Handbook of Artificial Intelligence at Work

Curated on Posted on by Stefaan Verhulst

Book edited by Martha Garcia-Murillo and Andrea Renda: “With the advancement in processing power and storage now enabling algorithms to expand their capabilities beyond their initial narrow applications, technology is becoming increasingly powerful. This highly topical Handbook provides a comprehensive overview of the impact of Artificial Intelligence (AI) on work, assessing its effect on an array of economic sectors, the resulting nature of work, and the subsequent policy implications of these changes.

Featuring contributions from leading experts across diverse fields, the Handbook of Artificial Intelligence at Work takes an interdisciplinary approach to understanding AI’s connections to existing economic, social, and political ecosystems. Considering a range of fields including agriculture, manufacturing, health care, education, law and government, the Handbook provides detailed sector-specific analyses of how AI is changing the nature of work, the challenges it presents and the opportunities it creates. Looking forward, it makes policy recommendations to address concerns, such as the potential displacement of some human labor by AI and growth in inequality affecting those lacking the necessary skills to interact with these technologies or without opportunities to do so.

This vital Handbook is an essential read for students and academics in the fields of business and management, information technology, AI, and public policy. It will also be highly informative from a cross-disciplinary perspective for practitioners, as well as policy makers with an interest in the development of AI technology…(More)”

Data Science, AI and Data Philanthropy in Foundations : On the Path to Maturity

Curated on Posted on by Stefaan Verhulst

Report by Filippo Candela, Sevda Kilicalp, and Daniel Spiers: “This research explores the data-related initiatives currently undertaken by a pool of foundations from across Europe. To the authors’ knowledge, this is the first study that has investigated the level of data work within philanthropic foundations, even though the rise of data and its importance has increasingly been recognised in the non-profit sector. Given that this is an inaugural piece of research, the study takes an exploratory approach, prioritising a comprehensive survey of data practices foundations are currently implementing or exploring. The goal was to obtain a snapshot of the current level of maturity and commitment of foundations regarding data-related matters…(More)”

Language Machinery

Curated on Posted on by Stefaan Verhulst

Essay by Richard Hughes Gibson: “… current debates about writing machines are not as fresh as they seem. As is quietly acknowledged in the footnotes of scientific papers, much of the intellectual infrastructure of today’s advances was laid decades ago. In the 1940s, the mathematician Claude Shannon demonstrated that language use could be both described by statistics and imitated with statistics, whether those statistics were in human heads or a machine’s memory. Shannon, in other words, was the first statistical language modeler, which makes ChatGPT and its ilk his distant brainchildren. Shannon never tried to build such a machine, but some astute early readers of his work recognized that computers were primed to translate his paper-and-ink experiments into a powerful new medium. In writings now discussed largely in niche scholarly and computing circles, these readers imagined—and even made preliminary sketches of—machines that would translate Shannon’s proposals into reality. These readers likewise raised questions about the meaning of such machines’ outputs and wondered what the machines revealed about our capacity to write.

The current barrage of commentary has largely neglected this backstory, and our discussions suffer for forgetting that issues that appear novel to us belong to the mid-twentieth century. Shannon and his first readers were the original residents of the headspace in which so many of us now find ourselves. Their ambitions and insights have left traces on our discourse, just as their silences and uncertainties haunt our exchanges. If writing machines constitute a “philosophical event” or a “prompt for philosophizing,” then I submit that we are already living in the event’s aftermath, which is to say, in Shannon’s aftermath. Amid the rampant speculation about a future dominated by writing machines, I propose that we turn in the other direction to listen to field reports from some of the first people to consider what it meant to read and write in Shannon’s world…(More)”.

Copyright Policy Options for Generative Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Joshua S. Gans: “New generative artificial intelligence (AI) models, including large language models and image generators, have created new challenges for copyright policy as such models may be trained on data that includes copy-protected content. This paper examines this issue from an economics perspective and analyses how different copyright regimes for generative AI will impact the quality of content generated as well as the quality of AI training. A key factor is whether generative AI models are small (with content providers capable of negotiations with AI providers) or large (where negotiations are prohibitive). For small AI models, it is found that giving original content providers copyright protection leads to superior social welfare outcomes compared to having no copyright protection. For large AI models, this comparison is ambiguous and depends on the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex-post `fair use’ type mechanism can lead to higher expected social welfare than traditional copyright regimes…(More)”.

How Health Data Integrity Can Earn Trust and Advance Health

Curated on Posted on by Stefaan Verhulst

Article by Jochen Lennerz, Nick Schneider and Karl Lauterbach: “Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.

