Operationalizing Digital Self Determination


Paper by Stefaan G. Verhulst: “We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries, information asymmetries, and agency asymmetries. These asymmetries limit human potential, both in a practical and psychological sense, leading to feelings of disempowerment and eroding public trust in technology. Existing methods to limit asymmetries (e.g., consent) as well as some alternatives under consideration (data ownership, collective ownership, personal information management systems) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required.
DSD is based on existing concepts of self-determination, as articulated in sources as varied as Kantian philosophy and the 1966 International Covenant on Economic, Social and Cultural Rights. Updated for the digital age, DSD contains several key characteristics, including the fact that it has both an individual and collective dimension; is designed to especially benefit vulnerable and marginalized groups; and is context-specific (yet also enforceable). Operationalizing DSD in this (and other) contexts so as to maximize the potential of data while limiting its harms requires a number of steps. In particular, a responsible operationalization of DSD would consider four key prongs or categories of action: processes, people and organizations, policies, and products and technologies…(More)”.

China just announced a new social credit law. Here’s what it means.


Article by Zeyi Yang: “It’s easier to talk about what China’s social credit system isn’t than what it is. Ever since 2014, when China announced a six-year plan to build a system to reward actions that build trust in society and penalize the opposite, it has been one of the most misunderstood things about China in Western discourse. Now, with new documents released in mid-November, there’s an opportunity to correct the record.

For most people outside China, the words “social credit system” conjure up an instant image: a Black Mirror–esque web of technologies that automatically score all Chinese citizens according to what they did right and wrong. But the reality is, that terrifying system doesn’t exist, and the central government doesn’t seem to have much appetite to build it, either. 

Instead, the system that the central government has been slowly working on is a mix of attempts to regulate the financial credit industry, enable government agencies to share data with each other, and promote state-sanctioned moral values—however vague that last goal in particular sounds. There’s no evidence yet that this system has been abused for widespread social control (though it remains possible that it could be wielded to restrict individual rights). 

While local governments have been much more ambitious with their innovative regulations, causing more controversies and public pushback, the countrywide social credit system will still take a long time to materialize. And China is now closer than ever to defining what that system will look like. On November 14, several top government agencies collectively released a draft law on the Establishment of the Social Credit System, the first attempt to systematically codify past experiments on social credit and, theoretically, guide future implementation. 

Yet the draft law still left observers with more questions than answers. 

“This draft doesn’t reflect a major sea change at all,” says Jeremy Daum, a senior fellow of the Yale Law School Paul Tsai China Center who has been tracking China’s social credit experiment for years. It’s not a meaningful shift in strategy or objective, he says. 

Rather, the law stays close to local rules that Chinese cities like Shanghai have released and enforced in recent years on things like data collection and punishment methods—just giving them a stamp of central approval. It also doesn’t answer lingering questions that scholars have about the limitations of local rules. “This is largely incorporating what has been out there, to the point where it doesn’t really add a whole lot of value,” Daum adds. 

So what is China’s current system actually like? Do people really have social credit scores? Is there any truth to the image of artificial-intelligence-powered social control that dominates Western imagination? …(More)”.

A CERN Model for Studying the Information Environment


Article by Alicia Wanless: “After the Second World War, European science was suffering. Scientists were leaving Europe in pursuit of safety and work opportunities, among other reasons. To stem the exodus and unite the community around a vision of science for peace, in 1949, a transatlantic group of scholars proposed the creation of a world-class physics research facility in Europe. The grand vision was for this center to unlock the mysteries of the universe. Their white paper laid the foundation for the European Center for Nuclear Research (CERN), which today supports fundamental research in physics across an international community of more than 10,000 scientists from twenty-three member states and more than seventy other nations. Together, researchers at CERN built cutting-edge instruments to observe dozens of subatomic particles for the first time. And along the way they invented the World Wide Web, which was originally conceived as a tool to empower CERN’s distributed teams.

Such large-scale collaboration is once again needed to connect scholars, policymakers, and practitioners internationally and to accelerate research, this time to unlock the mysteries of the information environment. Democracies around the world are grappling with how to safeguard democratic values against online abuse, the proliferation of illiberal and xenophobic narratives, malign interference, and a host of other challenges related to a rapidly evolving information environment. What are the conditions within the information environment that can foster democratic societies and encourage active citizen participation? Sadly, the evidence needed to guide policymaking and social action in this domain is sorely lacking.

