Paper by Sema Müge Özdemiray: “The view of achieving the desired results in public policies depends on steering individuals, with decisions and actions incompatible with rationality, in a predictable way has pushed policymakers to collaborate with psychology methods and theories. Accordingly, in the recent policy design of public authorities, there is an increasing interest in the nudge approach, which is considered a less costly, more liberal, more citizen-focused alternative to traditional policy instruments. Nudging, which has produced effective solutions for different social problems, has also brought with it many criticisms. These criticisms have led to questioning alternative and advanced new policy tools in the field of behavioral public policy. In this study, the “nudge-plus” approach is discussed as one of these policy tools, which was put forward by Peter John and Gerry Stoker and which argues that the criticisms directed to nudge can be overcome by incorporating a citizen-oriented perspective into the nudge approach. This study aims to draw attention to the prediction that the use of the nudge-plus method in public policy design can produce more effective results in line with today’s participatory and collaborative administration approach…(More)”.
Unleashing possibilities, ignoring risks: Why we need tools to manage AI’s impact on jobs
Article by Katya Klinova and Anton Korinek: “…Predicting the effects of a new technology on labor demand is difficult and involves significant uncertainty. Some would argue that, given the uncertainty, we should let the “invisible hand” of the market decide our technological destiny. But we believe that the difficulty of answering the question “Who is going to benefit and who is going to lose out?” should not serve as an excuse for never posing the question in the first place. As we emphasized, the incentives for cutting labor costs are artificially inflated. Moreover, the invisible hand theorem does not hold for technological change. Therefore, a failure to investigate the distribution of benefits and costs of AI risks invites a future with too many “so-so” uses of AI—uses that concentrate gains while distributing the costs. Although predictions about the downstream impacts of AI systems will always involve some uncertainty, they are nonetheless useful to spot applications of AI that pose the greatest risks to labor early on and to channel the potential of AI where society needs it the most.
In today’s society, the labor market serves as a primary mechanism for distributing income as well as for providing people with a sense of meaning, community, and purpose. It has been documented that job loss can lead to regional decline, a rise in “deaths of despair,” addiction and mental health problems. The path that we lay out aims to prevent abrupt job losses or declines in job quality on the national and global scale, providing an additional tool for managing the pace and shape of AI-driven labor market transformation.
Nonetheless, we do not want to rule out the possibility that humanity may eventually be much happier in a world where machines do a lot more economically valuable work. Even despite our best efforts to manage the pace and shape of AI labor market disruption through regulation and worker-centric practices, we may still face a future with significantly reduced human labor demand. Should the demand for human labor decrease permanently with the advancement of AI, timely policy responses will be needed to address both the lost incomes as well as the lost sense of meaning and purpose. In the absence of significant efforts to distribute the gains from advanced AI more broadly, the possible devaluation of human labor would deeply impact income distribution and democratic institutions’ sustainability. While a jobless future is not guaranteed, its mere possibility and the resulting potential societal repercussions demand serious consideration. One promising proposal to consider is to create an insurance policy against a dramatic decrease in the demand for human labor that automatically kicks in if the share of income received by workers declines, for example a “seed” Universal Basic Income that starts at a very small level and remains unchanged if workers continue to prosper but automatically rises if there is large scale worker displacement…(More)”.
Reimagining Our High-Tech World
Essay by Mike Kubzansky: “…Channeling the power of technology for the good of society requires a shared vision of an ideal society. Despite the country’s increasing polarization, most Americans agree on the principles of a representative democracy and embrace the three quintessential rights inscribed in the Declaration of Independence—life, liberty, and the pursuit of happiness. Freedom and individual liberty, including freedom of speech, religion, and assembly and the right to privacy, are fundamental to most people’s expectations for this country, as are equality for all citizens, a just legal system, and a strong economy. Widespread consensus also exists around giving children a strong start in life; ensuring access to basic necessities like health care, food, and housing; and taking care of the planet.
By deliberately building a digital tech system guided by these values, society has an opportunity to advance its interests and future-proof the digital tech system for better outcomes.
Such collective action requires a broad conversation about what kind of society Americans want and how digital technology fits into that vision. To initiate this discussion, I suggest five questions philanthropists, technologists, entrepreneurs, policy makers, academics, advocates, movement leaders, students, consumers, investors, and everyone else who has a stake in the nation’s future need to start asking—now….(More)”.