Imagine a scenario in which Mary, an individual with a rare disease, has agreed to share her medical records for a research project aimed at finding better treatments for genetic disorders. Mary’s consent is grounded in trust that her data will be handled with the utmost care, protected from unauthorized access, and used according to her wishes. 

It may sound simple, but meeting these standards comes with myriad complications. Whose job is it to weigh the risk that Mary might be reidentified, even if her information is de-identified and stored securely? How should that assessment be done? How can data from Mary’s records be aggregated with patients from health systems in other countries, each with their own requirements for data protection and formats for record keeping? How can Mary’s wishes be respected, both in terms of what research is conducted and in returning relevant results to her?

From electronic medical records to genomic sequencing, health care providers and researchers now have an unprecedented wealth of information that could help tailor treatments to individual needs, revolutionize understanding of disease, and enhance the overall quality of health care. Data protection, privacy safeguards, and cybersecurity are all paramount for safeguarding sensitive medical information, but much of the potential that lies in this abundance of data is being lost because well-intentioned regulations have not been set up to allow for data sharing and collaboration. This stymies efforts to study rare diseases, map disease patterns, improve public health surveillance, and advance evidence-based policymaking (for instance, by comparing effectiveness of interventions across regions and demographics). Projects that could excel with enough data get bogged down in bureaucracy and uncertainty. For example, Germany now has strict data protection laws—with heavy punishment for violations—that should allow de-identified health insurance claims to be used for research within secure processing environments, but the legality of such use has been challenged…(More)”.

Data and density: Two tools to boost health equity in cities

Curated on Posted on by Stefaan Verhulst

Article by Ann Aerts and Diana Rodríguez Franco: “Improving health and health equity for vulnerable populations requires addressing the social determinants of health. In the US, it is estimated that medical care only accounts for 10-20% of health outcomes while social determinants like education and income account for the remaining 80-90%.

Place-based interventions, however, are showing promise for improving health outcomes despite persistent inequalities. Research and practice increasingly point to the role of cities in promoting health equity — or reversing health inequities — as 56% of the global population lives in cities, and several social determinants of health are directly tied to urban factors like opportunity, environmental health, neighbourhoods and physical environments, access to food and more.

Thus, it is critical to identify both true drivers of good health and poor health outcomes so that underserved populations can be better served.

Place-based strategies can address health inequities and lead to meaningful improvements for vulnerable populations…

Initial data analysis revealed a strong correlation between cardiovascular disease risk in city residents and social determinants such as higher education, commuting time, access to Medicaid, rental costs and internet access.

Understanding which data points are correlated with health risks is key to effectively tailoring interventions.

Determined to reverse this trend, city authorities have launched a “HealthyNYC” campaign and are working with the Novartis Foundation to uncover the behavioural and social determinants behind non-communicable diseases (NCDs) (e.g. diabetes and cardiovascular disease), which cause 87% of all deaths in New York City…(More)”

Computing Power and the Governance of AI

Curated on Posted on by Stefaan Verhulst

Blog by Lennart Heim, Markus Anderljung, Emma Bluemke, and Robert Trager: “Computing power – compute for short – is a key driver of AI progress. Over the past thirteen years, the amount of compute used to train leading AI systems has increased by a factor of 350 million. This has enabled the major AI advances that have recently gained global attention.

‍Governments have taken notice. They are increasingly engaged in compute governance: using compute as a lever to pursue AI policy goals, such as limiting misuse risks, supporting domestic industries, or engaging in geopolitical competition. 

‍There are at least three ways compute can be used to govern AI. Governments can: 

  • Track or monitor compute to gain visibility into AI development and use
  • Subsidize or limit access to compute to shape the allocation of resources across AI projects
  • Monitor activity, limit access, or build “guardrails” into hardware to enforce rules

Compute governance is a particularly important approach to AI governance because it is feasible. Compute is detectable: training advanced AI systems requires tens of thousands of highly advanced AI chips, which cannot be acquired or used inconspicuously. It is excludable: AI chips, being physical goods, can be given to or taken away from specific actors and in cases of specific uses. And it is quantifiable: chips, their features, and their usage can be measured. Compute’s detectability and excludability are further enhanced by the highly concentrated structure of the AI supply chain: very few companies are capable of producing the tools needed to design advanced chips, the machines needed to make them, or the data centers that house them. 

‍However, just because compute can be used as a tool to govern AI doesn’t mean that it should be used in all cases. Compute governance is a double-edged sword, with both potential benefits and the risk of negative consequences: it can support widely shared goals like safety, but it can also be used to infringe on civil liberties, perpetuate existing power structures, and entrench authoritarian regimes. Indeed, some things are better ungoverned. 

