Paper by Anatol-Fiete Näher, et al: “Substantial opportunities for global health intelligence and research arise from the combined and optimised use of secondary data within data ecosystems. Secondary data are information being used for purposes other than those intended when they were collected. These data can be gathered from sources on the verge of widespread use such as the internet, wearables, mobile phone apps, electronic health records, or genome sequencing. To utilise their full potential, we offer guidance by outlining available sources and approaches for the processing of secondary data. Furthermore, in addition to indicators for the regulatory and ethical evaluation of strategies for the best use of secondary data, we also propose criteria for assessing reusability. This overview supports more precise and effective policy decision making leading to earlier detection and better prevention of emerging health threats than is currently the case…(More)”.
How can health data be used for public benefit? 3 uses that people agree on
Article by Alison Papricia et al: “Health data can include information about health-care services, health status and behaviours, medications and genetic data, in addition to demographic information like age, education and neighbourhood.
These facts and statistics are valuable because they offer insights and information about population health and well-being. However, they can also be sensitive, and there are legitimate public concerns about how these data are used, and by whom. The term “social licence” describes uses of health data that have public support.
Studies performed in Canada, the United Kingdom and internationally have all found public support and social licence for uses of health data that produce public benefits.
However, this support is conditional. Public concerns related to privacy, commercial motives, equity and fairness must be addressed.
Our team of health policy researchers set out to build upon prior studies with actionable advice from a group of 20 experienced public and patient advisers. Studies have shown that health data use, sharing and reuse is a complex topic. So we recruited people who already had some knowledge of potential uses of health data through their roles advising research institutions, hospitals, community organizations and governments.
We asked these experienced advisers to exchange views about uses of health data that they supported or opposed. We also gathered participants’ views about requirements for social licence, such as privacy, security and transparency.
Consensus views: After hours of facilitated discussion and weeks of reflection, all 20 participants agreed on some applications and uses of health data that are within social licence, and some that are not.
Participants agreed it is within social licence for health data to be used by:
- health-care practitioners — to directly improve the health-care decisions and services provided to a patient.
- governments, health-care facilities and health-system administrators — to understand and improve health care and the health-care system.
- university-based researchers — to understand the drivers of disease and well-being.
Participants agreed that it is not within social licence for:
- an individual or organization to sell (or re-sell) another person’s identified health data.
- health data to be used for a purpose that has no patient, public or societal benefit.
Points of disagreement: Among other topics, the participants discussed uses of health data about systemically marginalized populations and companies using health data. Though some participants saw benefits from both practices, there was not consensus support for either.
For example, participants were concerned that vulnerable populations could be exploited, and that companies would put profit ahead of public benefits. Participants also worried that if harms were done by companies or to marginalized populations, they could not be “undone.” Several participants expressed skepticism about whether risks could be managed, even if additional safeguards are in place.
The participants also had different views about what constitutes an essential requirement for social licence. This included discussions about benefits, governance, patient consent and involvement, equity, privacy and transparency.
Collectively, they generated a list of 85 essential requirements, but 38 of those requirements were only seen as essential by one person. There were also cases where some participants actively opposed a requirement that another participant thought was essential…(More)”
Civic Switchboard: Connecting Libraries and Community Information Networks
“Civic Switchboard is an Institute of Museum and Library Services supported effort that aims to develop the capacity of academic and public libraries in civic data ecosystems.
We encourage partnerships between libraries and local data intermediaries that will better serve data users, further democratize data, and support equitable access to information. Our project is created an online guide and toolkit for libraries interested in expanding (or beginning) their role around civic information…(More)”.
