Sovereignty and Data Localization

Curated on Posted on by Stefaan Verhulst

Paper by Emily Wu: “Data localization policies impose obligations on businesses to store and process data locally, rather than in servers located overseas. The adoption of data localization laws has been increasing, driven by the fear that a nation’s sovereignty will be threatened by their inability to exert full control over data stored outside their borders. This is particularly relevant to the US given its dominance in many areas of the digital ecosystem including artificial intelligence and cloud computing.

Unfortunately, data localization policies are causing more harm than good. They are ineffective at improving security, do little to simplify the regulatory landscape, and are causing economic harms to the markets where they are imposed. In order to move away from these policies, the fear of sovereignty dilution must be addressed by alternative means. This will be achieved most effectively by focusing on both technical concerns and value concerns.

To address technical concerns, the US should:

1. Enact a federal national privacy law to reduce the fears that foreign nations have about the power of US tech companies.

2. Mandate privacy and security frameworks by industry to demonstrate the importance that US industry places on privacy and security, recognizing it as fundamental to their business success.

3. Increase investment in cybersecurity to ensure that in a competitive market, the US has the best offering in both customer experience and security assurance

4. Expand multi-lateral agreements under CLOUD Act to help alleviate the concerns that data stored by US companies will be inaccessible to foreign governments in relevant to a criminal investigation…(More)”

Federal Statistical Needs for a National Advanced Industry and Technology Strategy

Curated on Posted on by Stefaan Verhulst

Position paper by Robert D. Atkinson: “With the rise of China and other robust economic competitors, the United States needs a more coherent national advanced technology strategy.1 Effectively crafting and implementing such a strategy requires the right kind of economic data. In part because of years of budget cuts to federal economic data agencies, coupled with a long-standing disregard of the need for sectoral and firm-level economic data to inform an industrial strategy, the federal government is severely lacking in the kinds of data needed.

Notwithstanding the hundreds of millions of dollars spent every year and the thousands of economists working for the federal government, the exact nature of the challenges to U.S. capabilities with regard to the competitiveness of America’s traded sectors is only weakly understood. At least since after the Great Depression, the federal government has never felt the need to develop strategic economic intelligence in order to fully understand the competitive position of its traded sectors or to help support overall economic productivity.2 Rather, most of the focus goes to understanding the ups and downs of the business cycle….

If the U.S. government is going to develop more effective policies to spur competitiveness, growth, and opportunity it will need to support better data collection. It should be able to understand the U.S. competitive position vis-à-vis other nations on key technologies and industries, as well as key strengths and weaknesses and where specific policies are needed.

Better data can also identify weaknesses in U.S. competitiveness that policy can address. For example, in the 1980s, studies conducted as part of the Census of Manufactures (studies that have long been discontinued) found many smaller firms lagging behind badly in costs and quality for reasons including inefficient work organization and obsolete machinery and equipment. End-product manufacturers bought parts and components from many of these smaller enterprises at prices higher than those paid by foreign-based firms with more efficient suppliers, contributing to the cost and quality disadvantages of U.S.-based manufacturers. Legislators heeded the findings in crafting what is now called the Manufacturing Extension Partnership, a program that, if too small in scale to have a significant impact on U.S. manufacturing overall, continues to provide meaningful assistance to thousands of companies each year.5

Moreover, as the federal government institutes more technology and industry policies and programs—as exemplified in the Senate U.S. Innovation and Competition Act—better data will be important to evaluate their effectiveness.

Finally, data are a key 21st century infrastructure. In a decentralized economy, good outcomes are possible only if organizations make good decisions—and that requires data, which, because of its public goods nature, is a quintessential role of government….(More)”.

On regulation for data trusts

Curated on Posted on by Stefaan Verhulst

Paper by Aline Blankertz and Louisa Specht: “Data trusts are a promising concept for enabling data use while maintaining data privacy. Data trusts can pursue many goals, such as increasing the participation of consumers or other data subjects, putting data protection into practice more effectively, or strengthening data sharing along the value chain. They have the potential to become an alternative model to the large platforms, which are accused of accumulating data power and using it primarily for their own purposes rather than for the benefit of their users. To fulfill these hopes, data trusts must be trustworthy so that their users understand and trust that data is being used in their interest.