Informing the Global Data Future: Benchmarking Data Governance Frameworks
Paper by Sara Marcucci, Natalia González Alarcón, Stefaan G. Verhulst and Elena Wüllhorst: “Data has become a critical trans-national and cross-border resource. Yet, the lack of a well-defined approach to using it poses challenges to harnessing its value. This article explores the increasing importance of global data governance due to the rapid growth of data, and the need for responsible data practices. The purpose of this paper is to compare approaches and identify patterns in the emergent data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the paper provides information about the conditions when a more holistic, coordinated transnational approach to data governance may be needed to responsibly manage the global flow of data. The report does this by (a) considering conditions specified by the literature that may be conducive to global data governance, and (b) analyzing and comparing existing frameworks, specifically investigating six key elements: purpose, principles, anchoring documents, data description and lifecycle, processes, and practices. The article closes with a series of final recommendations, which include adopting a broader concept of data stewardship to reconcile data protection and promotion, focusing on responsible reuse of data to unlock socioeconomic value, harmonizing meanings to operationalize principles, incorporating global human rights frameworks to provide common North Stars, unifying key definitions of data, adopting a data lifecycle approach, incorporating participatory processes and collective agency, investing in new professions with specific roles, improving accountability through oversight and compliance mechanisms, and translating recommendations into practical tools…(More)”
The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing
Paper by Leonard Boussioux, Jacqueline Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim Lakhani: “This study investigates the capability of generative artificial intelligence (AI) in creating innovative business solutions compared to human crowdsourcing methods. We initiated a crowdsourcing challenge focused on sustainable, circular economy business opportunities. The challenge attracted a diverse range of solvers from a myriad of countries and industries. Simultaneously, we employed GPT-4 to generate AI solutions using three different prompt levels, each calibrated to simulate distinct human crowd and expert personas. 145 evaluators assessed a randomized selection of 10 out of 234 human and AI solutions, a total of 1,885 evaluator-solution pairs. Results showed comparable quality between human and AI-generated solutions. However, human ideas were perceived as more novel, whereas AI solutions delivered better environmental and financial value. We use natural language processing techniques on the rich solution text to show that although human solvers and GPT-4 cover a similar range of industries of application, human solutions exhibit greater semantic diversity. The connection between semantic diversity and novelty is stronger in human solutions, suggesting differences in how novelty is created by humans and AI or detected by human evaluators. This study illuminates the potential and limitations of both human and AI crowdsourcing to solve complex organizational problems and sets the groundwork for a possible integrative human-AI approach to problem-solving…(More)”.
The Age of Prediction: Algorithms, AI, and the Shifting Shadows of Risk
Book by Igor Tulchinsky and Christopher E. Mason: “… about two powerful, and symbiotic, trends: the rapid development and use of artificial intelligence and big data to enhance prediction, as well as the often paradoxical effects of these better predictions on our understanding of risk and the ways we live. Beginning with dramatic advances in quantitative investing and precision medicine, this book explores how predictive technology is quietly reshaping our world in fundamental ways, from crime fighting and warfare to monitoring individual health and elections.
As prediction grows more robust, it also alters the nature of the accompanying risk, setting up unintended and unexpected consequences. The Age of Prediction details how predictive certainties can bring about complacency or even an increase in risks—genomic analysis might lead to unhealthier lifestyles or a GPS might encourage less attentive driving. With greater predictability also comes a degree of mystery, and the authors ask how narrower risks might affect markets, insurance, or risk tolerance generally. Can we ever reduce risk to zero? Should we even try? This book lays an intriguing groundwork for answering these fundamental questions and maps out the latest tools and technologies that power these projections into the future, sometimes using novel, cross-disciplinary tools to map out cancer growth, people’s medical risks, and stock dynamics…(More)”.
It’s like jury duty, but for getting things done
Article by Hollie Russon Gilman and Amy Eisenstein: “Citizens’ assemblies have the potential to repair our broken politics…Imagine a democracy where people come together and their voices are heard and are translated directly into policy. Frontline workers, doctors, teachers, friends, and neighbors — young and old — are brought together in a random, representative sample to deliberate the most pressing issues facing our society. And they are compensated for their time.
The concept may sound radical. But we already use this method for jury duty. Why not try this widely accepted practice to tackle the deepest, most crucial, and most divisive issues facing our democracy?
The idea — known today as citizens’ assemblies — originated in ancient Athens. Instead of a top-down government, Athens used sortition — a system that was horizontal and distributive. The kleroterion, an allotment machine, randomly selected citizens to hold civic office, ensuring that the people had a direct say in their government’s dealings….(More)”.
To measure social impact, we could start by using the tools we already have
Article by Shamina Singh: “…To measure social impact, we could start by using the tools we already have.
In the environmental context, companies have adopted the Greenhouse Gas (GHG) Protocol, which tracks the full spectrum of a company’s carbon emissions. The first scope accounts for direct emissions from its operations, the second relates to indirect emissions from energy purchased by the company, and the third tracks indirect emissions from a company’s entire value chain.