A Comparative Study of Citizen Crowdsourcing Platforms and the Use of Natural Language Processing (NLP) for Effective Participatory Democracy
Paper by Carina Antonia Hallin: ‘The use of crowdsourcing platforms to harness citizen insights for policymaking has gained increasing importance in regional and national policy planning. Participatory democracy using crowdsourcing platforms includes various initiatives, such as generating ideas for new law reforms (Aitamurto and Landemore 2015], economic development, and solving challenges related to how to create inclusive social actions and interventions for better, healthier, and more prosperous local communities (Bentley and Pugalis, 2014). Such case observations, coupled with the increasing prevalence of internet-based communication, point to the real benefits of implementing participatory democracies on a mass scale in which citizens are invited to contribute their ideas, opinions, and deliberations (Salganik and Levy 2015). By adopting collective intelligence platforms, public authorities can harness local knowledge from citizens to find the right ‘policy mix’ and collaborate with citizens and relevant actors in the policymaking processes. This comparative study aims to validate the adoption of collective intelligence and artificial intelligence/natural language processing (NLP) on crowdsourcing platforms for effective participatory democracy and policymaking in local governments. The study compares 15 citizen crowdsourcing platforms, including Natural language Processing (NLP), for policymaking across Europe and the United States. The study offers a framework for working with citizen crowdsourcing platforms and exploring the usefulness of NLP on the platforms for effective participatory democracy…(More)”.
Human-AI Teaming
Report by the National Academies of Sciences, Engineering, and Medicine: “Although artificial intelligence (AI) has many potential benefits, it has also been shown to suffer from a number of challenges for successful performance in complex real-world environments such as military operations, including brittleness, perceptual limitations, hidden biases, and lack of a model of causation important for understanding and predicting future events. These limitations mean that AI will remain inadequate for operating on its own in many complex and novel situations for the foreseeable future, and that AI will need to be carefully managed by humans to achieve their desired utility.
Human-AI Teaming: State-of-the-Art and Research Needs examines the factors that are relevant to the design and implementation of AI systems with respect to human operations. This report provides an overview of the state of research on human-AI teaming to determine gaps and future research priorities and explores critical human-systems integration issues for achieving optimal performance…(More)”
Filling Public Data Gaps
Report by Judah Axelrod, Karolina Ramos, and Rebecca Bullied: “Data are central to understanding the lived experiences of different people and communities and can serve as a powerful force for promoting racial equity. Although public data, including foundational sources for policymaking such as the US Census Bureau’s American Community Survey (ACS), offer accessible information on a range of topics, challenges of timeliness, granularity, representativeness, and degrees of disaggregation can limit those data’s utility for real-time analysis. Private data—data produced by private-sector organizations either through standard business or to market as an asset for purchase—can serve as a richer, more granular, and higher-frequency supplement or alternative to public data sources. This raises questions about how well private data assets can offer race-disaggregated insights that can inform policymaking.
In this report, we explore the current landscape of public-private data sharing partnerships that address topic areas where racial equity research faces data gaps: wealth and assets, financial well-being and income, and employment and job quality. We held 20 semistructured interviews with current producers and users of private-sector data and subject matter experts in the areas of data-sharing models and ethical data usage. Our findings are divided into five key themes:
- Incentives and disincentives, benefits, and risks to public-private data sharing
Agreements with prestigious public partners can bolster credibility for private firms and broaden their customer base, while public partners benefit from access to real-time, granular, rich data sources. But data sharing is often time and labor intensive, and firms can be concerned with conflicting business interests or diluting the value of proprietary data assets. - Availability of race-disaggregated data sources
We found no examples in our interviews of race-disaggregated data sources related to our thematic focus areas that are available externally. However, there are promising methods for data imputation, linkage, and augmentation through internal surveys. - Data collaboratives in practice
Most public-private data sharing agreements we learned about are between two parties and entail free or “freemium” access. However, we found promising examples of multilateral agreements that diversify the data-sharing landscape. - From data champions to data stewards
We found many examples of informal data champions who bear responsibility for relationship-building and securing data partnerships. This role has yet to mature to an institutionalized data steward within private firms we interviewed, which can make data sharing a fickle process. - Considerations for ethical data usage
Data privacy and transparency about how data are accessed and used are prominent concerns among prospective data users. Interviewees also stressed the importance of not privileging existing quantitative data above qualitative insights in cases where communities have offered long-standing feedback and narratives about their own experiences facing racial inequities, and that policymakers should not use a need to collect more data as an excuse for delaying policy action.