It is an important step that policymakers have recognized the potential of data trusts. This should be followed by measures that address specific risks and thus promote trust in the services. Currently, the political approach is to subject all forms of data trusts to the same rules through “one size fits all” regulation. This is the case, for example, with the Data Governance Act (DGA), which gives data trusts little leeway to evolve in the marketplace.

To encourage the development of data trusts, it makes sense to broadly define them as all organizations that manage data on behalf of others while adhering to a legal framework (including competition, trade secrets, and privacy). Which additional rules are necessary to ensure trustworthiness should be decided depending on the use case. The risk of a use case should be considered as well as the need for incentives to act as a data trust.

Risk factors can be identified across sectors; in particular, centralized or decentralized data storage and voluntary or mandatory use of data trusts are among them. The business model is not a main risk factor. Although many regulatory proposals call for strict neutrality, several data trusts without strict neutrality appear trustworthy in terms of monetization or vertical integration. At the same time, it is unclear what incentives exist for developing strictly neutral data trusts. Neutrality requirements that go beyond what is necessary make it less likely that desired alternative models will develop and take hold….(More)”.

Measuring What Matters for Child Well-being and Policies

Curated on Posted on by Stefaan Verhulst

Blog by Olivier Thévenon at the OECD: “Childhood is a critical period in which individuals develop many of the skills and abilities needed to thrive later in life. Promoting child well-being is not only an important end in itself, but is also essential for safeguarding the prosperity and sustainability of future generations. As the COVID-19 pandemic exacerbates existing challenges—and introduces new ones—for children’s material, physical, socio-emotional and cognitive development, improving child well-being should be a focal point of the recovery agenda.

To design effective child well-being policies, policy-makers need comprehensive and timely data that capture what is going on in children’s lives. Our new report, Measuring What Matters for Child Well-being and Policies, aims to move the child data agenda forward by laying the groundwork for better statistical infrastructures that will ultimately inform policy development. We identify key data gaps and outline a new aspirational measurement framework, pinpointing the aspects of children’s lives that should be assessed to monitor their well-being….(More)”.

Street Experiments

Curated on Posted on by Stefaan Verhulst

About: “City streets are increasingly becoming spaces for experimentation, for testing “in the wild” a seemingly unstoppable flow of “disruptive” mobility innovations such as mobility platforms for shared mobility and ride/hailing, electric and autonomous vehicles, micro-mobility solutions, etc. But also, and perhaps more radically, for recovering the primary function of city streets as public spaces, not just traffic channels.

City street experiments are:

​“intentional, temporary changes of the street use, regulation and/or form, aimed at exploring systemic change in urban mobility”

​They offer a prefiguration of what a radically different arrangement of the city´s mobility system and public space could look like and allow moving towards that vision by means of “learning by doing”.

The S.E.T. platform offers a collection of Resources for implementing and supporting street experiments. As well as a special section of COVID-19 devoted to the best practices of street experiments that offered solutions and strategies for cities to respond to the current pandemic and a SET Guidelines Kit that provides insights and considerations on creating impactful street experiments with long-term effects….(More)”.

Using big data for insights into the gender digital divide for girls: A discussion paper

Curated on Posted on by Stefaan Verhulst
Using big data for insights into the gender digital divide for girls: A discussion paper

UNICEF paper: “This discussion paper describes the findings of a study that used big data as an alternative data source to understand the gender digital divide for under-18s. It describes 6 key insights gained from analysing big data from Facebook and Instagram platforms, and discusses how big data can be further used to contribute to the body of evidence for the gender digital divide for adolescent girls….(More)”

ASEAN Data Management Framework

Curated on Posted on by Stefaan Verhulst

ASEAN Framework: “Due to the growing interactions between data, connected things and people, trust in data has become the pre-condition for fully realising the gains of digital transformation. SMEs are threading a fine line between balancing digital initiatives and concurrently managing data protection and customer privacy safeguards to ensure that these do not impede innovation. Therefore, there is a motivation to focus on digital data governance as it is critical to boost economic integration and technology adoption across all sectors in the ten ASEAN Member States (AMS).
To ensure that their data is appropriately managed and protected, organisations need to know what levels of technical, procedural and physical controls they need to put in place. The categorisation of datasets help organisations manage their data assets and put in place the right level of controls. This is applicable for both data at rest as well as data in transit. The establishment of an ASEAN Data Management Framework will promote sound data governance practices by helping organisations to discover the datasets they have, assign it with the appropriate categories, manage the data, protect it accordingly and all these while continuing to comply with relevant regulations. Improved governance and protection will instil trust in data sharing both between organisations and between countries, which will then promote the growth of trade and the flow of data among AMS and their partners in the digital economy….(More)”

