Paper by Hao Cui and Taha Yasseri: “Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in humanAI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. We explore how agents’ diversity and interactions influence the system’s collective intelligence and analyze real-world instances of AI-enhanced collective intelligence. We conclude by considering potential challenges and future developments in this field….(More)” See also: Where and When AI and CI Meet: Exploring the Intersection of Artificial and Collective Intelligence
Unlocking AI for All: The Case for Public Data Banks
Article by Kevin Frazier: “The data relied on by OpenAI, Google, Meta, and other artificial intelligence (AI) developers is not readily available to other AI labs. Google and Meta relied, in part, on data gathered from their own products to train and fine-tune their models. OpenAI used tactics to acquire data that now would not work or may be more likely to be found in violation of the law (whether such tactics violated the law when originally used by OpenAI is being worked out in the courts). Upstart labs as well as research outfits find themselves with a dearth of data. Full realization of the positive benefits of AI, such as being deployed in costly but publicly useful ways (think tutoring kids or identifying common illnesses), as well as complete identification of the negative possibilities of AI (think perpetuating cultural biases) requires that labs other than the big players have access to quality, sufficient data.
The proper response is not to return to an exploitative status quo. Google, for example, may have relied on data from YouTube videos without meaningful consent from users. OpenAI may have hoovered up copyrighted data with little regard for the legal and social ramifications of that approach. In response to these questionable approaches, data has (rightfully) become harder to acquire. Cloudflare has equipped websites with the tools necessary to limit data scraping—the process of extracting data from another computer program. Regulators have developed new legal limits on data scraping or enforced old ones. Data owners have become more defensive over their content and, in some cases, more litigious. All of these largely positive developments from the perspective of data creators (which is to say, anyone and everyone who uses the internet) diminish the odds of newcomers entering the AI space. The creation of a public AI training data bank is necessary to ensure the availability of enough data for upstart labs and public research entities. Such banks would prevent those new entrants from having to go down the costly and legally questionable path of trying to hoover up as much data as possible…(More)”.
The Deletion Remedy
Paper by Daniel Wilf-Townsend: “A new remedy has emerged in the world of technology governance. Where someone has wrongfully obtained or used data, this remedy requires them to delete not only that data, but also to delete tools such as machine learning models that they have created using the data. Model deletion, also called algorithmic disgorgement or algorithmic destruction, has been increasingly sought in both private litigation and public enforcement actions. As its proponents note, model deletion can improve the regulation of privacy, intellectual property, and artificial intelligence by providing more effective deterrence and better management of ongoing harms
But, this article argues, model deletion has a serious flaw. In its current form, it has the possibility of being a grossly disproportionate penalty. Model deletion requires the destruction of models whose training included illicit data in any degree, with no consideration of how much (or even whether) that data contributed to any wrongful gains or ongoing harms. Model deletion could thereby cause unjust losses in litigation and chill useful technologies.
This article works toward a well-balanced doctrine of model deletion by building on the remedy’s equitable origins. It identifies how traditional considerations in equity—such as a defendant’s knowledge and culpability, the balance of the hardships, and the availability of more tailored alternatives—can be applied in model deletion cases to mitigate problems of disproportionality. By accounting for proportionality, courts and agencies can develop a doctrine of model deletion that takes advantage of its benefits while limiting its potential excesses…(More)”.
The Arrival of Field Experiments in Economics
Article by Timothy Taylor: “When most people think of “experiments,” they think of test tubes and telescopes, of Petri dishes and Bunsen burners. But the physical apparatus is not central to what an “experiment” means. Instead, what matters is the ability to specify different conditions–and then to observe how the differences in the underlying conditions alter the outcomes. When “experiments” are understood in this broader way, the application of “experiments” is expanded.
