Data & policy最新文献_第8页

Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemic—ADDENDUM 在COVID-19大流行的早期阶段，使用Twitter跟踪移民情绪——附录

Data & policy Pub Date : 2022-04-04 DOI: 10.1017/dap.2022.5

Francisco Rowe, M. Mahony, Eduardo Graells-Garrido, M. Rango, Niklas Sievers

引用次数: 2

Data trust and data privacy in the COVID-19 period—ADDENDUM COVID-19期间的数据信任和数据隐私-附录

Data & policy Pub Date : 2022-04-04 DOI: 10.1017/dap.2022.4

N. Biddle, Ben Edwards, M. Gray, M. Hiscox, S. McEachern, K. Sollis

引用次数: 1

Mobile Big Data in the fight against COVID-19 移动大数据在抗击新冠肺炎中的应用

Data & policy Pub Date : 2022-02-28 DOI: 10.1017/dap.2021.39

Richard Benjamins, J. Vos, S. Verhulst

引用次数: 1

Turning mobile big data insights into public health responses in times of pandemics: Lessons learnt from the Democratic Republic of the Congo 将移动大数据见解转化为流行病时期的公共卫生应对措施：从刚果民主共和国吸取的教训

Data & policy Pub Date : 2022-02-23 DOI: 10.1017/dap.2021.30

Chloe Gueguen, Nicolas Snel, Eric Mutonji

{"title":"Turning mobile big data insights into public health responses in times of pandemics: Lessons learnt from the Democratic Republic of the Congo","authors":"Chloe Gueguen, Nicolas Snel, Eric Mutonji","doi":"10.1017/dap.2021.30","DOIUrl":"https://doi.org/10.1017/dap.2021.30","url":null,"abstract":"Abstract In low-income countries like the Democratic Republic of the Congo (DRC)—where data is scarce and national statistics offices often under-resourced—aggregated and anonymised mobile operators’ data can provide vital insights for decision-makers to promptly respond to both prevailing and new pandemics, such as COVID-19. Yet, while research on possible applications of mobile big data (MBD) analytics for COVID-19 is growing, there is still little evidence on how such use cases are actually being adopted by governmental authorities and how MBD insights can effectively be turned into informed public health actions in times of crises. This four-part commentary paper aims to bridge such literature gaps, by sharing lessons learnt from the DRC, whereby Congolese public health authorities, through a steep learning curve, have initiated a public–private sector dialogue with local mobile network operators (MNOs) and their ecosystem partners to leverage population mobility insights for COVID-19 policy-making. After having set the scene on the policy relevance of MBD analytics in the context of the DRC in the first section, the paper will then detail four key enablers that contributed, since March 2020, to accelerate Congolese authorities’ uptake of MBD, thus effectively increasing preparedness for future pandemics. Thirdly, we showcase concreate use-cases where “readiness-to-use” has actually translated into actual “usage” and “adoption” for decision-making, while introducing other use cases currently under development. Finally, we explore challenges when harnessing telco big data for decision-making with the ultimate aim to share lessons to replicate the successes and steer the development of MBD for social good in other low-income countries.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45252976","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}

引用次数: 1

From transparency to accountability of intelligent systems: Moving beyond aspirations 从智能系统的透明度到问责制:超越期望

Data & policy Pub Date : 2022-02-18 DOI: 10.1017/dap.2021.37

Rebecca Williams, Richard Cloete, Jennifer Cobbe, C. Cottrill, P. Edwards, Milan Markovic, Iman Naja, Frances Ryan, Jatinder Singh, Wei Pang

