Paper by Jonathan Gray: “The past decade has seen the rise of “data portals” as online devices for making data public. They have been accorded a prominent status in political speeches, policy documents, and official communications as sites of innovation, transparency, accountability, and participation. Drawing on research on data portals around the world, data portal software, and associated infrastructures, this paper explores three approaches for studying the social life of data portals as technopolitical devices: (a) interface analysis, (b) software analysis, and (c) metadata analysis. These three approaches contribute to the study of the social lives of data portals as dynamic, heterogeneous, and contested sites of public sector datafication. They are intended to contribute to critically assessing how participation around public sector datafication is invited and organized with portals, as well as to rethinking and recomposing them…(More)”.
Digital Anthropology Meets Data Science
Article by Katie Hillier: “Analyzing online ecosystems in real time, teams of anthropologists and data scientists can begin to understand rapid social changes as they happen.
Ask not what data science can do for anthropology, but what anthropology can do for data science. —Anders Kristian Munk, Why the World Needs Anthropologists Symposium 2022
In the last decade, emerging technologies, such as AI, immersive realities, and new and more addictive social networks, have permeated almost every aspect of our lives. These innovations are influencing how we form identities and belief systems. Social media influences the rise of subcultures on TikTok, the communications of extremist communities on Telegram, and the rapid spread of conspiracy theories that bounce around various online echo chambers.
People with shared values or experiences can connect and form online cultures at unprecedented scales and speeds. But these new cultures are evolving and shifting faster than our current ability to understand them.
To keep up with the depth and speed of online transformations, digital anthropologists are teaming up with data scientists to develop interdisciplinary methods and tools to bring the deep cultural context of anthropology to scales available only through data science—producing a surge in innovative methodologies for more effectively decoding online cultures in real time…(More)”.
Five Enablers for a New Phase of Behavioral Science
Article by Michael Hallsworth: “Over recent weeks I’ve been sharing parts of a “manifesto” that tries to give a coherent vision for the future of applied behavioral science. Stepping back, if I had to identify a theme that comes through the various proposals, it would be the need for self-reflective practice.
Behavioral science has seen a tremendous amount of growth and interest over the last decade, largely focused on expanding its uses and methods. My sense is it’s ready for a new phase of maturity. That maturity involves behavioral scientists reflecting on the various ways that their actions are shaped by structural, institutional, environmental, economic, and historical factors.
I’m definitely not exempt from this need for self-reflection. There are times when I’ve focused on a cognitive bias when I should have been spending more time exploring the context and motivations for a decision instead. Sometimes I’ve homed in on a narrow slice of a problem that we can measure, even if that means dispensing with wider systemic effects and challenges. Once I spent a long time trying to apply the language of heuristics and biases to explain why people were failing to use the urgent care alternatives to hospital emergency departments, before realizing that their behavior was completely reasonable.
The manifesto critiques things like this, but it doesn’t have all the answers. Because it tries to both cover a lot of ground and go into detail, many of the hard knots of implementation go unpicked. The truth is that writing reports and setting goals is the easy part. Turning those goals into practice is much tougher; as behavioral scientists know, there is often a gap between intention and action.
Right now, I and others don’t always realize the ambitions set out in the manifesto. Changing that is going to take time and effort, and it will involve the discomfort of disrupting familiar practices. Some have made public commitments in this direction; my organization is working on upgrading its practices in line with proposals around making predictions prior to implementation, strengthening RCTs to cope with complexity, and enabling people to use behavioral science, among others.
The truth is that writing reports and setting goals is the easy part. Turning those goals into practice is much tougher; as behavioral scientists know, there is often a gap between intention and action.
But changes by individual actors will not be enough. The big issue is that several of the proposals require coordination. For example, one of the key ideas is the need for more multisite studies that are well coordinated and have clear goals. Another prioritizes developing international professional networks to support projects in low- and middle-income countries…(More)”.
The Rise of Virtual Communities
Book by Amber Atherton: “Uncover the fascinating history of virtual communities and how we connect to each other online. The Rise of Virtual Communities, explores the earliest online community platforms, mapping the technological evolutions, and the individuals, that have shaped the culture of the internet.