Researchers, governments, and civil society must come together to help. This paper explores how CERN can serve as a model for developing the Institute for Research on the Information Environment (IRIE). By connecting disciplines and providing shared engineering resources and capacity-building across the world’s democracies, IRIE will scale up applied research to enable evidence-based policymaking and implementation…(More)”.

OECD Good Practice Principles for Public Service Design and Delivery in the Digital Age


OECD Report: “The digital age provides great opportunities to transform how public services are designed and delivered. The OECD Good Practice Principles for Service Design and Delivery in the Digital Age provide a clear, actionable and comprehensive set of objectives for the high-quality digital transformation of public services. Reflecting insights gathered from across OECD member countries, these nine principles are arranged under three pillars of “Build accessible, ethical and equitable public services that prioritise user needs, rather than government needs”; “Deliver with impact, at scale and with pace”; and “Be accountable and transparent in the design and delivery of public services to reinforce and strengthen public trust”. The principles are advisory rather than prescriptive, allowing for local interpretation and implementation. They should also be considered in conjunction with wider OECD work to equip governments in harnessing the potential of digital technology and data to improve outcomes for all…(More)”.

Machine Learning in Public Policy: The Perils and the Promise of Interpretability


Report by Evan D. Peet, Brian G. Vegetabile, Matthew Cefalu, Joseph D. Pane, Cheryl L. Damberg: “Machine learning (ML) can have a significant impact on public policy by modeling complex relationships and augmenting human decisionmaking. However, overconfidence in results and incorrectly interpreted algorithms can lead to peril, such as the perpetuation of structural inequities. In this Perspective, the authors give an overview of ML and discuss the importance of its interpretability. In addition, they offer the following recommendations, which will help policymakers develop trustworthy, transparent, and accountable information that leads to more-objective and more-equitable policy decisions: (1) improve data through coordinated investments; (2) approach ML expecting interpretability, and be critical; and (3) leverage interpretable ML to understand policy values and predict policy impacts…(More)”.

A Data Capability Framework for the not-for-profit sector


Report by Anthony McCosker, Frances Shaw, Xiaofang Yao and Kath Albury: “As community services rapidly digitise, they are generating more data than ever before. These transformations are leading to innovation in data analysis and enthusiasm about the potential for data-driven decision making. However, increased use of personal data and automated systems raises ethical issues including gaining community trust, and introduces challenges in building knowledge, skills and capability.

Despite optimism across the not-for-profit (NFP) sector about the use of data analysis and automation to improve services and social impact, we are already seeing a growing data divide. Private sector companies have for some time invested heavily in data science and machine learning. However, many in the NFP sector are unsure how to meet the demands of these digital and data transformations. With limited resources, small, medium and large organisations alike face challenges in building their data capability and channelling it toward improved social outcomes. Working with marginalised clients, collecting sensitive personal information, and tackling seemingly intractable cycles of disadvantage, the sector needs a data capability revolution.

This short guide sets out a Data Capability Framework developed with and for the NFP sector and explains how it can be used to raise the bar in the use of data for impact and innovation. It conceptualises the core dimensions of data capability that need to be addressed. These dimensions can be tailored to meet an organisation’s specific strategic goals, impact and outcomes.

The Framework distils the challenges and successes of organisations we have worked with. It represents both the factors that underpin effective data capability and the pathways to achieving it. In other words, as technologies and data science techniques continue to change, data capability is both an outcome to aspire to, and a dynamic, ongoing process of experimentation and adaption…(More)”.

AI Audit-Washing and Accountability


Report by Ellen P. Goodman and Julia Tréhu: “.. finds that auditing could be a robust means for holding AI systems accountable, but today’s auditing regimes are not yet adequate to the job. The report assesses the effectiveness of various auditing regimes and proposes guidelines for creating trustworthy auditing systems.

Various government and private entities rely on or have proposed audits as a way of ensuring AI systems meet legal, ethical and other standards. This report finds that audits can in fact provide an agile co-regulatory approach—one that relies on both governments and private entities—to ensure societal accountability for algorithmic systems through private oversight.

But the “algorithmic audit” remains ill-defined and inexact, whether concerning social media platforms or AI systems generally. The risk is significant that inadequate audits will obscure problems with algorithmic systems. A poorly designed or executed audit is at best meaningless and at worst even excuses harms that the audits claim to mitigate.

