Open data: The building block of 21st century (open) science


Paper by Corina Pascu and Jean-Claude Burgelman: “Given this irreversibility of data driven and reproducible science and the role machines will play in that, it is foreseeable that the production of scientific knowledge will be more like a constant flow of updated data driven outputs, rather than a unique publication/article of some sort. Indeed, the future of scholarly publishing will be more based on the publication of data/insights with the article as a narrative.

For open data to be valuable, reproducibility is a sine qua non (King2011; Piwowar, Vision and Whitlock2011) and—equally important as most of the societal grand challenges require several sciences to work together—essential for interdisciplinarity.

This trend correlates with the already ongoing observed epistemic shift in the rationale of science: from demonstrating the absolute truth via a unique narrative (article or publication), to the best possible understanding what at that moment is needed to move forward in the production of knowledge to address problem “X” (de Regt2017).

Science in the 21st century will be thus be more “liquid,” enabled by open science and data practices and supported or even co-produced by artificial intelligence (AI) tools and services, and thus a continuous flow of knowledge produced and used by (mainly) machines and people. In this paradigm, an article will be the “atomic” entity and often the least important output of the knowledge stream and scholarship production. Publishing will offer in the first place a platform where all parts of the knowledge stream will be made available as such via peer review.

The new frontier in open science as well as where most of future revenue will be made, will be via value added data services (such as mining, intelligence, and networking) for people and machines. The use of AI is on the rise in society, but also on all aspects of research and science: what can be put in an algorithm will be put; the machines and deep learning add factor “X.”

AI services for science 4 are already being made along the research process: data discovery and analysis and knowledge extraction out of research artefacts are accelerated with the use of AI. AI technologies also help to maximize the efficiency of the publishing process and make peer-review more objective5 (Table 1).

Table 1. Examples of AI services for science already being developed

Abbreviation: AI, artificial intelligence.

Source: Authors’ research based on public sources, 2021.

Ultimately, actionable knowledge and translation of its benefits to society will be handled by humans in the “machine era” for decades to come. But as computers are indispensable research assistants, we need to make what we publish understandable to them.

The availability of data that are “FAIR by design” and shared Application Programming Interfaces (APIs) will allow new ways of collaboration between scientists and machines to make the best use of research digital objects of any kind. The more findable, accessible, interoperable, and reusable (FAIR) data resources will become available, the more it will be possible to use AI to extract and analyze new valuable information. The main challenge is to master the interoperability and quality of research data…(More)”.

We can’t create shared value without data. Here’s why


Article by Kriss Deiglmeier: “In 2011, I was co-teaching a course on Corporate Social Innovation at the Stanford Graduate School of Business, when our syllabus nearly went astray. A paper appeared in Harvard Business Review (HBR), titled “Creating Shared Value,” by Michael E. Porter and Mark R. Kramer. The students’ excitement was palpable: This could transform capitalism, enabling Adam Smith’s “invisible hand” to bend the arc of history toward not just efficiency and profit, but toward social impact…

History shows that the promise of shared value hasn’t exactly been realized. In the past decade, most indexes of inequality, health, and climate change have gotten worse, not better. The gap in wealth equality has widened – the combined worth of the top 1% in the United States increased from 29% of all wealth in 2011 to 32.3% in 2021 and the bottom 50% increased their share from 0.4% to 2.6% of overall wealth; everyone in between saw their share of wealth decline. The federal minimum wage has remained stagnant at $7.25 per hour while the US dollar has seen a cumulative price increase of 27.81%

That said, data is by no means the only – or even primary – obstacle to achieving shared value, but the role of data is a key aspect that needs to change. In a shared value construct, data is used primarily for profit and not the societal benefit at the speed and scale required.

Unfortunately, the technology transformation has resulted in an emerging data divide. While data strategies have benefited the commercial sector, the public sector and nonprofits lag in education, tools, resources, and talent to use data in finding and scaling solutions. The result is the disparity between the expanding use of data to create commercial value, and the comparatively weak use of data to solve social and environmental challenges…

Data is part of our future and is being used by corporations to drive success, as they should. Bringing data into the shared value framework is about ensuring that other entities and organizations also have the access and tools to harness data for solving social and environmental challenges as well….

Business has the opportunity to help solve the data divide through a shared value framework by bringing talent, product and resources to bear beyond corporate boundaries to help solve our social and environmental challenges. To succeed, it’s essential to re-envision the shared value framework to ensure data is at the core to collectively solve these challenges for everyone. This will require a strong commitment to collaboration between business, government and NGOs – and it will undoubtedly require a dedication to increasing data literacy at all levels of education….(More)”.

Facebook-owner Meta to share more political ad targeting data


Article by Elizabeth Culliford: “Facebook owner Meta Platforms Inc (FB.O) will share more data on targeting choices made by advertisers running political and social-issue ads in its public ad database, it said on Monday.