Read in-depth interviews with the visionary founders of iconic online platforms, and uncover the history of virtual communities and how the industry has developed over time. Featuring never-before told stories, this exploration introduces new ideas and predictions for the future, explaining how we got here and challenging what we think we may know about building online communities….(More)”.
International Data Governance – Pathways to Progress
Press Release: “In May 2023, the United Nations System Chief Executives Board for Coordination endorsed International Data Governance – Pathways to Progress, developed through the High-level Committee on Programmes (HLCP) which approved the paper at its 45th session in March 2023. International Data Governance – Pathways to Progress and its addenda were developed by the HLCP Working Group on International Data Governance…(More)”. (See Annex 1: Mapping and Comparing Data Governance Frameworks).
Regulating Cross-Border Data Flows
Book by Bryan Mercurio, and Ronald Yu: “Data is now one of, if not the world’s most valuable resource. The adoption of data-driven applications across economic sectors has made data and the flow of data so pervasive that it has become integral to everything we as members of society do – from conducting our finances to operating businesses to powering the apps we use every day. For this reason, governing cross-border data flows is inherently difficult given the ubiquity and value of data, and the impact government policies can have on national competitiveness, business attractiveness and personal rights. The challenge for governments is to address in a coherent manner the broad range of data-related issues in the context of a global data-driven economy.
This book engages with the unexplored topic of why and how governments should develop a coherent and consistent strategic framework regulating cross-border data flows. The objective is to fill a very significant gap in the legal and policy setting by considering multiple perspectives in order to assist in the development of a jurisdiction’s coherent and strategic policy framework…(More)“.
3 barriers to successful data collaboratives
Article by Federico Bartolomucci: “Data collaboratives have proliferated in recent years as effective means of promoting the use of data for social good. This type of social partnership involves actors from the private, public, and not-for-profit sectors working together to leverage public or private data to enhance collective capacity to address societal and environmental challenges. The California Data Collaborative for instance, combines the data of numerous Californian water managers to enhance data-informed policy and decision making.
But, in my years as a researcher studying more than a hundred cases of data collaboratives, I have observed widespread feelings of isolation among collaborating partners due to the absence of success-proven reference models. …Below, I provide an overview of three governance challenges faced by practitioners, as well as recommendations for addressing them. In doing so, I encourage every practitioner embarking on a data collaborative initiative to reflect on these challenges and create ad-hoc strategies to address them…
1. Overly relying on grant funding limits a collaborative’s options.
Data Collaboratives are typically conceived as not-for-profit projects, relying solely on grant funding from the founding partners. This is the case, for example, with TD1_Index, a global collaboration that seeks to gather data on Type 1 diabetes, raise awareness, and advance research on the topic. Although grant funding schemas work in some cases (like in that of T1D_Index), relying solely on grant funding makes a data collaborative heavily dependent on the willingness of one or more partners to sustain its activities and hinders its ability to achieve operational and decisional autonomy.
Operational and decisional autonomy indeed appears to be a beneficial condition for a collaborative to develop trust, involve other partners, and continuously adapt its activities and structure to external events—characteristics required for operating in a highly innovative sector.
Hybrid business models that combine grant funding with revenue-generating activities indicate a promising evolutionary path. The simplest way to do this is to monetize data analysis and data stewardship services. The ActNow Coalition, a U.S.-based not-for-profit organization, combines donations with client-funded initiatives in which the team provides data collection, analysis, and visualization services. Offering these types of services generates revenues for the collaborative and gaining access to them is among the most compelling incentives for partners to join the collaboration.
In studying data collaboratives around the world, two models emerge as most effective: (1) pay-per-use models, in which collaboration partners can access data-related services on demand (see Civity NL and their project Sniffer Bike) and (2) membership models, in which participation in the collaborative entitles partners to access certain services under predefined conditions (see the California Data Collaborative).
