Policy primer on non-personal data 

Curated on Posted on by Stefaan Verhulst

Primer by the International Chamber of Commerce: “Non-personal data plays a critical role in providing solutions to global challenges. Unlocking its full potential requires policymakers, businesses, and all other stakeholders to collaborate to construct policy environments that can capitalise on its benefits.  

This report gives insights into the different ways that non-personal data has a positive impact on society, with benefits including, but not limited to: 

  1. Tracking disease outbreaks; 
  2. Facilitating international scientific cooperation; 
  3. Understanding climate-related trends; 
  4.  Improving agricultural practices for increased efficiency; 
  5. Optimising energy consumption; 
  6. Developing evidence-based policy; 
  7. Enhancing cross-border cybersecurity cooperation. 

In addition, businesses of all sizes benefit from the transfer of data across borders, allowing companies to establish and maintain international supply chains and smaller businesses to enter new markets or reduce operating costs. 

Despite these benefits, international flows of non-personal data are frequently limited by restrictions and data localisation measures. A growing patchwork of regulations can also create barriers to realising the potential of non-personal data. This report explores the impact of data flow restrictions including: 

  • Hindering global supply chains; 
  • Limiting the use of AI reliant on large datasets; 
  • Disincentivising data sharing amongst companies; 
  • Preventing companies from analysing the data they hold…(More)”.

Boston experimented with using generative AI for governing. It went surprisingly well

Curated on Posted on by Stefaan Verhulst

Article by Santiago Garces and Stephen Goldsmith: “…we see the possible advances of generative AI as having the most potential. For example, Boston asked OpenAI to “suggest interesting analyses” after we uploaded 311 data. In response, it suggested two things: time series analysis by case time, and a comparative analysis by neighborhood. This meant that city officials spent less time navigating the mechanics of computing an analysis, and had more time to dive into the patterns of discrepancy in service. The tools make graphs, maps, and other visualizations with a simple prompt. With lower barriers to analyze data, our city officials can formulate more hypotheses and challenge assumptions, resulting in better decisions.

Not all city officials have the engineering and web development experience needed to run these tests and code. But this experiment shows that other city employees, without any STEM background, could, with just a bit of training, utilize these generative AI tools to supplement their work.

To make this possible, more authority would need to be granted to frontline workers who too often have their hands tied with red tape. Therefore, we encourage government leaders to allow workers more discretion to solve problems, identify risks, and check data. This is not inconsistent with accountability; rather, supervisors can utilize these same generative AI tools, to identify patterns or outliers—say, where race is inappropriately playing a part in decision-making, or where program effectiveness drops off (and why). These new tools will more quickly provide an indication as to which interventions are making a difference, or precisely where a historic barrier is continuing to harm an already marginalized community.  

Civic groups will be able to hold government accountable in new ways, too. This is where the linguistic power of large language models really shines: Public employees and community leaders alike can request that tools create visual process maps, build checklists based on a description of a project, or monitor progress compliance. Imagine if people who have a deep understanding of a city—its operations, neighborhoods, history, and hopes for the future—can work toward shared goals, equipped with the most powerful tools of the digital age. Gatekeepers of formerly mysterious processes will lose their stranglehold, and expediters versed in state and local ordinances, codes, and standards, will no longer be necessary to maneuver around things like zoning or permitting processes. 

Numerous challenges would remain. Public workforces would still need better data analysis skills in order to verify whether a tool is following the right steps and producing correct information. City and state officials would need technology partners in the private sector to develop and refine the necessary tools, and these relationships raise challenging questions about privacy, security, and algorithmic bias…(More)”

Indigenous Peoples and Local Communities Are Using Satellite Data to Fight Deforestation

Curated on Posted on by Stefaan Verhulst

Article by Katie Reytar, Jessica Webb and Peter Veit: “Indigenous Peoples and local communities hold some of the most pristine and resource-rich lands in the world — areas highly coveted by mining and logging companies and other profiteers.  Land grabs and other threats are especially severe in places where the government does not recognize communities’ land rights, or where anti-deforestation and other laws are weak or poorly enforced. It’s the reason many Indigenous Peoples and local communities often take land monitoring into their own hands — and some are now using digital tools to do it. 