Meta said it would also include detailed targeting information for these individual ads in its “Facebook Open Research and Transparency” database used by academic researchers, in an expansion of a pilot launched last year.

“Instead of analyzing how an ad was delivered by Facebook, it’s really going and looking at an advertiser strategy for what they were trying to do,” said Jeff King, Meta’s vice president of business integrity, in a phone interview.

The social media giant has faced pressure in recent years to provide transparency around targeted advertising on its platforms, particularly around elections. In 2018, it launched a public ad library, though some researchers criticized it for glitches and a lack of detailed targeting data.Meta said the ad library will soon show a summary of targeting information for social issue, electoral or political ads run by a page….The company has run various programs with external researchers as part of its transparency efforts. Last year, it said a technical error meant flawed data had been provided to academics in its “Social Science One” project…(More)”.

The Era of Borderless Data Is Ending


David McCabe and Adam Satariano at the New York Times: “Every time we send an email, tap an Instagram ad or swipe our credit cards, we create a piece of digital data.

The information pings around the world at the speed of a click, becoming a kind of borderless currency that underpins the digital economy. Largely unregulated, the flow of bits and bytes helped fuel the rise of transnational megacompanies like Google and Amazon and reshaped global communications, commerce, entertainment and media.

Now the era of open borders for data is ending.

France, Austria, South Africa and more than 50 other countries are accelerating efforts to control the digital information produced by their citizens, government agencies and corporations. Driven by security and privacy concerns, as well as economic interests and authoritarian and nationalistic urges, governments are increasingly setting rules and standards about how data can and cannot move around the globe. The goal is to gain “digital sovereignty.”

Consider that:

  • In Washington, the Biden administration is circulating an early draft of an executive order meant to stop rivals like China from gaining access to American data.
  • In the European Union, judges and policymakers are pushing efforts to guard information generated within the 27-nation bloc, including tougher online privacy requirements and rules for artificial intelligence.
  • In India, lawmakers are moving to pass a law that would limit what data could leave the nation of almost 1.4 billion people.
  • The number of laws, regulations and government policies that require digital information to be stored in a specific country more than doubled to 144 from 2017 to 2021, according to the Information Technology and Innovation Foundation.

While countries like China have long cordoned off their digital ecosystems, the imposition of more national rules on information flows is a fundamental shift in the democratic world and alters how the internet has operated since it became widely commercialized in the 1990s.

The repercussions for business operations, privacy and how law enforcement and intelligence agencies investigate crimes and run surveillance programs are far-reaching. Microsoft, Amazon and Google are offering new services to let companies store records and information within a certain territory. And the movement of data has become part of geopolitical negotiations, including a new pact for sharing information across the Atlantic that was agreed to in principle in March…(More)”.

Regulatory Insights on Artificial Intelligence


Book edited by Mark Findlay, Jolyon Ford, Josephine Seah, and Dilan Thampapillai: “This provocative book investigates the relationship between law and artificial intelligence (AI) governance, and the need for new and innovative approaches to regulating AI and big data in ways that go beyond market concerns alone and look to sustainability and social good.
 
Taking a multidisciplinary approach, the contributors demonstrate the interplay between various research methods, and policy motivations, to show that law-based regulation and governance of AI is vital to efforts at ensuring justice, trust in administrative and contractual processes, and inclusive social cohesion in our increasingly technologically-driven societies. The book provides valuable insights on the new challenges posed by a rapid reliance on AI and big data, from data protection regimes around sensitive personal data, to blockchain and smart contracts, platform data reuse, IP rights and limitations, and many other crucial concerns for law’s interventions. The book also engages with concerns about the ‘surveillance society’, for example regarding contact tracing technology used during the Covid-19 pandemic.
 
The analytical approach provided will make this an excellent resource for scholars and educators, legal practitioners (from constitutional law to contract law) and policy makers within regulation and governance. The empirical case studies will also be of great interest to scholars of technology law and public policy. The regulatory community will find this collection offers an influential case for law’s relevance in giving institutional enforceability to ethics and principled design…(More)”.

Artificial intelligence is breaking patent law


Article by Alexandra George & Toby Walsh: “In 2020, a machine-learning algorithm helped researchers to develop a potent antibiotic that works against many pathogens (see Nature https://doi.org/ggm2p4; 2020). Artificial intelligence (AI) is also being used to aid vaccine development, drug design, materials discovery, space technology and ship design. Within a few years, numerous inventions could involve AI. This is creating one of the biggest threats patent systems have faced.

Patent law is based on the assumption that inventors are human; it currently struggles to deal with an inventor that is a machine. Courts around the world are wrestling with this problem now as patent applications naming an AI system as the inventor have been lodged in more than 100 countries1. Several groups are conducting public consultations on AI and intellectual property (IP) law, including in the United States, United Kingdom and Europe.

