Generative AI, Jobs, and Policy Response


Paper by the Global Partnership on AI: “Generative AI and the Future of Work remains notably absent from the global AI governance dialogue. Given the transformative potential of this technology in the workplace, this oversight suggests a significant gap, especially considering the substantial implications this technology has for workers, economies and society at large. As interest grows in the effects of Generative AI on occupations, debates centre around roles being replaced or enhanced by technology. Yet there is an incognita, the “Big Unknown”, an important number of workers whose future depends on decisions yet to be made
In this brief, recent articles about the topic are surveyed with special attention to the “Big Unknown”. It is not a marginal number: nearly 9% of the workforce, or 281 million workers worldwide, are in this category. Unlike previous AI developments which focused on automating narrow tasks, Generative AI models possess the scope, versatility, and economic viability to impact jobs across multiple industries and at varying skill levels. Their ability to produce human-like outputs in areas like language, content creation and customer interaction, combined with rapid advancement and low deployment costs, suggest potential near-term impacts that are much broader and more abrupt than prior waves of AI. Governments, companies, and social partners should aim to minimize any potential negative effects from Generative AI technology in the world of work, as well as harness potential opportunities to support productivity growth and decent work. This brief presents concrete policy recommendations at the global and local level. These insights, are aimed to guide the discourse towards a balanced and fair integration of Generative AI in our professional landscape To navigate this uncertain landscape and ensure that the benefits of Generative AI are equitably distributed, we recommend 10 policy actions that could serve as a starting point for discussion and implementation…(More)”.

Technology Foresight for Public Funding of Innovation: Methods and Best Practices


JRC Paper: “In times of growing uncertainties and complexities, anticipatory thinking is essential for policymakers. Technology foresight explores the longer-term futures of Science, Technology and Innovation. It can be used as a tool to create effective policy responses, including in technology and innovation policies, and to shape technological change. In this report we present six anticipatory and technology foresight methods that can contribute to anticipatory intelligence in terms of public funding of innovation: the Delphi survey, genius forecasting, technology roadmapping, large language models used in foresight, horizon scanning and scenario planning. Each chapter provides a brief overview of the method with case studies and recommendations. The insights from this report show that only by combining different anticipatory viewpoints and approaches to spotting, understanding and shaping emergent technologies, can public funders such as the European Innovation Council improve their proactive approaches to supporting ground-breaking technologies. In this way, they will help innovation ecosystems to develop…(More)”.

Disaster preparedness: Will a “norm nudge” sink or swim?


Article by Jantsje Mol: “In these times of unprecedented climate change, one critical question persists: how do we motivate homeowners to protect their homes and loved ones from the ever-looming threat of flooding? This question led to a captivating behavioral science study, born from a research visit to the Wharton Risk Management and Decision Processes Center in 2019 (currently the Wharton Climate Center). Co-founded and co-directed by the late Howard Kunreuther, the Center has been at the forefront of understanding and mitigating the impact of natural disasters. In this study, we explored the potential of social norms to boost flood preparedness among homeowners. While the results may not align with initial expectations, they shed light on the complexities of human behavior, the significance of meticulous testing, and the enduring legacy of a visionary scholar.

The Power of Social Norms

Before we delve into the results, let’s take a moment to understand what social norms are and why they matter. Social norms dictate what is considered acceptable or expected in a given community. A popular behavioral intervention based on social norms is a norm-nudge: reading information about what others do (say, energy saving behavior of neighbors or tax compliance rates of fellow citizens) may adjust one’s own behavior closer. Norm-nudges are cheap, easy to implement and less prone to political resistance than traditional interventions such as taxes, but they might be ineffective or even backfire. Norm-nudges have been applied to health, finance and the environment, but not yet to the context of natural disaster risk-reduction…(More)”.