{"title":"From transparency to accountability of intelligent systems: Moving beyond aspirations","authors":"Rebecca Williams, Richard Cloete, Jennifer Cobbe, C. Cottrill, P. Edwards, Milan Markovic, Iman Naja, Frances Ryan, Jatinder Singh, Wei Pang","doi":"10.1017/dap.2021.37","DOIUrl":"https://doi.org/10.1017/dap.2021.37","url":null,"abstract":"Abstract A number of governmental and nongovernmental organizations have made significant efforts to encourage the development of artificial intelligence in line with a series of aspirational concepts such as transparency, interpretability, explainability, and accountability. The difficulty at present, however, is that these concepts exist at a fairly abstract level, whereas in order for them to have the tangible effects desired they need to become more concrete and specific. This article undertakes precisely this process of concretisation, mapping how the different concepts interrelate and what in particular they each require in order to move from being high-level aspirations to detailed and enforceable requirements. We argue that the key concept in this process is accountability, since unless an entity can be held accountable for compliance with the other concepts, and indeed more generally, those concepts cannot do the work required of them. There is a variety of taxonomies of accountability in the literature. However, at the core of each account appears to be a sense of “answerability”; a need to explain or to give an account. It is this ability to call an entity to account which provides the impetus for each of the other concepts and helps us to understand what they must each require.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41937742","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}

引用次数: 9

The Social Data Foundation model: Facilitating health and social care transformation through datatrust services 社会数据基金会模式:通过数据信托服务促进卫生和社会保健转型

Data & policy Pub Date : 2022-02-10 DOI: 10.1017/dap.2022.1

M. Boniface, L. Carmichael, W. Hall, B. Pickering, Sophie Stalla-Bourdillon, Steve Taylor

{"title":"The Social Data Foundation model: Facilitating health and social care transformation through datatrust services","authors":"M. Boniface, L. Carmichael, W. Hall, B. Pickering, Sophie Stalla-Bourdillon, Steve Taylor","doi":"10.1017/dap.2022.1","DOIUrl":"https://doi.org/10.1017/dap.2022.1","url":null,"abstract":"Abstract Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47092485","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}

引用次数: 3

A participatory approach for empowering community engagement in data governance: The Monash Net Zero Precinct 授权社区参与数据治理的参与式方法:莫纳什零净值区

Data & policy Pub Date : 2022-02-02 DOI: 10.1017/dap.2021.33

D. Sharp, Misita Anwar, Sarah Goodwin, R. Raven, L. Bartram, L. Kamruzzaman

{"title":"A participatory approach for empowering community engagement in data governance: The Monash Net Zero Precinct","authors":"D. Sharp, Misita Anwar, Sarah Goodwin, R. Raven, L. Bartram, L. Kamruzzaman","doi":"10.1017/dap.2021.33","DOIUrl":"https://doi.org/10.1017/dap.2021.33","url":null,"abstract":"Abstract Data governance is an emerging field of study concerned with how a range of actors can successfully manage data assets according to rules of engagement, decision rights, and accountabilities. Urban studies scholarship has continued to demonstrate and criticize lack of community engagement in smart city development and urban data governance projects, including in local sustainability initiatives. However, few move beyond critique to unpack in more detail what community engagement should look like. To overcome this gap, we develop and test a participatory methodology to identify approaches to empowering community engagement in data governance in the context of the Monash Net Zero Precinct in Melbourne, Australia. Our approach uses design for social innovation to enable a small group of “precinct citizens” to co-design prototypes and multicriteria mapping as a participatory appraisal method to open up and reveal a diversity of perspectives and uncertainties on data governance approaches. The findings reveal the importance of creating deliberative spaces for pluralising community engagement in data governance that consider the diverse values and interests of precinct citizens. This research points toward new ways to conceptualize and design enabling processes of community engagement in data governance and reflects on implementation strategies attuned to the politics of participation to support the embedding of these innovations within specific socio-institutional contexts.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41587784","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}

引用次数: 8

The value of network data confirmed by the Covid-19 epidemic and its expanded usages 新冠肺炎疫情确认的网络数据价值及其扩展用途