Freely available satellite imagery and data from sites like Global Forest Watch and LandMark provide near-real-time information that tracks deforestation and land degradation. Indigenous and local communities are increasingly using tools like this to gather evidence that deforestation and degradation are happening on their lands, build their case against illegal activities and take legal action to prevent it from continuing.  

Three examples from Suriname, Indonesia and Peru illustrate a growing trend in fighting land rights violations with data…(More)”.

The public good of statistics – narratives from around the world

Curated on Posted on by Stefaan Verhulst

Blog by Ken Roy:” I have been looking at some of the narratives used by bodies producing Official Statistics – specifically those in a sample of recent strategies and business plans from different National Statistical Offices. Inevitably these documents focus on planned programmes of work – the key statistical outputs, the technical and methodological investments etc – and occasionally on interesting things like budgets.

When these documents touch on the rationale for (or purpose of) Official Statistics, one approach is to present Official Statistics as a ‘right’ of citizens or as essential national infrastructure. For example Statistics Finland frame Official Statistics as “our shared national capital”. A further common approach is to reference the broad purpose of improved decision making – Statistics Canada has the aim that “Canadians have the key information they need to make evidence-based decisions.”

Looking beyond these high-level statements, I was keen to find any further, more specific, expressions of real-world impacts. The following sets out some initial groups of ideas and some representative quotes.

In terms of direct impacts for citizens, some strategies have a headline aim that citizens are knowledgeable about their world – Statistics Iceland aims to enable an “informed society”. A slightly different ambition is that different groups of citizens are represented or ‘seen’ by Official Statistics. The UK Statistics Authority aims to “reflect the experiences of everyone in our society so that everyone counts, and is counted, and no one is forgotten”. There are also references to the role of Official Statistics (and data more broadly) in empowering citizens – most commonly through giving them the means to hold government to account. One of the headline aims of New Zealand’s Data Investment Plan is that “government is held to account through a robust and transparent data system”.

Also relevant to citizens is the ambition for Official Statistics to enable healthy, informed public debate – one aim of the Australian Bureau of Statistics is that their work will “provide reliable information on a range of matters critical to public debate”.

Some narratives hint at the contribution of Official Statistics systems to national economic success. Stats NZ notes that “the integrity of official data can have wide-ranging implications … such as the interest charged on government borrowing.” The Papua New Guinea statistics office references a focus on “private sector investors who want to use data and statistics to aid investment decisions”.

Finally, we come to governments. Official Statistics are regularly presented as essential to a better, more effective, government process – through establishing understanding of the circumstances and needs of citizens, businesses and places and hence supporting the development and implementation of better policies, programmes and services in response. The National Bureau of Statistics (Tanzania) sees Official Statistics as enabling “evidence-based formulation, planning, monitoring and evaluation which are key in the realization of development aspirations.” A related theme is the contribution to good governance – the United Nations presents Official Statistics as “an essential element of the accountability of governments and public bodies to the public in a democratic society…(More)”.

The Time is Now: Establishing a Mutual Commitment Framework (MCF) to Accelerate Data Collaboratives

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst, Andrew Schroeder and William Hoffman: “The key to unlocking the value of data lies in responsibly lowering the barriers and shared risks of data access, re-use, and collaboration in the public interest. Data collaboratives, which foster responsible access and re-use of data among diverse stakeholders, provide a solution to these challenges.

Today, however, setting up data collaboratives takes too much time and is prone to multiple delays, hindering our ability to understand and respond swiftly and effectively to urgent global crises. The readiness of data collaboratives during crises faces key obstacles in terms of data use agreements, technical infrastructure, vetted and reproducible methodologies, and a clear understanding of the questions which may be answered more effectively with additional data.

Organizations aiming to create data collaboratives often face additional challenges, as they often lack established operational protocols and practices which can streamline implementation, reduce costs, and save time. New regulations are emerging that should help drive the adoption of standard protocols and processes. In particular, the EU Data Governance Act and the forthcoming Data Act aim to enable responsible data collaboration. Concepts like data spaces and rulebooks seek to build trust and strike a balance between regulation and technological innovation.

This working paper advances the case for creating a Mutual Commitment Framework (MCF) in advance of a crisis that can serve as a necessary and practical means to break through chronic choke points and shorten response times. By accelerating the establishment of operational (and legally cognizable) data collaboratives, duties of care can be defined and a stronger sense of trust, clarity, and purpose can be instilled among participating entities. This structured approach ensures that data sharing and processing are conducted within well-defined, pre-authorized boundaries, thereby lowering shared risks and promoting a conducive environment for collaboration…(More)”.