Inadequate audits or those without clear standards provide false assurance of compliance with norms and laws, “audit-washing” problematic or illegal practices. Like green-washing and ethics-washing before, the audited entity can claim credit without doing the work.

The paper identifies the core specifications needed in order for algorithmic audits to be a reliable AI accountability mechanism:

  • Who” conducts the audit—clearly defined qualifications, conditions for data access, and guardrails for internal audits;
  • What” is the type and scope of audit—including its position within a larger sociotechnical system;
  • Why” is the audit being conducted—whether for narrow legal standards or broader ethical goals, essential for audit comparison, along with potential costs; and
  • How” are the audit standards determined—an important baseline for the development of audit certification mechanisms and to guard against audit-washing.

Algorithmic audits have the potential to increase the reliability and innovation of technology in the twenty-first century, much as financial audits transformed the way businesses operated in the twentieth century. They will take different forms, either within a sector or across sectors, especially for systems that pose the highest risk. Ensuring that AI is accountable and trusted is key to ensuring that democracies remain centers of innovation while shaping technology to democratic values…(More)”

We could run out of data to train AI language programs 


Article by Tammy Xu: “Large language models are one of the hottest areas of AI research right now, with companies racing to release programs like GPT-3 that can write impressively coherent articles and even computer code. But there’s a problem looming on the horizon, according to a team of AI forecasters: we might run out of data to train them on.

Language models are trained using texts from sources like Wikipedia, news articles, scientific papers, and books. In recent years, the trend has been to train these models on more and more data in the hope that it’ll make them more accurate and versatile.

The trouble is, the types of data typically used for training language models may be used up in the near future—as early as 2026, according to a paper by researchers from Epoch, an AI research and forecasting organization, that is yet to be peer reviewed. The issue stems from the fact that, as researchers build more powerful models with greater capabilities, they have to find ever more texts to train them on. Large language model researchers are increasingly concerned that they are going to run out of this sort of data, says Teven Le Scao, a researcher at AI company Hugging Face, who was not involved in Epoch’s work.

The issue stems partly from the fact that language AI researchers filter the data they use to train models into two categories: high quality and low quality. The line between the two categories can be fuzzy, says Pablo Villalobos, a staff researcher at Epoch and the lead author of the paper, but text from the former is viewed as better-written and is often produced by professional writers…(More)”.

New Interoperable Europe Act to deliver more efficient public services through improved cooperation between national administrations on data exchanges and IT solutions


Press Release: “The Commission has adopted the Interoperable Europe Act proposal and its accompanying Communication to strengthen cross-border interoperability and cooperation in the public sector across the EU. The Act will support the creation of a network of sovereign and interconnected digital public administrations and will accelerate the digital transformation of Europe’s public sector. It will help the EU and its Member States to deliver better public services to citizens and businesses, and as such, it is an essential step to achieve Europe’s digital targets for 2030 and support trusted data flows. It will also help save costs, and cross-border interoperability can lead to cost-savings between €5.5 and €6.3 million for citizens and between €5.7 and €19.2 billion for businesses dealing with public administrations…

The Interoperable Europe Act introduces:

  • A structured EU cooperation where public administrations, supported by public and private actors, come together in the framework of projects co-owned by Member States, as well as regions and cities.
  • Mandatory assessments to evaluate the impact of changes in information technology (IT) systems on cross-border interoperability in the EU.
  • The sharing and reuse of solutions, often open source, powered by an ‘Interoperable Europe Portal’ – a one-stop-shop for solutions and community cooperation.
  • Innovation and support measures, including regulatory sandboxes for policy experimentation, GovTech projects to develop and scale up solutions for reuse, and training support…(More)”.

People watching: Abstractions and orthodoxies of monitoring


Paper by Victoria Wang and John V.Tucker: “Our society has an insatiable appetite for data. Much of the data is collected to monitor the activities of people, e.g., for discovering the purchasing behaviour of customers, observing the users of apps, managing the performance of personnel, and conforming to regulations and laws, etc. Although monitoring practices are ubiquitous, monitoring as a general concept has received little analytical attention. We explore: (i) the nature of monitoring facilitated by software; (ii) the structure of monitoring processes; and (iii) the classification of monitoring systems. We propose an abstract definition of monitoring as a theoretical tool to analyse, document, and compare disparate monitoring applications. For us, monitoring is simply the systematic collection of data about the behaviour of people and objects. We then extend this concept with mechanisms for detecting events that require interventions and changes in behaviour, and describe five types of monitoring…(More)”.