Data & policy Pub Date : 2022-01-19 DOI: 10.1017/dap.2021.31

P. Chambreuil, Ju Y. Jeon, Thierry Barba

{"title":"The value of network data confirmed by the Covid-19 epidemic and its expanded usages","authors":"P. Chambreuil, Ju Y. Jeon, Thierry Barba","doi":"10.1017/dap.2021.31","DOIUrl":"https://doi.org/10.1017/dap.2021.31","url":null,"abstract":"Abstract Data driven analysis is proven to create a competitive advantage to business. Governments and nonprofit organizations also turn to Big Data to harness its benefits and use it for social good. Among different types of data sources, location data collected from mobile networks is especially valuable for its representativeness, real-time observation, and versatility. There is a distinction between mobile positioning data (MPD) generated by the exchanges between mobile devices and the core network; versus over-the-top or system-level location data collecting individual GPS location. MPD is composed of all mobile network events regardless of the mobile phone brand, operating system, app usage, frequency bands or mobile generation; it is uniform and ubiquitous. Getting the best out of MPD relies on the knowledge of how to create an advanced algorithm for homogeneously processing this massive, complex data into insightful indicators. Anonymized and aggregated MPD enables the testing of multiple combinations with other data sources, fully abiding by GDPR, to arrive at innovative solutions. These unique insights can help tackle societal challenges (the state of mobile data for social good June 2017 GSMA, UN Global pulse). It can help to establish accurate statistics about population movements, density, location, social patterns, finances, and ambient environmental conditions. This article demonstrates how MPD has been used to help combat Covid-19 in Europe, the Middle East, and Africa. Furthermore, depending on the future direction, MPD and data analysis can serve powering economic development as well as working toward the Sustainable Development Goals, whilst respecting data privacy.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47508143","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}

引用次数: 2

Data protection for the common good: Developing a framework for a data protection-focused data commons 为了共同利益的数据保护:开发一个以数据保护为重点的数据共享框架

Data & policy Pub Date : 2022-01-17 DOI: 10.1017/dap.2021.40

Janis Wong, Tristan Henderson, K. Ball

{"title":"Data protection for the common good: Developing a framework for a data protection-focused data commons","authors":"Janis Wong, Tristan Henderson, K. Ball","doi":"10.1017/dap.2021.40","DOIUrl":"https://doi.org/10.1017/dap.2021.40","url":null,"abstract":"Abstract In our data-driven society, personal data affecting individuals as data subjects are increasingly being collected and processed by sizeable and international companies. While data protection laws and privacy technologies attempt to limit the impact of data breaches and privacy scandals, they rely on individuals having a detailed understanding of the available recourse, resulting in the responsibilization of data protection. Existing data stewardship frameworks incorporate data-protection-by-design principles but may not include data subjects in the data protection process itself, relying on supplementary legal doctrines to better enforce data protection regulations. To better protect individual autonomy over personal data, this paper proposes a data protection-focused data commons to encourage co-creation of data protection solutions and rebalance power between data subjects and data controllers. We conduct interviews with commons experts to identify the institutional barriers to creating a commons and challenges of incorporating data protection principles into a commons, encouraging participatory innovation in data governance. We find that working with stakeholders of different backgrounds can support a commons’ implementation by openly recognizing data protection limitations in laws, technologies, and policies when applied independently. We propose requirements for deploying a data protection-focused data commons by applying our findings and data protection principles such as purpose limitation and exercising data subject rights to the Institutional Analysis and Development (IAD) framework. Finally, we map the IAD framework into a commons checklist for policy-makers to accommodate co-creation and participation for all stakeholders, balancing the data protection of data subjects with opportunities for seeking value from personal data.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42723291","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}

引用次数: 5

PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics PolicyCLOUD:用于策略分析的无云服务器生态系统的原型

Data & policy Pub Date : 2022-01-16 DOI: 10.1017/dap.2022.32

O. Biran, Oshrit Feder, Y. Moatti, Athanasios Kiourtis, D. Kyriazis, George Manias, Argyro Mavrogiorgou, N. Sgouros, Martim T. Barata, Isabella Oldani, M. A. Sanguino, Pavlos Kranas, Samuele Baroni

{"title":"PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics","authors":"O. Biran, Oshrit Feder, Y. Moatti, Athanasios Kiourtis, D. Kyriazis, George Manias, Argyro Mavrogiorgou, N. Sgouros, Martim T. Barata, Isabella Oldani, M. A. Sanguino, Pavlos Kranas, Samuele Baroni","doi":"10.1017/dap.2022.32","DOIUrl":"https://doi.org/10.1017/dap.2022.32","url":null,"abstract":"Abstract We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57162309","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}

引用次数: 5