The Government Analytics Handbook

Curated on Posted on by Stefaan Verhulst

(Open Access) Book edited by Daniel Rogger and Christian Schuster: “Governments across the world make thousands of personnel management decisions, procure millions of goods and services, and execute billions of processes each day. They are data rich.  And yet, there is little systematic practice to-date which capitalizes on this data to make public administrations work better. This means that governments are missing out on data insights to save billions in procurement expenditures, recruit better talent into government, and identify sources of corruption, to name just a few.

The Government Analytics Handbook seeks to change that. It presents frontier evidence and practitioner insights on how to leverage data to make governments work better. Covering a range of microdata sources—such as administrative data and public servant surveys—as well as tools and resources for undertaking the analytics, it transforms the ability of governments to take a data-informed approach to diagnose and improve how public organizations work…(More)”.

AI in public services will require empathy, accountability

Curated on Posted on by Stefaan Verhulst

Article by Yogesh Hirdaramani: “The Australian Government Department of the Prime Minister and Cabinet has released the first of its Long Term Insights Briefing, which focuses on how the Government can integrate artificial intelligence (AI) into public services while maintaining the trustworthiness of public service delivery.

Public servants need to remain accountable and transparent with their use of AI, continue to demonstrate empathy for the people they serve, use AI to better meet people’s needs, and build AI literacy amongst the Australian public, the report stated.

The report also cited a forthcoming study that found that Australian residents with a deeper understanding of AI are more likely to trust the Government’s use of AI in service delivery. However,more than half of survey respondents reported having little knowledge of AI.

Key takeaways

The report aims to supplement current policy work on how AI can be best governed in the public service to realise its benefits while maintaining public trust.

In the longer term, the Australian Government aims to use AI to deliver personalised services to its citizens, deliver services more efficiently and conveniently, and achieve a higher standard of care for its ageing population.

AI can help public servants achieve these goals through automating processes, improving service processing and response time, and providing AI-enabled interfaces which users can engage with, such as chatbots and virtual assistants.

However, AI can also lead to unfair or unintended outcomes due to bias in training data or hallucinations, the report noted.

According to the report, the trustworthy use of AI will require public servants to:

  1. Demonstrate integrity by remaining accountable for AI outcomes and transparent about AI use
  2. Demonstrate empathy by offering face-to-face services for those with greater vulnerabilities 
  3. Use AI in ways that improve service delivery for end-users
  4. Build internal skills and systems to implement AI, while educating the public on the impact of AI

The Australian Taxation Office currently uses AI to identify high-risk business activity statements to determine whether refunds can be issued or if further review is required, noted the report. Taxpayers can appeal the decision if staff decide to deny refunds…(More)”

The Tragedy of AI Governance

Curated on Posted on by Stefaan Verhulst

Paper by Simon Chesterman: “Despite hundreds of guides, frameworks, and principles intended to make AI “ethical” or “responsible”, ever more powerful applications continue to be released ever more quickly. Safety and security teams are being downsized or sidelined to bring AI products to market. And a significant portion of AI developers apparently believe there is a real risk that their work poses an existential threat to humanity.

This contradiction between statements and action can be attributed to three factors that undermine the prospects for meaningful governance of AI. The first is the shift of power from public to private hands, not only in deployment of AI products but in fundamental research. The second is the wariness of most states about regulating the sector too aggressively, for fear that it might drive innovation elsewhere. The third is the dysfunction of global processes to manage collective action problems, epitomized by the climate crisis and now frustrating efforts to govern a technology that does not respect borders. The tragedy of AI governance is that those with the greatest leverage to regulate AI have the least interest in doing so, while those with the greatest interest have the least leverage.

Resolving these challenges either requires rethinking the incentive structures — or waiting for a crisis that brings the need for regulation and coordination into sharper focus…(More)”

Open-access reformers launch next bold publishing plan

Curated on Posted on by Stefaan Verhulst

Article by Layal Liverpool: “The group behind the radical open-access initiative Plan S has announced its next big plan to shake up research publishing — and this one could be bolder than the first. It wants all versions of an article and its associated peer-review reports to be published openly from the outset, without authors paying any fees, and for authors, rather than publishers, to decide when and where to first publish their work.

The group of influential funding agencies, called cOAlition S, has over the past five years already caused upheaval in the scholarly publishing world by pressuring more journals to allow immediate open-access publishing. Its new proposal, prepared by a working group of publishing specialists and released on 31 October, puts forward an even broader transformation in the dissemination of research.

It outlines a future “community-based” and “scholar-led” open-research communication system (see go.nature.com/45zyjh) in which publishers are no longer gatekeepers that reject submitted work or determine first publication dates. Instead, authors would decide when and where to publish the initial accounts of their findings, both before and after peer review. Publishers would become service providers, paid to conduct processes such as copy-editing, typesetting and handling manuscript submissions…(More)”.

Your Face Belongs to Us

Curated on Posted on by Stefaan Verhulst

Book by Kashmir Hill: “… was skeptical when she got a tip about a mysterious app called Clearview AI that claimed it could, with 99 percent accuracy, identify anyone based on just one snapshot of their face. The app could supposedly scan a face and, in just seconds, surface every detail of a person’s online life: their name, social media profiles, friends and family members, home address, and photos that they might not have even known existed. If it was everything it claimed to be, it would be the ultimate surveillance tool, and it would open the door to everything from stalking to totalitarian state control. Could it be true?

