The politics of data visualisation and policy making

Nash, Kathryn; Trott, Verity; Allen, William

doi:10.1177/13548565221079156

Cited by 9 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To be effective in this aim, findings need to be presented in a clear and concise manner. Teams requested assistance with data visualization and advice on how to best present findings to policy makers 12 .…”

Section: Data Analysis and Presentationmentioning

confidence: 99%

Short Communication - Lessons learnt during the implementation of Unity-aligned SARS-CoV-2 seroprevalence studies in Africa

Farley

Okeibunor

Baldé

et al. 2023

Preprint

View full text Add to dashboard Cite

The WHO Unity Studies initiative engaged low- and middle-income countries in the implementation of standardized SARS-CoV-2 sero-epidemiological investigation protocols and timely sharing of comparable results for evidence-based action. To gain a deeper understanding of the methodological challenges faced when conducting seroprevalence studies in the Africa region, we conducted unstructured interviews with key study teams in five countries. We discuss the challenges identified: participant recruitment and retention, sample frame, sample and data management, data analysis and presentation to policy makers. Potential solutions to aid future implementation include preparedness actions such as the development of new tools, robust planning and practice.

show abstract

Section: Data Analysis and Presentationmentioning

confidence: 99%

Short Communication - Lessons learnt during the implementation of Unity-aligned SARS-CoV-2 seroprevalence studies in Africa

Farley

Okeibunor

Baldé

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors pinpointed that group privacy issues can be related to a social group, a firm, and even a nation. Nash et al [170] discussed privacy issues in policy-making concerning an individual or groups. The authors discussed an example of how the collection of data by tech giants such as Facebook violates Australia's privacy principles.…”

Section: Group Privacy: a New Dimension Of Privacymentioning

confidence: 99%

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

View full text Add to dashboard Cite

Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications is has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings.

show abstract

“…One of the main aims of the Unity Studies is to provide robust evidence for rapid policy decision‐making 1 . To be effective in this aim, findings need to be presented in a clear and concise manner 12 . Teams requested assistance with data visualisation and advice on how to best present findings to policy makers.…”

Section: Introductionmentioning

confidence: 99%

“… 1 To be effective in this aim, findings need to be presented in a clear and concise manner. 12 Teams requested assistance with data visualisation and advice on how to best present findings to policy makers.…”

Section: Introductionmentioning

confidence: 99%

Short communication—Lessons learnt during the implementation of Unity‐aligned SARS‐CoV‐2 seroprevalence studies in Africa

Farley,

Okeibunor,

Balde

et al. 2023

Influenza Resp Viruses

View full text Add to dashboard Cite

The WHO Unity Studies initiative engaged low‐ and middle‐income countries in the implementation of standardised SARS‐CoV‐2 sero‐epidemiological investigation protocols and timely sharing of comparable results for evidence‐based action. To gain a deeper understanding of the methodological challenges faced when conducting seroprevalence studies in the African region, we conducted unstructured interviews with key study teams in five countries. We discuss the challenges identified: participant recruitment and retention, sampling, sample and data management, data analysis and presentation. Potential solutions to aid future implementation include preparedness actions such as the development of new tools, robust planning and practice.

show abstract

The politics of data visualisation and policy making

Cited by 9 publications

References 32 publications

Short Communication - Lessons learnt during the implementation of Unity-aligned SARS-CoV-2 seroprevalence studies in Africa

Short Communication - Lessons learnt during the implementation of Unity-aligned SARS-CoV-2 seroprevalence studies in Africa

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

Short communication—Lessons learnt during the implementation of Unity‐aligned SARS‐CoV‐2 seroprevalence studies in Africa

Contact Info

Product

Resources

About