Innovation in Anticipation for Migration: A Deep Dive into Methods, Tools, and Data Sources

Curated on Posted on by Stefaan Verhulst

Blog by Sara Marcucci and Stefaan Verhulst: “In the ever-evolving landscape of anticipatory methods for migration policy, innovation is a dynamic force propelling the field forward. This seems to be happening in two main ways: first, as we mentioned in our previous blog, one of the significant shifts lies in the blurring of boundaries between quantitative forecasting and qualitative foresight, as emerging mixed-method approaches challenge traditional paradigms. This transformation opens up new pathways for understanding complex phenomena, particularly in the context of human migration flows. 

Innovation in Anticipation for Migration: A Deep Dive into Methods, Tools, and Data Sources

Second, the innovation happening today is not necessarily rooted in the development of entirely new methodologies, but rather in how existing methods are adapted and enhanced. Indeed, innovation seems to extend to the utilization of diverse tools and data sources that bolster the effectiveness of existing methods, offering a more comprehensive and timely perspective on migration trends.

In the context of this blog series, methods refer to the various approaches and techniques used to anticipate and analyze migration trends, challenges, and opportunities. These methods are employed to make informed decisions and develop policies related to human migration. They can include a wide range of strategies to gather and interpret data and insights in the field of migration policy. 

Tools, on the other hand, refer to the specific instruments or technologies used to support and enhance the effectiveness of these methods. They encompass a diverse set of resources and technologies that facilitate data collection, analysis, and decision-making in the context of migration policy. These tools can include both quantitative and qualitative data collection and analysis tools, as well as innovative data sources, software, and techniques that help enhance anticipatory methods.

This blog aims to deep dive into the main anticipatory methods adopted in the field of migration, as well as some of the tools and data sources employed to enhance and experiment with them. First, the blog will provide a list of methods considered; second, it will illustrate the main innovative tools employed, and finally it will provide a set of new, non-traditional data sources that are increasingly being used to feed anticipatory methods…(More)”.

The State of Open Data 2023

Curated on Posted on by Stefaan Verhulst

Report by Springer Nature, Digital Science and Figshare: “The 2023 survey showed that the key motivations for researchers to share their data remain very similar to previous years, with full citation of research papers or a data citation ranking highly. 89% of respondents also said they make their data available publicly, however almost three quarters of respondents had never received support with planning, managing or sharing research data.

One size does not fit all: Variations in responses from different areas of expertise and geographies highlight a need for a more nuanced approach to research data management support globally. For example, 64% of respondents supported the idea of a national mandate for making research data openly available, with Indian and German respondents more likely to support this idea (both 71%).

Credit is an ongoing issue: For eight years running, our survey has revealed a recurring concern among researchers: the perception that they don’t receive sufficient recognition for openly sharing their data. 60% of respondents said they receive too little credit for sharing their data.

AI awareness hasn’t translated to action: For the first time, this year we asked survey respondents to indicate if they were using ChatGPT or similar AI tools for data collection, data processing and metadata collection. The most common response to all three questions was ‘I’m aware of these tools but haven’t considered it.’..(More)”.