In this riveting account, Hill tracks the improbable rise of Clearview AI, helmed by Hoan Ton-That, an Australian computer engineer, and Richard Schwartz, a former Rudy Giuliani advisor, and its astounding collection of billions of faces from the internet. The company was boosted by a cast of controversial characters, including conservative provocateur Charles C. Johnson and billionaire Donald Trump backer Peter Thiel—who all seemed eager to release this society-altering technology on the public. Google and Facebook decided that a tool to identify strangers was too radical to release, but Clearview forged ahead, sharing the app with private investors, pitching it to businesses, and offering it to thousands of law enforcement agencies around the world.
      
Facial recognition technology has been quietly growing more powerful for decades. This technology has already been used in wrongful arrests in the United States. Unregulated, it could expand the reach of policing, as it has in China and Russia, to a terrifying, dystopian level.
     
Your Face Belongs to Us is a gripping true story about the rise of a technological superpower and an urgent warning that, in the absence of vigilance and government regulation, Clearview AI is one of many new technologies that challenge what Supreme Court Justice Louis Brandeis once called “the right to be let alone.”…(More)”.

Choosing AI’s Impact on the Future of Work 

Curated on Posted on by Stefaan Verhulst

Article by Daron Acemoglu & Simon Johnson  …“Too many commentators see the path of technology as inevitable. But the historical record is clear: technologies develop according to the vision and choices of those in positions of power. As we document in Power and Progress: Our 1,000-Year Struggle over Technology and Prosperity, when these choices are left entirely in the hands of a small elite, you should expect that group to receive most of the benefits, while everyone else bears the costs—potentially for a long time.

Rapid advances in AI threaten to eliminate many jobs, and not just those of writers and actors. Jobs with routine elements, such as in regulatory compliance or clerical work, and those that involve simple data collection, data summary, and writing tasks are likely to disappear.

But there are still two distinct paths that this AI revolution could take. One is the path of automation, based on the idea that AI’s role is to perform tasks as well as or better than people. Currently, this vision dominates in the US tech sector, where Microsoft and Google (and their ecosystems) are cranking hard to create new AI applications that can take over as many human tasks as possible.

The negative impact on people along the “just automate” path is easy to predict from prior waves of digital technologies and robotics. It was these earlier forms of automation that contributed to the decline of American manufacturing employment and the huge increase in inequality over the last four decades. If AI intensifies automation, we are very likely to get more of the same—a gap between capital and labor, more inequality between the professional class and the rest of the workers, and fewer good jobs in the economy….(More)”

Automating Empathy 

Curated on Posted on by Stefaan Verhulst

Open Access Book by Andrew McStay: “We live in a world where artificial intelligence and intensive use of personal data has become normalized. Companies across the world are developing and launching technologies to infer and interact with emotions, mental states, and human conditions. However, the methods and means of mediating information about people and their emotional states are incomplete and problematic.

Automating Empathy offers a critical exploration of technologies that sense intimate dimensions of human life and the modern ethical questions raised by attempts to perform and simulate empathy. It traces the ascendance of empathic technologies from their origins in physiognomy and pathognomy to the modern day and explores technologies in nations with non-Western ethical histories and approaches to emotion, such as Japan. The book examines applications of empathic technologies across sectors such as education, policing, and transportation, and considers key questions of everyday use such as the integration of human-state sensing in mixed reality, the use of neurotechnologies, and the moral limits of using data gleaned through automated empathy. Ultimately, Automating Empathy outlines the key principles necessary to usher in a future where automated empathy can serve and do good…(More)”

A standardised differential privacy framework for epidemiological modeling with mobile phone data

Curated on Posted on by Stefaan Verhulst

Paper by Merveille Koissi Savi et al: “During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework…(More)”.

Data Equity: Foundational Concepts for Generative AI

Curated on Posted on by Stefaan Verhulst

WEF Report: “This briefing paper focuses on data equity within foundation models, both in terms of the impact of Generative AI (genAI) on society and on the further development of genAI tools.

GenAI promises immense potential to drive digital and social innovation, such as improving efficiency, enhancing creativity and augmenting existing data. GenAI has the potential to democratize access and usage of technologies. However, left unchecked, it could deepen inequities. With the advent of genAI significantly increasing the rate at which AI is deployed and developed, exploring frameworks for data equity is more urgent than ever.

The goals of the briefing paper are threefold: to establish a shared vocabulary to facilitate collaboration and dialogue; to scope initial concerns to establish a framework for inquiry on which stakeholders can focus; and to shape future development of promising technologies.

The paper represents a first step in exploring and promoting data equity in the context of genAI. The proposed definitions, framework and recommendations are intended to proactively shape the development of promising genAI technologies…(More)”.

Artificial intelligence in government: Concepts, standards, and a unified framework

Curated on Posted on by Stefaan Verhulst

Paper by Vincent J. Straub, Deborah Morgan, Jonathan Bright, Helen Margetts: “Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI…(More)”.