At the Center for Inclusive Growth, we have been thinking about how to capture social impact in a similarly methodical way. Just as the environmental framework is tied to the level of control over the source of emissions, we could account for the level of control in social impact. I’ll offer up the following framework to show how our team is thinking about this challenge, so we can help spark a dialogue using the following as a conceptual starting point.
The first scope could cover each company’s approach toward its own employees, since companies have a direct influence on this stakeholder group through workplace investments, programs, and corporate culture. This category could assess pay equity, diversity within leadership ranks, talent development and career progression for underrepresented groups, labor standards, and more. Many companies already track these metrics.
Then, the second scope could look at how companies leverage their core competencies, deploy their products and services and work within their supply chains to help address societal challenges. Companies have skills, technologies, and capital that can create widespread social benefits, and many are already leading the way. The activity in this second category involves stakeholders at a level of control that is less direct than the first, such as customers and suppliers.
Finally, philanthropic giving, volunteering, and other community investments would comprise the third scope. This level of control is distinct from the second scope because company resources are entrusted to other entities that make decisions about how it’s spent. These efforts, while indirect, can strengthen a company’s brand and reputation, cultivate innovation and opportunity, and generate significant societal value.
From there, it’s about measuring the outputs of our investments in all three scopes. A system of accountability for follow-through is vital because when it comes to improving people’s lives, communities, and futures, outcomes matter-not just effort.
There is so much good work happening in the social impact space, but much more work to be done to measure it. To incentivize continued progress, we have to start quantifying the impact, even if the best way to do that looks different across companies or industries…(More)”
Do People Like Algorithms? A Research Strategy
Paper by Cass R. Sunstein and Lucia Reisch: “Do people like algorithms? In this study, intended as a promissory note and a description of a research strategy, we offer the following highly preliminary findings. (1) In a simple choice between a human being and an algorithm, across diverse settings and without information about the human being or the algorithm, people in our tested groups are about equally divided in their preference. (2) When people are given a very brief account of the data on which an algorithm relies, there is a large shift in favor of the algorithm over the human being. (3) When people are given a very brief account of the experience of the relevant human being, without an account of the data on which the relevant algorithm relies, there is a moderate shift in favor of the human being. (4) When people are given both (a) a very brief account of the experience of the relevant human being and (b) a very brief account of the data on which the relevant algorithm relies, there is a large shift in favor of the algorithm over the human being. One lesson is that in the tested groups, at least one-third of people seem to have a clear preference for either a human being or an algorithm – a preference that is unaffected by brief information that seems to favor one or the other. Another lesson is that a brief account of the data on which an algorithm relies does have a significant effect on a large percentage of the tested groups, whether or not people are also given positive information about the human alternative. Across the various surveys, we do not find persistent demographic differences, with one exception: men appear to like algorithms more than women do. These initial findings are meant as proof of concept, or more accurately as a suggestion of concept, intended to inform a series of larger and more systematic studies of whether and when people prefer to rely on algorithms or human beings, and also of international and demographic differences…(More)”.
The Design of Digital Democracy
Book by Gianluca Sgueo: “Ever-stronger ties between technology, entertainment and design are transforming our relationship with democratic decision-making. When we are online, or when we use digital products and services, we tend to focus more on certain factors like speed of service and user-friendliness, and to overlook the costs – both for ourselves and others. As a result, a widening gap separates our expectations of everything related to digitalization – including government – and the actual practice of democratic governance. Democratic regulators, unable to meet citizens’ demands for tangible, fast and gratifying returns, are seeing the poorest results ever recorded in terms of interest, engagement and retention, despite using the most cutting-edge technologies.
This book explores various aspects of the relationship between democracy, technology and entertainment. These include, on the one hand, the role that digital technology has in strengthening our collective intelligence, nurturing empathic relations between citizens and democratic institutions, and supporting processes of political aggregation, deliberation and collaboration. On the other hand, they comprise the challenges accompanying digital technology for representation, transparency and inclusivity in democratic decision-making.
The book’s main argument is that digital democratic spaces should be redesigned to narrow the gap between the expectations and outcomes of democratic decision-making. It suggests abandoning the notion of digital participatory rights as being fast and easy to enjoy. It also refutes the notion that digital democratic decision-making can only be effective when it delivers rapid and successful responses to the issues of the day, regardless of their complexity.
Ultimately, the success or failure of digital democracy will depend on the ability of public regulators to design digital public spaces with a commitment to complexity, so as to make them appealing, but also effective at engaging citizens…(More)”.