Google launches new search tool to help combat food insecurity

Curated on Posted on by Stefaan Verhulst

Article by Andrew J. Hawkins: “Google announced a new website designed to be a “one-stop shop” for people with food insecurity. The “Find Food Support” site includes a food locator tool powered by Google Maps which people can use to search for their nearest food bank, food pantry, or school lunch program pickup site in their community.

Google is working with non-profit groups like No Kid Hungry and FoodFinder, as well as the US Department of Agriculture, to aggregate 90,000 locations with free food support across all 50 states — with more locations to come.

The new site is a product of Google’s newly formed Food for Good team, formerly known as Project Delta when it was headquartered at Alphabet’s X moonshot division. Project Delta’s mission is to “create a smarter food system,” which includes standardizing data to improve communication between food distributors to curb food waste….(More)”.

The ‘hidden data’ that could boost the UK’s productivity and job market

Curated on Posted on by Stefaan Verhulst

Report from Learning and Work Institute and Nesta (UK): “… highlights the complexities of labour market data used to support adults in their career planning…

The deficiencies in the UK’s labour market data are illustrated by the experiences of the winners of the CareerTech Challenge Prize, the team developing Bob UK, a tool designed to provide instant, online careers advice and job recommendations based on information about local vacancies and the jobseeker’s skills. The developers attempted to source UK data that directly replicated data sources used to develop the version of Bob which has helped over 250,000 jobseekers in France. However, it became apparent that equivalent sources of data rarely existed. The Bob UK team was able to work around this issue by carefully combining alternative sources of data from a number of UK and non-UK sources.

Many other innovators experienced similar barriers, finding that the publicly available data that could help people to make more informed decisions about their careers is often incomplete, difficult to use and poorly described. The impact of this is significant. A shocking insight from the report is that one solution enabled careers advisors to base course recommendations on labour market information for the first time. Prior to using this tool, such information was too time-consuming for careers advisors to uncover and analyse for it to be of use, and job seekers were given advice that was not based on employer demand for skills…To address this issue of hidden and missing data and unleash the productivity-raising potential of better skills matching, the report makes a series of recommendations, including:

  • The creation of a central labour market data repository that collates publicly available information about the labour market.
  • Public data providers should review the quality and accessibility of the data they hold, and make it easier for developers to use.

The development of better skills and labour market taxonomies to facilitate consistency between sources and enhance data matching…(More)”

Facial Recognition Technology: Federal Law Enforcement Agencies Should Better Assess Privacy and Other Risks

Curated on Posted on by Stefaan Verhulst

Report by the U.S. Government Accountability Office: “GAO surveyed 42 federal agencies that employ law enforcement officers about their use of facial recognition technology. Twenty reported owning systems with facial recognition technology or using systems owned by other entities, such as other federal, state, local, and non-government entities (see figure).

Ownership and Use of Facial Recognition Technology Reported by Federal Agencies that Employ Law Enforcement Officers

HLP_5 - 103705

Note: For more details, see figure 2 in GAO-21-518.

Agencies reported using the technology to support several activities (e.g., criminal investigations) and in response to COVID-19 (e.g., verify an individual’s identity remotely). Six agencies reported using the technology on images of the unrest, riots, or protests following the death of George Floyd in May 2020. Three agencies reported using it on images of the events at the U.S. Capitol on January 6, 2021. Agencies said the searches used images of suspected criminal activity.

All fourteen agencies that reported using the technology to support criminal investigations also reported using systems owned by non-federal entities. However, only one has awareness of what non-federal systems are used by employees. By having a mechanism to track what non-federal systems are used by employees and assessing related risks (e.g., privacy and accuracy-related risks), agencies can better mitigate risks to themselves and the public….GAO is making two recommendations to each of 13 federal agencies to implement a mechanism to track what non-federal systems are used by employees, and assess the risks of using these systems. Twelve agencies concurred with both recommendations. U.S. Postal Service concurred with one and partially concurred with the other. GAO continues to believe the recommendation is valid, as described in the report….(More)”.