To redesign democracy, the U.S. should borrow an idea from Dublin


Article by Claudia Chwalisz and Zia Khan: “…Let’s start with some of the mechanics: The typical citizens’ assembly convenes community members from all walks of life to study, deliberate, and provide recommendations to policy questions on behalf of the larger public. Crucially, these representatives are randomly selected through a lottery (also known as sortition) and serve temporarily, as with jury duty.The idea is to reach beyond the typical folks who show up at a school board meeting or that run for office but instead engage a true cross-section of the community. Assemblies make every citizen a potential representative of the people, not just a vote to be turned out. 

While citizen’s assemblies were eclipsed as a tool of governance as elections came to define democracy, the idea actually dates back to ancient Athens and shaped early democratic institutions in America, like the jury system. Now, as the United States grapples with its own challenges of division and discord and the 2024 elections loom, this old idea points us toward new ways of giving people real voice and power. 

Assemblies can create good conditions for people to have honest conversations, grapple with tradeoffs, and understand different points of view. As shown in other equally diverse and large countries, citizens’ assemblies can be instrumental in addressing issues that have proven particularly divisive or have been susceptible to political stagnation, such as homelessnessclimate changeland usesafety and policingabortiontransgender rightsmigration, and others.

In most places, assemblies have been only advisory thus far—but the moral authority of speaking on behalf of the people and hard-won consensus can be powerful. In Ireland and many other places, they’ve been organized by public authorities as a way to supplement input from elected officials…(More)”.

Can Google Trends predict asylum-seekers’ destination choices?


Paper by Haodong Qi & Tuba Bircan: “Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration…(More)”.

AI and Big Data: Disruptive Regulation


Book by Mark Findlay, Josephine Seah, and Willow Wong: “This provocative and timely book identifies and disrupts the conventional regulation and governance discourses concerning AI and big data. It suggests that, instead of being used as tools for exclusionist commercial markets, AI and big data can be employed in governing digital transformation for social good.

Analysing the ways in which global technology companies have colonised data access, the book reveals how trust, ethics, and digital self-determination can be reconsidered and engaged to promote the interests of marginalised stakeholders in data arrangement. Chapters examine the regulation of labour engagement in digital economies, the landscape of AI ethics, and a multitude of questions regarding participation, costs, and sustainability. Presenting several informative case studies, the book challenges some of the accepted qualifiers of frontier tech and data use and proposes innovative ways of actioning the more conventional regulatory components of big data.

Scholars and students in information and media law, regulation and governance, and law and politics will find this book to be critical reading. It will also be of interest to policymakers and the AI and data science community…(More)”.

Extremely Online: The Untold Story of Fame, Influence, and Power on the Internet


Book by Taylor Lorenz: “For over a decade, Taylor Lorenz has been the authority on internet culture, documenting its far-reaching effects on all corners of our lives. Her reporting is serious yet entertaining and illuminates deep truths about ourselves and the lives we create online. In her debut book, Extremely Online, she reveals how online influence came to upend the world, demolishing traditional barriers and creating whole new sectors of the economy. Lorenz shows this phenomenon to be one of the most disruptive changes in modern capitalism.

By tracing how the internet has changed what we want and how we go about getting it, Lorenz unearths how social platforms’ power users radically altered our expectations of content, connection, purchasing, and power. Lorenz documents how moms who started blogging were among the first to monetize their personal brands online, how bored teens who began posting selfie videos reinvented fame as we know it, and how young creators on TikTok are leveraging opportunities to opt out of the traditional career pipeline. It’s the real social history of the internet.

Emerging seemingly out of nowhere, these shifts in how we use the internet seem easy to dismiss as fads. However, these social and economic transformations have resulted in a digital dynamic so unappreciated and insurgent that it ultimately created new approaches to work, entertainment, fame, and ambition in the 21st century…(More)”.