For example, back in 1881 when Louis Pasteur tested his vaccine for sheep anthrax, he gave the vaccine to half of a flock of sheep, expose the entire group to anthrax, and showed that those with the vaccine survived. More recently, the “Green Revolution” in agricultural technology was essentially a set of experiments, by systematically breeding plant varieties and then looking at the outcomes in terms of yield, water use, pest resistance, and the like.
This understanding of “experiment” can be applied in economics, as well. John A. List explains in “Field Experiments: Here Today Gone Tomorrow?” (American Economist, published online August 6, 2024). By “field experiments,” List is seeking to differentiate his topic from “lab experiments,” which for economists refers to experiments carried out in a classroom context, often with students as the subjects, and to focus instead on experiments that involve people in the “field”–that is, in the context of their actual economic activities, including work, selling and buying, charitable giving, and the like. As List points out, these kinds of economic experiments have been going on for decades. He points out that government agencies have been conducting field experiments for decades…(More)”.
Leveraging AI for Democracy: Civic Innovation on the New Digital Playing Field
Report by Beth Kerley, Carl Miller, and Fernanda Campagnucci: “Like social media before them, new AI tools promise to change the game when it comes to civic engagement. These technologies offer bold new possibilities for investigative journalists, anticorruption advocates, and others working with limited resources to advance democratic norms.
Yet the transformation wrought by AI advances is far from guaranteed to work in democracy’s favor. Potential threats to democracy from AI have drawn wide attention. To better the odds for prodemocratic actors in a fluid technological environment, systematic thinking about how to make AI work for democracy is needed.
The essays in this report outline possible paths toward a prodemocratic vision for AI. An overview essay by Beth Kerley based on insights from an International Forum for Democratic Studies expert workshop reflects on the critical questions that confront organizations seeking to deploy AI tools. Fernanda Campagnucci, spotlighting the work of Open Knowledge Brasil to open up government data, explores how AI advances are creating new opportunities for citizens to scrutinize public information. Finally, Demos’s Carl Miller sheds light on how AI technologies that enable new forms of civic deliberation might change the way we think about democratic participation itself…(More)“.
How to rebuild democracy to truly harness the power of the people
Article by Kyle Ellingson: “Many of us entered this so-called super-election year with a sense of foreboding. So far, not much has happened to allay those fears. Russia’s war on Ukraine is exacerbating a perception that democracy is threatened in Europe and beyond. In the US, Donald Trump, a presidential candidate with self-professed autocratic tendencies, has faced two assassination attempts. And more broadly, people seem to be losing faith in politics. “Most people from a diverse array of countries around the world lack confidence in the performance of their political institutions,” says a 2024 report by the International Institute for Democracy and Electoral Assistance.
On many objective measures, too, democracy isn’t functioning as it should. The systems we call democracies tend to favour the rich. Political violence is growing, as is legislative gridlock, and worldwide, elections are becoming less free and fair. Some 30 years after commentators crowed about the triumph of Western liberal democracy, their prediction seems further than ever from being realised. What happened?
According to Lex Paulson at the University Mohammed VI Polytechnic in Rabat, Morocco, we have lost sight of what democracy is. “We have made a terrible confusion between the system known as a republic – which relies on elections, parties and a permanent governing class – and the system known as a democracy, in which citizens directly participate in decisions and rotate power.” …(More)”.
China: Autocracy 2.0
Paper by David Y. Yang: “Autocracy 2.0, exemplified by modern China, is economically robust, technologically advanced, globally engaged, and controlled through subtle and sophisticated methods. What defines China’s political economy, and what drives Autocracy 2.0? What is its future direction? I start by discussing two key challenges autocracies face: incentives and information. I then describe Autocracy 1.0’s reliance on fear and repression to address these issues. It makes no credible promises, using coercion for compliance, resulting in a low-information environment. Next, I introduce Autocracy 2.0, highlighting its significant shift in handling commitment and information challenges. China uses economic incentives to align interests with regime survival, fostering support. It employs advanced bureaucratic structures and technology to manage incentives and information, enabling success in a high-information environment. Finally, I explore Autocracy 3.0’s potential. In China, forces might revert to Autocracy 1.0, using technology for state control as growth slows but aspirations stay high. Globally, modern autocracies, led by China, are becoming major geopolitical forces, challenging the liberal democratic order…(More)”.
