Paper by Seliem El-Sayed, Ilona Kickbusch & Barbara Prainsack: “Most data governance frameworks are designed to protect the individuals from whom data originates. However, the impacts of digital practices extend to a broader population and are embedded in significant power asymmetries within and across nations. Further, inequities in digital societies impact everyone, not just those directly involved. Addressing these challenges requires an approach which moves beyond individual data control and is grounded in the values of equity and a just contribution of benefits and risks from data use. Solidarity-based data governance (in short: data solidarity), suggests prioritising data uses over data type and proposes that data uses that generate public value should be actively facilitated, those that generate significant risks and harms should be prohibited or strictly regulated, and those that generate private benefits with little or no public value should be ‘taxed’ so that profits generated by corporate data users are reinvested in the public domain. In the context of global health data governance, the public value generated by data use is crucial. This contribution clarifies the meaning, importance, and potential of public value within data solidarity and outlines methods for its operationalisation through the PLUTO tool, specifically designed to assess the public value of data uses…(More)”.
The Bridging Dictionary
About: “What if generative AI could help us understand people with opposing views better just by showing how they use common words and phrases differently? That’s the deceptively simple-sounding idea behind a new experiment from MIT’s Center for Constructive Communication (CCC).
It’s called the Bridging Dictionary (BD), a research prototype that’s still very much a work in progress – one we hope your feedback will help us improve.
The Bridging Dictionary identifies words and phrases that both reflect and contribute to sharply divergent views in our fractured public sphere. That’s the “dictionary” part. If that’s all it did, we could just call it the “Frictionary.” But the large language model (LLM) that undergirds the BD also suggests less polarized alternatives – hence “bridging.”
In this prototype, research scientist Doug Beeferman and a team at CCC led by Maya Detwiller and Dennis Jen used thousands of transcripts and opinion articles from foxnews.com and msnbc.com as proxies for the conversation on the right and the left. You’ll see the most polarized words and phrases when you sample the BD for yourself, but you can also plug any term of your choosing into the search box. (For a more complete explanation of the methodology behind the BD, see https://bridgingdictionary.org/info/ .)…(More)”.
The People Say
About: “The People Say is an online research hub that features first-hand insights from older adults and caregivers on the issues most important to them, as well as feedback from experts on policies affecting older adults.
This project particularly focuses on the experiences of communities often under-consulted in policymaking, including older adults of color, those who are low income, and/or those who live in rural areas where healthcare isn’t easily accessible. The People Say is funded by The SCAN Foundation and developed by researchers and designers at the Public Policy Lab.
We believe that effective policymaking listens to most-affected communities—but policies and systems that serve older adults are typically formed with little to no input from older adults themselves. We hope The People Say will help policymakers hear the voices of older adults when shaping policy…(More)”
Government reform starts with data, evidence
Article by Kshemendra Paul: “It’s time to strengthen the use of data, evidence and transparency to stop driving with mud on the windshield and to steer the government toward improving management of its programs and operations.
Existing Government Accountability Office and agency inspectors general reports identify thousands of specific evidence-based recommendations to improve efficiency, economy and effectiveness, and reduce fraud, waste and abuse. Many of these recommendations aim at program design and requirements, highlighting specific instances of overlap, redundancy and duplication. Others describe inadequate internal controls to balance program integrity with the experience of the customer, contractor or grantee. While progress is being reported in part due to stronger partnerships with IGs, much remains to be done. Indeed, GAO’s 2023 High Risk List, which it has produced going back to 1990, shows surprisingly slow progress of efforts to reduce risk to government programs and operations.
Here are a few examples:
- GAO estimates recent annual fraud of between $233 billion to $521 billion, or about 3% to 7% of federal spending. On the other hand, identified fraud with high-risk Recovery Act spending was held under 1% using data, transparency and partnerships with Offices of Inspectors General.
- GAO and IGs have collectively identified hundreds of billions in potential cost savings or improvements not yet addressed by federal agencies.
- GAO has recently described shortcomings with the government’s efforts to build evidence. While federal policymakers need good information to inform their decisions, the Commission on Evidence-Based Policymaking previously said, “too little evidence is produced to meet this need.”
One of the main reasons for agency sluggishness is the lack of agency and governmentwide use of synchronized, authoritative and shared data to support how the government manages itself.