2. Demonstrating impact is key to a collaborative’s survival.
As partners’ participation in data collaboratives is primarily motivated by a shared social purpose, the collaborative’s ability to demonstrate its efficacy in achieving its purpose means being able to defend its raison d’être. Demonstrating impact enables collaboratives to retain existing partners, renew commitments, and recruit new partners…(More)”.
Misunderstanding Misinformation
Article by Claire Wardle: “In the fall of 2017, Collins Dictionary named fake news word of the year. It was hard to argue with the decision. Journalists were using the phrase to raise awareness of false and misleading information online. Academics had started publishing copiously on the subject and even named conferences after it. And of course, US president Donald Trump regularly used the epithet from the podium to discredit nearly anything he disliked.
By spring of that year, I had already become exasperated by how this term was being used to attack the news media. Worse, it had never captured the problem: most content wasn’t actually fake, but genuine content used out of context—and only rarely did it look like news. I made a rallying cry to stop using fake news and instead use misinformation, disinformation, and malinformation under the umbrella term information disorder. These terms, especially the first two, have caught on, but they represent an overly simple, tidy framework I no longer find useful.
Both disinformation and misinformation describe false or misleading claims, but disinformation is distributed with the intent to cause harm, whereas misinformation is the mistaken sharing of the same content. Analyses of both generally focus on whether a post is accurate and whether it is intended to mislead. The result? We researchers become so obsessed with labeling the dots that we can’t see the larger pattern they show.
By focusing narrowly on problematic content, researchers are failing to understand the increasingly sizable number of people who create and share this content, and also overlooking the larger context of what information people actually need. Academics are not going to effectively strengthen the information ecosystem until we shift our perspective from classifying every post to understanding the social contexts of this information, how it fits into narratives and identities, and its short-term impacts and long-term harms…(More)”.
AI Is Tearing Wikipedia Apart
Article by Claire Woodcock: “As generative artificial intelligence continues to permeate all aspects of culture, the people who steward Wikipedia are divided on how best to proceed.
During a recent community call, it became apparent that there is a community split over whether or not to use large language models to generate content. While some people expressed that tools like Open AI’s ChatGPT could help with generating and summarizing articles, others remained wary.
The concern is that machine-generated content has to be balanced with a lot of human review and would overwhelm lesser-known wikis with bad content. While AI generators are useful for writing believable, human-like text, they are also prone to including erroneous information, and even citing sources and academic papers which don’t exist. This often results in text summaries which seem accurate, but on closer inspection are revealed to be completely fabricated.
“The risk for Wikipedia is people could be lowering the quality by throwing in stuff that they haven’t checked,” Bruckman added. “I don’t think there’s anything wrong with using it as a first draft, but every point has to be verified.”
The Wikimedia Foundation, the nonprofit organization behind the website, is looking into building tools to make it easier for volunteers to identify bot-generated content. Meanwhile, Wikipedia is working to draft a policy that lays out the limits to how volunteers can use large language models to create content.
The current draft policy notes that anyone unfamiliar with the risks of large language models should avoid using them to create Wikipedia content, because it can open the Wikimedia Foundation up to libel suits and copyright violations—both of which the nonprofit gets protections from but the Wikipedia volunteers do not. These large language models also contain implicit biases, which often result in content skewed against marginalized and underrepresented groups of people.
The community is also divided on whether large language models should be allowed to train on Wikipedia content. While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked…(More)”.
The Ethics of Artificial Intelligence for the Sustainable Development Goals
Book by Francesca Mazzi and Luciano Floridi: “Artificial intelligence (AI) as a general-purpose technology has great potential for advancing the United Nations Sustainable Development Goals (SDGs). However, the AI×SDGs phenomenon is still in its infancy in terms of diffusion, analysis, and empirical evidence. Moreover, a scalable adoption of AI solutions to advance the achievement of the SDGs requires private and public actors to engage in coordinated actions that have been analysed only partially so far. This volume provides the first overview of the AI×SDGs phenomenon and its related challenges and opportunities. The first part of the book adopts a programmatic approach, discussing AI×SDGs at a theoretical level and from the perspectives of different stakeholders. The second part illustrates existing projects and potential new applications…(More)”.