Data Governance and Privacy Challenges in the Digital Healthcare Revolution

Curated on Posted on by Stefaan Verhulst

Paper by Nargiz Kazimova: “The onset of the COVID-19 pandemic has catalyzed an imperative for digital transformation in the healthcare sector. This study investigates the accelerated shift towards a digitally-enhanced healthcare delivery system, advocating for the widespread adoption of telemedicine and the relaxation of regulatory barriers. The paper also scrutinizes the burgeoning use of electronic health records, wearable devices, artificial intelligence, and machine learning, and how these technologies offer promising avenues for improving patient care and medical outcomes. Despite the advancements, the rapid digital integration raises significant privacy and security concerns. The stigma associated with certain illnesses and potential discrimination presents serious challenges that digital healthcare innovations can exacerbate.
This research underscores the criticality of stringent data governance to safeguard personal health information in the face of growing digitalization. The analysis begins with an exploration of the data governance role in optimizing healthcare outcomes and preserving privacy, followed by an assessment of the breadth and depth of health data proliferation. The paper subsequently navigates the complex legal and ethical terrain, contrasting HIPAA and GDPR frameworks to underline the current regulatory challenges.
A comprehensive set of strategic recommendations is provided for reinforcing data governance and enhancing privacy protection in healthcare. The author advises on updating legal provisions to match the dynamic healthcare environment, widening the scope of privacy laws, and improving the transparency of data-sharing practices. The establishment of ethical guidelines for the collection and use of health data is also recommended, focusing on explicit consent, decision-making transparency, harm accountability, maintenance of data anonymity, and the mitigation of biases in datasets.
Moreover, the study advocates for stronger transparency in data sharing with clear communication on data use, rigorous internal and external audit mechanisms, and informed consent processes. The conclusion calls for increased collaboration between healthcare providers, patients, administrative staff, ethicists, regulators, and technology companies to create governance models that reconcile patient rights with the expansive use of health data. The paper culminates in a call to action for a balanced approach to privacy and innovation in the data-driven era of healthcare…(More)”.

The AI regulations that aren’t being talked about

Curated on Posted on by Stefaan Verhulst

Article by Deloitte: “…But our research shows that this focus may be overlooking some of the most important tools already on the books. Of the 1,600+ policies we analyzed, only 11% were focused on regulating AI-adjacent issues like data privacy, cybersecurity, intellectual property, and so on (Figure 5). Even when limiting the search to only regulations, 60% were focused directly on AI and only 40% on AI-adjacent issues (Figure 5). For example, several countries have data protection agencies with regulatory powers to help protect citizens’ data privacy. But while these agencies may not have AI or machine learning named specifically in their charters, the importance of data in training and using AI models makes them an important AI-adjacent tool.

This can be problematic because directly regulating a fast-moving technology like AI can be difficult. Take the hypothetical example of removing bias from home loan decisions. Regulators could accomplish this goal by mandating that AI should have certain types of training data to ensure that the models are representative and will not produce biased results, but such an approach can become outdated when new methods of training AI models emerge. Given the diversity of different types of AI models already in use, from recurrent neural networks to generative pretrained transformers to generative adversarial networks and more, finding a single set of rules that can deliver what the public desires both now, and in the future, may be a challenge…(More)”.

The battle over right to repair is a fight over your car’s data

Curated on Posted on by Stefaan Verhulst

Article by Ofer Tur-Sinai: “Cars are no longer just a means of transportation. They have become rolling hubs of data communication. Modern vehicles regularly transmit information wirelessly to their manufacturers.

However, as cars grow “smarter,” the right to repair them is under siege.

As legal scholars, we find that the question of whether you and your local mechanic can tap into your car’s data to diagnose and repair spans issues of property rights, trade secrets, cybersecurity, data privacy and consumer rights. Policymakers are forced to navigate this complex legal landscape and ideally are aiming for a balanced approach that upholds the right to repair, while also ensuring the safety and privacy of consumers…

Until recently, repairing a car involved connecting to its standard on-board diagnostics port to retrieve diagnostic data. The ability for independent repair shops – not just those authorized by the manufacturer – to access this information was protected by a state law in Massachusetts, approved by voters on Nov. 6, 2012, and by a nationwide memorandum of understanding between major car manufacturers and the repair industry signed on Jan. 15, 2014.

However, with the rise of telematics systems, which combine computing with telecommunications, these dynamics are shifting. Unlike the standardized onboard diagnostics ports, telematics systems vary across car manufacturers. These systems are often protected by digital locks, and circumventing these locks could be considered a violation of copyright law. The telematics systems also encrypt the diagnostic data before transmitting it to the manufacturer.

This reduces the accessibility of telematics information, potentially locking out independent repair shops and jeopardizing consumer choice – a lack of choice that can lead to increased costs for consumers….

One issue left unresolved by the legislation is the ownership of vehicle data. A vehicle generates all sorts of data as it operates, including location, diagnostic, driving behavior, and even usage patterns of in-car systems – for example, which apps you use and for how long.

In recent years, the question of data ownership has gained prominence. In 2015, Congress legislated that the data stored in event data recorders belongs to the vehicle owner. This was a significant step in acknowledging the vehicle owner’s right over specific datasets. However, the broader issue of data ownership in today’s connected cars remains unresolved…(More)”.