For example, the Energy Department IG found that, “[t]he department often lacks the data necessary to make critical decisions, evaluate and effectively manage risks, or gain visibility into program results.” It is past time for the government to commit itself to move away from its widespread use of data calls, the error-prone, costly and manual aggregation of data used to support policy analysis and decision-making. Efforts to embrace data-informed approaches to manage government programs and operations are stymied by lack of basic agency and governmentwide data hygiene. While bright pockets exist, management gaps, as DOE OIG stated, “create blind spots in the universe of data that, if captured, could be used to more efficiently identify, track and respond to risks…”
The proposed approach starts with current agency operating models, then drives into management process integration to tackle root causes of dysfunction from the bottom up. It recognizes that inefficiency, fraud and other challenges are diffused, deeply embedded and have non-obvious interrelationships within the federal complex…(More)”
Participatory seascape mapping: A community-based approach to ocean governance and marine conservation
Paper by Isabel James: “Despite the global proliferation of ocean governance frameworks that feature socioeconomic variables, the inclusion of community needs and local ecological knowledge remains underrepresented. Participatory mapping or Participatory GIS (PGIS) has emerged as a vital method to address this gap by engaging communities that are conventionally excluded from ocean planning and marine conservation. Originally developed for forest management and Indigenous land reclamation, the scholarship on PGIS remains predominantly focused on terrestrial landscapes. This review explores recent research that employs the method in the marine realm, detailing common methodologies, data types and applications in governance and conservation. A typology of ocean-centered PGIS studies was identified, comprising three main categories: fisheries, habitat classification and blue economy activities. Marine Protected Area (MPA) design and conflict management are the most prevalent conservation applications of PGIS. Case studies also demonstrate the method’s effectiveness in identifying critical marine habitats such as fish spawning grounds and monitoring endangered megafauna. Participatory mapping shows particular promise in resource and data limited contexts due to its ability to generate large quantities of relatively reliable, quick and low-cost data. Validation steps, including satellite imagery and ground-truthing, suggest encouraging accuracy of PGIS data, despite potential limitations related to human error and spatial resolution. This review concludes that participatory mapping not only enriches scientific research but also fosters trust and cooperation among stakeholders, ultimately contributing to more resilient and equitable ocean governance…(More)”.
To Whom Does the World Belong?
Essay by Alexander Hartley: “For an idea of the scale of the prize, it’s worth remembering that 90 percent of recent U.S. economic growth, and 65 percent of the value of its largest 500 companies, is already accounted for by intellectual property. By any estimate, AI will vastly increase the speed and scale at which new intellectual products can be minted. The provision of AI services themselves is estimated to become a trillion-dollar market by 2032, but the value of the intellectual property created by those services—all the drug and technology patents; all the images, films, stories, virtual personalities—will eclipse that sum. It is possible that the products of AI may, within my lifetime, come to represent a substantial portion of all the world’s financial value.
In this light, the question of ownership takes on its true scale, revealing itself as a version of Bertolt Brecht’s famous query: To whom does the world belong?
Questions of AI authorship and ownership can be divided into two broad types. One concerns the vast troves of human-authored material fed into AI models as part of their “training” (the process by which their algorithms “learn” from data). The other concerns ownership of what AIs produce. Call these, respectively, the input and output problems.
So far, attention—and lawsuits—have clustered around the input problem. The basic business model for LLMs relies on the mass appropriation of human-written text, and there simply isn’t anywhere near enough in the public domain. OpenAI hasn’t been very forthcoming about its training data, but GPT-4 was reportedly trained on around thirteen trillion “tokens,” roughly the equivalent of ten trillion words. This text is drawn in large part from online repositories known as “crawls,” which scrape the internet for troves of text from news sites, forums, and other sources. Fully aware that vast data scraping is legally untested—to say the least—developers charged ahead anyway, resigning themselves to litigating the issue in retrospect. Lawyer Peter Schoppert has called the training of LLMs without permission the industry’s “original sin”—to be added, we might say, to the technology’s mind-boggling consumption of energy and water in an overheating planet. (In September, Bloomberg reported that plans for new gas-fired power plants have exploded as energy companies are “racing to meet a surge in demand from power-hungry AI data centers.”)…(More)”.
Beyond checking a box: how a social licence can help communities benefit from data reuse and AI
Article by Stefaan Verhulst and Peter Addo: “In theory, consent offers a mechanism to reduce power imbalances. In reality, existing consent mechanisms are limited and, in many respects, archaic, based on binary distinctions – typically presented in check-the-box forms that most websites use to ask you to register for marketing e-mails – that fail to appreciate the nuance and context-sensitive nature of data reuse. Consent today generally means individual consent, a notion that overlooks the broader needs of communities and groups.