Our research yielded several recommendations for data producers and users that engage in data sharing, and for funders seeking to advance data-sharing efforts and promote racial equity…(More)”
The 15-Minute City Quantified Using Mobility Data
Paper by Timur Abbiasov et al: “Americans travel 7 to 9 miles on average for shopping and recreational activities, which is far longer than the 15-minute (walking) city advocated by ecologically-oriented urban planners. This paper provides a comprehensive analysis of local trip behavior in US cities using GPS data on individual trips from 40 million mobile devices. We define local usage as the share of trips made within 15-minutes walking distance from home, and find that the median US city resident makes only 12% of their daily trips within such a short distance. We find that differences in access to local services can explain eighty percent of the variation in 15-minute usage across metropolitan areas and 74 percent of the variation in usage within metropolitan areas. Differences in historic zoning permissiveness within New York suggest a causal link between access and usage, and that less restrictive zoning rules, such as permitting more mixed-use development, would lead to shorter travel times. Finally, we document a strong correlation between local usage and experienced segregation for poorer, but not richer, urbanites, which suggests that 15-minute cities may also exacerbate the social isolation of marginalized communities…(More)”.
Is bigger better? A study of the effect of group size on collective intelligence in online groups
Paper by Nada Hashmi, G. Shankaranarayanan and Thomas W. Malone: “What is the optimal size for online groups that use electronic communication and collaboration tools? Previous research typically suggested optimal group sizes of about 5 to 7 members, but this research predominantly examined in-person groups. Here we investigate online groups whose members communicate with each other using two electronic collaboration tools: text chat and shared editing. Unlike previous research that studied groups performing a single task, here we measure group performance using a test of collective intelligence (CI) that includes a combination of tasks specifically chosen to predict performance on a wide range of other tasks [72]. Our findings suggest that there is a curvilinear relationship between group size and performance and that the optimal group size in online groups is between 25 and 35. This, in turn, suggests that online groups may now allow more people to be productively involved in group decision-making than was possible with in-person groups in the past…(More)”.
Code for What? Computer Science for Storytelling and Social Justice
Book by Clifford Lee and Elisabeth Soep: “Educators are urged to teach “code for all”—to make a specialized field accessible for students usually excluded from it. In this book, Clifford Lee and Elisabeth Soep instead ask the question, “Code for what?” What if coding were a justice-driven medium for storytelling rather than a narrow technical skill? What if “democratizing” computer science went beyond the usual one-off workshop and empowered youth to create digital products for social impact? Lee and Soep answer these questions with stories of a diverse group of young people in Oakland, California, who combine journalism, data, design, and code to create media that makes a difference.
These teenage and young adult producers created interactive projects that explored gendered and racialized dress code policies in schools; designed tools for LBGTQ+ youth experiencing discrimination; investigated facial recognition software and what can be done about it; and developed a mobile app to promote mental health through self-awareness and outreach for support, and more, for distribution to audiences that could reach into the millions. Working with educators and media professionals at YR Media, an award-winning organization that helps young people from underserved communities build skills in media, journalism, and the arts, these teens found their own vibrant answers to “why code?” They code for insight, connection and community, accountability, creative expression, joy, and hope…(More)”.
ResearchDataGov
“ResearchDataGov.org is a product of the federal statistical agencies and units, created in response to the Foundations of Evidence-based Policymaking Act of 2018. The site is the single portal for discovery of restricted data in the federal statistical system. The agencies have provided detailed descriptions of each data asset. Users can search for data by topic, agency, and keywords. Questions related to the data should be directed to the owning agency, using the contact information on the page that describes the data. In late 2022, users will be able to apply for access to these data using a single-application process built into ResearchDataGov. ResearchDataGov.org is built by and hosted at ICPSR at the University of Michigan, under contract and guidance from the National Center for Science and Engineering Statistics within the National Science Foundation.
The data described in ResearchDataGov.org are owned by and accessed through the agencies and units of the federal statistical system. Data access is determined by the owning or distributing agency and is limited to specific physical or virtual data enclaves. Even though all data assets are listed in a single inventory, they are not necessarily available for use in the same location(s). Multiple data assets accessed in the same location may not be able to be used together due to disclosure risk and other requirements. Please note the access modality of the data in which you are interested and seek guidance from the owning agency about whether assets can be linked or otherwise used together…(More)”.