If courts and governments decide that AI-made inventions cannot be patented, the implications could be huge. Funders and businesses would be less incentivized to pursue useful research using AI inventors when a return on their investment could be limited. Society could miss out on the development of worthwhile and life-saving inventions.

Rather than forcing old patent laws to accommodate new technology, we propose that national governments design bespoke IP law — AI-IP — that protects AI-generated inventions. Nations should also create an international treaty to ensure that these laws follow standardized principles, and that any disputes can be resolved efficiently. Researchers need to inform both steps….(More)”.

Beyond Data: Reclaiming Human Rights at the Dawn of the Metaverse


Book by Elizabeth M. Renieris: “Ever-pervasive technology poses a clear and present danger to human dignity and autonomy, as many have pointed out. And yet, for the past fifty years, we have been so busy protecting data that we have failed to protect people. In Beyond Data, Elizabeth Renieris argues that laws focused on data protection, data privacy, data security and data ownership have unintentionally failed to protect core human values, including privacy. And, as our collective obsession with data has grown, we have, to our peril, lost sight of what’s truly at stake in relation to technological development—our dignity and autonomy as people.

Far from being inevitable, our fixation on data has been codified through decades of flawed policy. Renieris provides a comprehensive history of how both laws and corporate policies enacted in the name of data privacy have been fundamentally incapable of protecting humans. Her research identifies the inherent deficiency of making data a rallying point in itself—data is not an objective truth, and what’s more, its “entirely contextual and dynamic” status makes it an unstable foundation for organizing. In proposing a human rights–based framework that would center human dignity and autonomy rather than technological abstractions, Renieris delivers a clear-eyed and radically imaginative vision of the future.

At once a thorough application of legal theory to technology and a rousing call to action, Beyond Data boldly reaffirms the value of human dignity and autonomy amid widespread disregard by private enterprise at the dawn of the metaverse….(More)”.

We Need to Take Back Our Privacy


Zeynep Tufekci in The New York Times: “…Congress, and states, should restrict or ban the collection of many types of data, especially those used solely for tracking, and limit how long data can be retained for necessary functions — like getting directions on a phone.

Selling, trading and merging personal data should be restricted or outlawed. Law enforcement could obtain it subject to specific judicial oversight.

Researchers have been inventing privacy-preserving methods for analyzing data sets when merging them is in the public interest but the underlying data is sensitive — as when health officials are tracking a disease outbreak and want to merge data from multiple hospitals. These techniques allow computation but make it hard, if not impossible, to identify individual records. Companies are unlikely to invest in such methods, or use end-to-end encryption as appropriate to protect user data, if they could continue doing whatever they want. Regulation could make these advancements good business opportunities, and spur innovation.

I don’t think people like things the way they are. When Apple changed a default option from “track me” to “do not track me” on its phones, few people chose to be tracked. And many who accept tracking probably don’t realize how much privacy they’re giving up, and what this kind of data can reveal. Many location collectors get their data from ordinary apps — could be weather, games, or anything else — that often bury that they will share the data with others in vague terms deep in their fine print.

Under these conditions, requiring people to click “I accept” to lengthy legalese for access to functions that have become integral to modern life is a masquerade, not informed consent.

Many politicians have been reluctant to act. The tech industry is generous, cozy with power, and politicians themselves use data analysis for their campaigns. This is all the more reason to press them to move forward…(More)”.

Everyday Data Cultures


Book by Jean Burgess, Kath Albury, Anthony McCosker, and Rowan Wilken: “The AI revolution can seem powerful and unstoppable, extracting data from every aspect of our lives and subjecting us to unprecedented surveillance and control. But at ground level, even the most advanced ‘smart’ technologies are not as all-powerful as either the tech companies or their critics would have us believe.

From gig worker activism to wellness tracking with sex toys and TikTokers’ manipulation of the algorithm, this book shows how ordinary people are negotiating the datafication of society. The book establishes a new theoretical framework for understanding everyday experiences of data and automation, and offers guidance on the ethical responsibilities we share as we learn to live together with data-driven machines…(More)”.

State of Open Data Policy Repository


The GovLab: “To accompany its State of Open Data Policy Summit, the Open Data Policy Lab announced the release of a new resource to assess recent policy developments surrounding open data, data reuse, and data collaboration around the world: State of Open Data Repository of Recent Developments.

This document examines recent legislation, directives, and proposals that affect open data and data collaboration. Its goal is to capture signals of concerns, direction and leadership as to determine what stakeholders may focus on in the future. The review currently surfaced approximately 50 examples of recent legislative acts, proposals, directives, and other policy documents, from which the Open Data Policy Lab draws findings about the need to promote more innovative policy frameworks.

This collection demonstrates that, while there is growing interest in open data and data collaboration, policy development still remains nascent and focused on open data repositories at the expense of other collaborative arrangements. As we indicated in our report on the Third Wave of Open Data, there is an urgent need for governance frameworks at the local, regional, and national level to facilitate responsible reuse…(More)”.