While we understand the need to safeguard information about an individual such as, say, their health status, this information can help address or even prevent societal health crises. Individualised notions of consent fail to consider the potential public good of reusing individual data responsibly. This makes them particularly problematic in societies that have more collective orientations, where prioritising individual choices could disrupt the social fabric.
The notion of a social licence, which has its roots in the 1990s within the extractive industries, refers to the collective acceptance of an activity, such as data reuse, based on its perceived alignment with community values and interests. Social licences go beyond the priorities of individuals and help balance the risks of data misuse and missed use (for example, the risks of violating privacy vs. neglecting to use private data for public good). Social licences permit a broader notion of consent that is dynamic, multifaceted and context-sensitive.
Policymakers, citizens, health providers, think tanks, interest groups and private industry must accept the concept of a social licence before it can be established. The goal for all stakeholders is to establish widespread consensus on community norms and an acceptable balance of social risk and opportunity.
Community engagement can create a consensus-based foundation for preferences and expectations concerning data reuse. Engagement could take place via dedicated “data assemblies” or community deliberations about data reuse for particular purposes under particular conditions. The process would need to involve voices as representative as possible of the different parties involved, and include those that are traditionally marginalised or silenced…(More)”.
Global Trends in Government Innovation 2024
OECD Report: “Governments worldwide are transforming public services through innovative approaches that place people at the center of design and delivery. This report analyses nearly 800 case studies from 83 countries and identifies five critical trends in government innovation that are reshaping public services. First, governments are working with users and stakeholders to co-design solutions and anticipate future needs to create flexible, responsive, resilient and sustainable public services. Second, governments are investing in scalable digital infrastructure, experimenting with emergent technologies (such as automation, AI and modular code), and expanding innovative and digital skills to make public services more efficient. Third, governments are making public services more personalised and proactive to better meet people’s needs and expectations and reduce psychological costs and administrative frictions, ensuring they are more accessible, inclusive and empowering, especially for persons and groups in vulnerable and disadvantaged circumstances. Fourth, governments are drawing on traditional and non-traditional data sources to guide public service design and execution. They are also increasingly using experimentation to navigate highly complex and unpredictable environments. Finally, governments are reframing public services as opportunities and channels for citizens to exercise their civic engagement and hold governments accountable for upholding democratic values such as openness and inclusion…(More)”.
Direct democracy in the digital age: opportunities, challenges, and new approaches
Article by Pattharapong Rattanasevee, Yared Akarapattananukul & Yodsapon Chirawut: “This article delves into the evolving landscape of direct democracy, particularly in the context of the digital era, where ICT and digital platforms play a pivotal role in shaping democratic engagement. Through a comprehensive analysis of empirical data and theoretical frameworks, it evaluates the advantages and inherent challenges of direct democracy, such as majority tyranny, short-term focus, polarization, and the spread of misinformation. It proposes the concept of Liquid democracy as a promising hybrid model that combines direct and representative elements, allowing for voting rights delegation to trusted entities, thereby potentially mitigating some of the traditional drawbacks of direct democracy. Furthermore, the article underscores the necessity for legal regulations and constitutional safeguards to protect fundamental rights and ensure long-term sustainability within a direct democracy framework. This research contributes to the ongoing discourse on democratic innovation and highlights the need for a balanced approach to integrating digital tools with democratic processes…(More)”.
It Was the Best of Times, It Was the Worst of Times: The Dual Realities of Data Access in the Age of Generative AI
Article by Stefaan Verhulst: “It was the best of times, it was the worst of times… It was the spring of hope, it was the winter of despair.” –Charles Dickens, A Tale of Two Cities
Charles Dickens’s famous line captures the contradictions of the present moment in the world of data. On the one hand, data has become central to addressing humanity’s most pressing challenges — climate change, healthcare, economic development, public policy, and scientific discovery. On the other hand, despite the unprecedented quantity of data being generated, significant obstacles remain to accessing and reusing it. As our digital ecosystems evolve, including the rapid advances in artificial intelligence, we find ourselves both on the verge of a golden era of open data and at risk of slipping deeper into a restrictive “data winter.”
These two realities are concurrent: the challenges posed by growing restrictions on data reuse, and the countervailing potential brought by advancements in privacy-enhancing technologies (PETs), synthetic data, and data commons approaches. It argues that while current trends toward closed data ecosystems threaten innovation, new technologies and frameworks could lead to a “Fourth Wave of Open Data,” potentially ushering in a new era of data accessibility and collaboration…(More)” (First Published in Industry Data for Society Partnership’s (IDSP) 2024 Year in Review).