by users into their equivalent transcripts in real time, in Hindi, and use deep-learning based architectures to retrieve corresponding candidate answers which are instantly played to the users. We report several insights from an earlier phase of running question-answering programmes through a manual operation, to how it was transitioned to an automated setup, and document the user experiences during this journey.
{"title":"Early Results from Automating Voice-based Question-Answering Services Among Low-income Populations in India","authors":"Aman Khullar, M. Santosh, Praveen Kumar, Shoaib Rahman, Rajeshwari Tripathi, Deepak Kumar, Sangeeta Saini, Rachit Pandey, Aaditeshwar Seth","doi":"10.1145/3460112.3471946","DOIUrl":"https://doi.org/10.1145/3460112.3471946","url":null,"abstract":"Question-answering systems where users can ask questions based on emergent needs which are then answered by experts or peers, have emerged as an important information seeking modality on digital platforms. Automating this process has been an active area of research since many years, to identify relevant answers from pre-existing question-answer databases. We report on the feasibility of running automated question-answering systems in the context of rural and less-literate users in India, accessed through IVR (Interactive Voice Response) systems. We use commercial speech recognition APIs to convert audio questions asked by users into their equivalent transcripts in real time, in Hindi, and use deep-learning based architectures to retrieve corresponding candidate answers which are instantly played to the users. We report several insights from an earlier phase of running question-answering programmes through a manual operation, to how it was transitioned to an automated setup, and document the user experiences during this journey.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134179499","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}
Despite decades of research confirming the benefits, most farms do not incorporate soil moisture sensing into their irrigation practices. Soil moisture sensing can be broken into two broad approaches, both of which have drawbacks. In situ sensors are installed in the ground, tend to be difficult to deploy and maintain, and have high costs. Remote-sensing based approaches use radars to infer soil moisture from surface reflection properties. While completely wireless, remote sensing suffers from lower resolution and accuracy compared to in situ sensing. We propose a hybrid approach that combines the advantages of both. This paper introduces the idea of using inexpensive in situ backscatter tags with above-ground radars, which enables completely wireless soil moisture measurements with high-accuracy and high-resolution. Our key idea is introducing a simple, power efficient modulation scheme that enables commodity radars to easily detect and range the underground tag. We have benchmarked our approach against oven-based, industry-standard ground-truth measurements and demonstrated that, at a realistic depth and across several types of soil, we achieve a 90th percentile error of 3.4%, which is the same accuracy as state-of-the-art in situ sensors. We also demonstrate that our approach works with similar accuracy at a real farm.
{"title":"Low-cost In-ground Soil Moisture Sensing with Radar Backscatter Tags","authors":"Colleen Josephson","doi":"10.1145/3460112.3472326","DOIUrl":"https://doi.org/10.1145/3460112.3472326","url":null,"abstract":"Despite decades of research confirming the benefits, most farms do not incorporate soil moisture sensing into their irrigation practices. Soil moisture sensing can be broken into two broad approaches, both of which have drawbacks. In situ sensors are installed in the ground, tend to be difficult to deploy and maintain, and have high costs. Remote-sensing based approaches use radars to infer soil moisture from surface reflection properties. While completely wireless, remote sensing suffers from lower resolution and accuracy compared to in situ sensing. We propose a hybrid approach that combines the advantages of both. This paper introduces the idea of using inexpensive in situ backscatter tags with above-ground radars, which enables completely wireless soil moisture measurements with high-accuracy and high-resolution. Our key idea is introducing a simple, power efficient modulation scheme that enables commodity radars to easily detect and range the underground tag. We have benchmarked our approach against oven-based, industry-standard ground-truth measurements and demonstrated that, at a realistic depth and across several types of soil, we achieve a 90th percentile error of 3.4%, which is the same accuracy as state-of-the-art in situ sensors. We also demonstrate that our approach works with similar accuracy at a real farm.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117039806","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}
This paper covers a scoping review to establish the breadth of alternative credit scoring literature. The field is nascent and gaining popularity due to the crucial role alternative data is playing to accelerate financial inclusion. Historically, evaluating creditworthiness required availability of past financial activity such as loan repayment. Such stringent requirements rendered people with little or no financial history ‘credit invisible’. Advancements in Artificial Intelligence and Machine Learning have enabled scoring algorithms to work with non-financial data such as digital footprints from mobile devices and psychometric data to compute credit scores. Although the largest portion of ‘credit invisibles’ are in developing economies, research in the area is predominantly originating from developed economies and most alternative credit scoring models are trained with data from developed economies. There is need for more research from developing contexts and utilization of alternative data from populations with a smaller digital footprint.
{"title":"Poster: A Scoping Review of Alternative Credit Scoring Literature","authors":"R. Njuguna, Karen Sowon","doi":"10.1145/3460112.3471972","DOIUrl":"https://doi.org/10.1145/3460112.3471972","url":null,"abstract":"This paper covers a scoping review to establish the breadth of alternative credit scoring literature. The field is nascent and gaining popularity due to the crucial role alternative data is playing to accelerate financial inclusion. Historically, evaluating creditworthiness required availability of past financial activity such as loan repayment. Such stringent requirements rendered people with little or no financial history ‘credit invisible’. Advancements in Artificial Intelligence and Machine Learning have enabled scoring algorithms to work with non-financial data such as digital footprints from mobile devices and psychometric data to compute credit scores. Although the largest portion of ‘credit invisibles’ are in developing economies, research in the area is predominantly originating from developed economies and most alternative credit scoring models are trained with data from developed economies. There is need for more research from developing contexts and utilization of alternative data from populations with a smaller digital footprint.","PeriodicalId":271063,"journal":{"name":"ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642415","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}
Sebastin Santy, Kalika Bali, M. Choudhury, Sandipan Dandapat, T. Ganu, Anurag Shukla, Jahanvi Shah, V. Seshadri
Seamless access to information in a rapidly globalizing world demands for availability of information across, ideally all but at the least a large number of, languages. Machine translation has been proposed as a technological solution to this complex problem. However, despite seven decades of research, and recently seen rapid progress in the field - thanks to deep learning and availability of large data-sets, perfect machine translation across a large number of the world’s languages still remains elusive. In fact, it is a distant and perhaps even an impossible goal. Erroneous translations, on the other hand, can be detrimental in critical situations such as talking to a law enforcement officer; or, they could potentially perpetuate social biases or stereotypes, for instance, by producing mis-gendered translations. In this work, we argue that language translation is inherently a socio-technical system, which has to be viewed, studied, and optimized for, as such. The need and context of translation, the socio-demographic factors behind the human translators as well as the consumers of the translated content affect the complexity of the translation system, as much as the accuracy of the technology and its interface. Through a series of case studies on mixed-initiative interaction based approach to translation, we bring out the various socio-technical factors and their complex interactions that one has to bear in mind while designing for the ideal human-machine translation systems. Through these observations, we make multiple recommendations which, at the core, suggest that ”solving” translation in the real sense would require more coordinated efforts between the technical (NLP) and social communities (HCI + CSCW + DEV).
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