Zillow introduces First Street’s comprehensive climate risk data on for-sale listings across the US
Press Release: “Zillow® is introducing climate risk data, provided by First Street…Home shoppers will gain insights into five key risks—flood, wildfire, wind, heat and air quality—directly from listing pages, complete with risk scores, interactive maps and insurance requirements.
With more than 80% of buyers now considering climate risks when purchasing a home, this feature provides a clearer understanding of potential hazards, helping buyers to better assess long-term affordability and plan for the future. In assisting buyers to navigate the growing risk of climate change, Zillow is the only platform to feature tailored insurance recommendations alongside detailed historical insights, showing if or when a property has experienced past climate events, such as flooding or wildfires…
When using Zillow’s search map view, home shoppers can explore climate risk data through an interactive map highlighting five key risk categories: flood, wildfire, wind, heat and air quality. Each risk is color-coded and has its own color scale, helping consumers intuitively navigate their search. Informative labels give more context to climate data and link to First Street’s property-specific climate risk reports for full insights.
When viewing a for-sale property on Zillow, home shoppers will see a new climate risk section. This section includes a separate module for each risk category—flood, wildfire, wind, heat and air quality—giving detailed, property-specific data from First Street. This section not only shows how these risks might affect the home now and in the future, but also provides crucial information on wind, fire and flood insurance requirements.
Nationwide, more new listings came with major climate risk, compared to homes listed for sale five years ago, according to a Zillow analysis conducted in August. That trend holds true for all five of the climate risk categories Zillow analyzed. Across all new listings in August, 16.7% were at major risk of wildfire, while 12.8% came with a major risk of flooding…(More)”.
The paradox of climate data in West Africa: growing urgency coupled with diminishing accessibility
Cirad: “In 2022, a prolonged drought devastated maize crops in northern Burkina Faso, leaving two million people without sufficient food resources. This dramatic situation could have been better anticipated and its impacts could have been mitigated with the collection and equitable sharing of specific data: that of agrometeorology, the science that studies the effects of meteorological, climatological and hydrological factors on crops.
Although it is too late to prevent the 2022 drought, protecting people from future droughts remains an urgent priority, especially in Africa, a continent where climate change poses a serious threat to rainfed agriculture, its main agricultural and economic activity.
To anticipate these climate risks, it is essential to have access to reliable meteorological data, which is crucial for ensuring sustainable and resilient agricultural practices. Yet in West Africa, the accessibility and reliability of this data are increasingly threatened and face unprecedented diplomatic, economic and security challenges…(More)”.
Harnessing digital footprint data for population health: a discussion on collaboration, challenges and opportunities in the UK
Paper by Romana Burgess et al: “Digital footprint data are inspiring a new era in population health and well-being research. Linking these novel data with other datasets is critical for future research wishing to use these data for the public good. In order to succeed, successful collaboration among industry, academics and policy-makers is vital. Therefore, we discuss the benefits and obstacles for these stakeholder groups in using digital footprint data for research in the UK. We advocate for policy-makers’ inclusion in research efforts, stress the exceptional potential of digital footprint research to impact policy-making and explore the role of industry as data providers, with a focus on shared value, commercial sensitivity, resource requirements and streamlined processes. We underscore the importance of multidisciplinary approaches, consumer trust and ethical considerations in navigating methodological challenges and further call for increased public engagement to enhance societal acceptability. Finally, we discuss how to overcome methodological challenges, such as reproducibility and sharing of learnings, in future collaborations. By adopting a multiperspective approach to outlining the challenges of working with digital footprint data, our contribution helps to ensure that future research can navigate these challenges effectively while remaining reproducible, ethical and impactful…(More)”