Evidence 2.0: The Next Era of Evidence-Based Policymaking


Interview with Nick Hart & Jason Saul: “One of the great—if largely unsung—bipartisan congressional acts of recent history was the passage in 2018 of the Foundations for Evidence-Based Policymaking Act. In essence, the “Evidence Act” codified the goal of using solid, consistent evidence as the basis for funding decisions on trillions of dollars of public money. Agencies use this data to decide on the most effective and most promising solutions for a vast array of issues, from early-childhood education to environmental protection.

Five years later, while most federal agencies have created fairly robust evidence bases, unlocking that evidence for practical use by decision makers remains challenging. One might argue that if Evidence 1.0 was focused on the production of evidence, then the next five years—let’s call it Evidence 2.0—will be focused on the effective use of that evidence. Now that evidence is readily available to policymakers, the question is, how can that data be standardized, aggregated, derived, applied, and used for predictive decision-making?…(More)”.

Hopes over fears: Can democratic deliberation increase positive emotions concerning the future?


Paper by S. Ahvenharju, M. Minkkinen, and F. Lalot: “Deliberative mini-publics have often been considered to be a potential way to promote future-oriented thinking. Still, thinking about the future can be hard as it can evoke negative emotions such as stress and anxiety. This article establishes why a more positive outlook towards the future can benefit long-term decision-making. Then, it explores whether and to what extent deliberative mini-publics can facilitate thinking about the future by moderating negative emotions and encouraging positive emotions. We analyzed an online mini-public held in the region of Satakunta, Finland, organized to involve the public in the drafting process of a regional plan extending until the year 2050. In addition to the standard practices related to mini-publics, the Citizens’ Assembly included an imaginary time travel exercise, Future Design, carried out with half of the participants. Our analysis makes use of both survey and qualitative data. We found that democratic deliberation can promote positive emotions, like hopefulness and compassion, and lessen negative emotions, such as fear and confusion, related to the future. There were, however, differences in how emotions developed in the various small groups. Interviews with participants shed further light onto how participants felt during the event and how their sentiments concerning the future changed…(More)”.

What Big Tech Knows About Your Body


Article by Yael Grauer: “If you were seeking online therapy from 2017 to 2021—and a lot of people were—chances are good that you found your way to BetterHelp, which today describes itself as the world’s largest online-therapy purveyor, with more than 2 million users. Once you were there, after a few clicks, you would have completed a form—an intake questionnaire, not unlike the paper one you’d fill out at any therapist’s office: Are you new to therapy? Are you taking any medications? Having problems with intimacy? Experiencing overwhelming sadness? Thinking of hurting yourself? BetterHelp would have asked you if you were religious, if you were LGBTQ, if you were a teenager. These questions were just meant to match you with the best counselor for your needs, small text would have assured you. Your information would remain private.

Except BetterHelp isn’t exactly a therapist’s office, and your information may not have been completely private. In fact, according to a complaint brought by federal regulators, for years, BetterHelp was sharing user data—including email addresses, IP addresses, and questionnaire answers—with third parties, including Facebook and Snapchat, for the purposes of targeting ads for its services. It was also, according to the Federal Trade Commission, poorly regulating what those third parties did with users’ data once they got them. In July, the company finalized a settlement with the FTC and agreed to refund $7.8 million to consumers whose privacy regulators claimed had been compromised. (In a statement, BetterHelp admitted no wrongdoing and described the alleged sharing of user information as an “industry-standard practice.”)

We leave digital traces about our health everywhere we go: by completing forms like BetterHelp’s. By requesting a prescription refill online. By clicking on a link. By asking a search engine about dosages or directions to a clinic or pain in chest dying. By shopping, online or off. By participating in consumer genetic testing. By stepping on a smart scale or using a smart thermometer. By joining a Facebook group or a Discord server for people with a certain medical condition. By using internet-connected exercise equipment. By using an app or a service to count your steps or track your menstrual cycle or log your workouts. Even demographic and financial data unrelated to health can be aggregated and analyzed to reveal or infer sensitive information about people’s physical or mental-health conditions…(More)”.