‍In our paper we argue that compute is a particularly promising node for AI governance. We also highlight the risks of compute governance and offer suggestions for how to mitigate them. This post summarizes our findings and key takeaways, while also offering some of our own commentary…(More)”

AI is too important to be monopolised

Curated on Posted on by Stefaan Verhulst

Article by Marietje Schaake: “…From the promise of medical breakthroughs to the perils of election interference, the hopes of helpful climate research to the challenge of cracking fundamental physics, AI is too important to be monopolised.

Yet the market is moving in exactly that direction, as resources and talent to develop the most advanced AI sit firmly in the hands of a very small number of companies. That is particularly true for resource-intensive data and computing power (termed “compute”), which are required to train large language models for a variety of AI applications. Researchers and small and medium-sized enterprises risk fatal dependency on Big Tech once again, or else they will miss out on the latest wave of innovation. 

On both sides of the Atlantic, feverish public investments are being made in an attempt to level the computational playing field. To ensure scientists have access to capacities comparable to those of Silicon Valley giants, the US government established the National AI Research Resource last month. This pilot project is being led by the US National Science Foundation. By working with 10 other federal agencies and 25 civil society groups, it will facilitate government-funded data and compute to help the research and education community build and understand AI. 

The EU set up a decentralised network of supercomputers with a similar aim back in 2018, before the recent wave of generative AI created a new sense of urgency. The EuroHPC has lived in relative obscurity and the initiative appears to have been under-exploited. As European Commission president Ursula von der Leyen said late last year: we need to put this power to use. The EU now imagines that democratised supercomputer access can also help with the creation of “AI factories,” where small businesses pool their resources to develop new cutting-edge models. 

There has long been talk of considering access to the internet a public utility, because of how important it is for education, employment and acquiring information. Yet rules to that end were never adopted. But with the unlocking of compute as a shared good, the US and the EU are showing real willingness to make investments into public digital infrastructure.

Even if the latest measures are viewed as industrial policy in a new jacket, they are part of a long overdue step to shape the digital market and offset the outsized power of big tech companies in various corners of our societies…(More)”.

Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data

Curated on Posted on by Stefaan Verhulst

Report by the National Academies of Sciences, Engineering, and Medicine: “Protecting privacy and ensuring confidentiality in data is a critical component of modernizing our national data infrastructure. The use of blended data – combining previously collected data sources – presents new considerations for responsible data stewardship. Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data provides a framework for managing disclosure risks that accounts for the unique attributes of blended data and poses a series of questions to guide considered decision-making.

Technical approaches to manage disclosure risk have advanced. Recent federal legislation, regulation and guidance has described broadly the roles and responsibilities for stewardship of blended data. The report, drawing from the panel review of both technical and policy approaches, addresses these emerging opportunities and the new challenges and responsibilities they present. The report underscores that trade-offs in disclosure risks, disclosure harms, and data usefulness are unavoidable and are central considerations when planning data-release strategies, particularly for blended data…(More)”.

Enabling Data-Driven Innovation : Learning from Korea’s Data Policies and Practices for Harnessing AI 

Curated on Posted on by Stefaan Verhulst

Report by the World Bank: “Over the past few decades, the Republic of Korea has consciously undertaken initiatives to transform its economy into a competitive, data-driven system. The primary objectives of this transition were to stimulate economic growth and job creation, enhance the nation’s capacity to withstand adversities such as the aftermath of COVID-19, and position it favorably to capitalize on emerging technologies, particularly artificial intelligence (AI). The Korean government has endeavored to accomplish these objectives through establishing a dependable digital data infrastructure and a comprehensive set of national data policies. This policy note aims to present a comprehensive synopsis of Korea’s extensive efforts to establish a robust digital data infrastructure and utilize data as a key driver for innovation and economic growth. The note additionally addresses the fundamental elements required to realize these benefits of data, including data policies, data governance, and data infrastructure. Furthermore, the note highlights some key results of Korea’s data policies, including the expansion of public data opening, the development of big data platforms, and the growth of the AI Hub. It also mentions the characteristics and success factors of Korea’s data policy, such as government support and the reorganization of institutional infrastructures. However, it acknowledges that there are still challenges to overcome, such as in data collection and utilization as well as transitioning from a government-led to a market-friendly data policy. The note concludes by providing developing countries and emerging economies with specific insights derived from Korea’s forward-thinking policy making that can assist them in harnessing the potential and benefits of data…(More)”.