Paper by Ajinkya Kulkarni, et al: “Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targeting this population have received little attention. In this study, we seek to bridge this gap, and establish a baseline for the use of voice anonymization techniques designed for adult speech when applied to children’s voices. Such an evaluation is essential, as children’s speech presents a distinct set of challenges when compared to that of adults. This study comprises three children’s datasets, six anonymization methods, and objective and subjective utility metrics for evaluation. Our results show that existing systems for adults are still able to protect children’s voice privacy, but suffer from much higher utility degradation. In addition, our subjective study displays the challenges of automatic evaluation methods for speech quality in children’s speech, highlighting the need for further research…(More)”. See also: Responsible Data for Children.
Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
Position paper by Nicholas Vincent, Matthew Prewitt and Hanlin Li: “…argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, producers of information goods (such as journalists, researchers, and creative professionals) need to be able to collectively bargain with AI product builders in order to receive reasonable terms and a sustainable return on the informational value they contribute. We argue that without increased market coordination or collective bargaining on the side of these primary information producers, AI will exacerbate a large-scale “information market failure” that will lead not only to undesirable concentration of capital, but also to a potential “ecological collapse” in the informational commons. On the other hand, collective bargaining in the information economy can create market frictions and aligned incentives necessary for a pro-social, sustainable AI future. We provide concrete actions that can be taken to support a coalitionbased approach to achieve this goal. For example, researchers and developers can establish technical mechanisms such as federated data management tools and explainable data value estimations, to inform and facilitate collective bargaining in the information economy. Additionally, regulatory and policy interventions may be introduced to support trusted data intermediary organizations representing guilds or syndicates of information producers…(More)”.
Upgrading Democracies with Fairer Voting Methods
Paper by Evangelos Pournaras, et al: “Voting methods are instrumental design element of democracies. Citizens use them to express and aggregate their preferences to reach a collective decision. However, voting outcomes can be as sensitive to voting rules as they are to people’s voting choices. Despite the significance and inter-disciplinary scientific progress on voting methods, several democracies keep relying on outdated voting methods that do not fit modern, pluralistic societies well, while lacking social innovation. Here, we demonstrate how one can upgrade real-world democracies, namely by using alternative preferential voting methods such as cumulative voting and the method of equal shares designed for a proportional representation of voters’ preferences. By rigorously assessing a new participatory budgeting approach applied in the city of Aarau, Switzerland, we unravel the striking voting outcomes of fair voting methods: more winning projects with the same budget and broader geographic and preference representation of citizens by the elected projects, in particular for voters who used to be under-represented, while promoting novel project ideas. We provide profound causal evidence showing that citizens prefer proportional voting methods, which possess strong legitimacy without the need of very technical specialized explanations. We also reveal strong underlying democratic values exhibited by citizens who support fair voting methods such as altruism and compromise. These findings come with a global momentum to unleash a new and long-awaited participation blueprint of how to upgrade democracies…(More)”.
Amplifying Human Creativity and Problem Solving with AI Through Generative Collective Intelligence
Paper by Thomas P. Kehler, Scott E. Page, Alex Pentland, Martin Reeves and John Seely Brown: “We propose a new framework for human-AI collaboration that amplifies the distinct capabilities
of both. This framework, which we call Generative Collective Intelligence (GCI), shifts AI to the
group/social level and employs AI in dual roles: as interactive agents and as technology that
accumulates, organizes, and leverages knowledge. By creating a cognitive bridge between
human reasoning and AI models, GCI can overcome limitations of purely algorithmic
approaches to problem-solving and decision-making. The framework demonstrates how AI can
be reframed as a social and cultural technology that enables groups to solve complex problems
through structured collaboration that transcends traditional communication barriers. We describe
the mathematical foundations of GCI based on comparative judgment and minimum regret
principles, and illustrate its applications across domains including climate adaptation, healthcare
transformation, and civic participation. By combining human creativity with AI’s computational
capabilities, GCI offers a promising approach to addressing complex societal challenges that
neither human or machines can solve alone…(More)”.
Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16
Paper by Dilek Fraisl: “This paper presents the results of a pilot study conducted in Ghana that utilized citizen data approaches for monitoring a governance indicator within the SDG framework, focusing on indicator 16.6.2 citizen satisfaction with public services. This indicator is a crucial measure of governance quality, as emphasized by the UN Sustainable Development Goals (SDGs) through target 16.6 Develop effective, accountable, and transparent institutions at all levels. Indicator 16.6.2 specifically measures satisfaction with key public services, including health, education, and other government services, such as government-issued identification documents through a survey. However, with only 5 years remaining to achieve the SDGs, the lack of data continues to pose a significant challenge in monitoring progress toward this target, particularly regarding the experiences of marginalized populations. Our findings suggest that well-designed citizen data initiatives can effectively capture the experiences of marginalized individuals and communities. Additionally, they can serve as valuable supplements to official statistics, providing crucial data on population groups typically underrepresented in traditional surveys…(More)”.
Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review
Paper by Tino Kreutzer et al: “Humanitarian organizations are rapidly expanding their use of data in the pursuit of operational gains in effectiveness and efficiency. Ethical risks, particularly from artificial intelligence (AI) data processing, are increasingly recognized yet inadequately addressed by current humanitarian data protection guidelines. This study reports on a scoping review that maps the range of ethical issues that have been raised in the academic literature regarding data processing of people affected by humanitarian crises….
We identified 16,200 unique records and retained 218 relevant studies. Nearly one in three (n = 66) discussed technologies related to AI. Seventeen studies included an author from a lower-middle income country while four included an author from a low-income country. We identified 22 ethical issues which were then grouped along the four ethical value categories of autonomy, beneficence, non-maleficence, and justice. Slightly over half of included studies (n = 113) identified ethical issues based on real-world examples. The most-cited ethical issue (n = 134) was a concern for privacy in cases where personal or sensitive data might be inadvertently shared with third parties. Aside from AI, the technologies most frequently discussed in these studies included social media, crowdsourcing, and mapping tools.
Studies highlight significant concerns that data processing in humanitarian contexts can cause additional harm, may not provide direct benefits, may limit affected populations’ autonomy, and can lead to the unfair distribution of scarce resources. The increase in AI tool deployment for humanitarian assistance amplifies these concerns. Urgent development of specific, comprehensive guidelines, training, and auditing methods is required to address these ethical challenges. Moreover, empirical research from low and middle-income countries, disproportionally affected by humanitarian crises, is vital to ensure inclusive and diverse perspectives. This research should focus on the ethical implications of both emerging AI systems, as well as established humanitarian data management practices…(More)”.
Data as Policy
Paper by Janet Freilich and W. Nicholson Price II: “A large literature on regulation highlights the many different methods of policy-making: command-and-control rulemaking, informational disclosures, tort liability, taxes, and more. But the literature overlooks a powerful method to achieve policy objectives: data. The state can provide (or suppress) data as a regulatory tool to solve policy problems. For administrations with expansive views of government’s purpose, government-provided data can serve as infrastructure for innovation and push innovation in socially desirable directions; for administrations with deregulatory ambitions, suppressing or choosing not to collect data can reduce regulatory power or serve as a back-door mechanism to subvert statutory or common law rules. Government-provided data is particularly powerful for data-driven technologies such as AI where it is sometimes more effective than traditional methods of regulation. But government-provided data is a policy tool beyond AI and can influence policy in any field. We illustrate why government-provided data is a compelling tool both for positive regulation and deregulation in contexts ranging from addressing healthcare discrimination, automating legal practice, smart power generation, and others. We then consider objections and limitations to the role of government-provided data as policy instrument, with substantial focus on privacy concerns and the possibility for autocratic abuse.
We build on the broad literature on regulation by introducing data as a regulatory tool. We also join—and diverge from—the growing literature on data by showing that while data can be privately produced purely for private gain, they do not need to be. Rather, government can be deeply involved in the generation and sharing of data, taking a much more publicly oriented view. Ultimately, while government-provided data are not a panacea for either regulatory or data problems, governments should view data provision as an understudied but useful tool in the innovation and governance toolbox…(More)”
Using Gamification to Engage Citizens in Micro-Mobility Data Sharing
Paper by Anu Masso, Anniki Puura, Jevgenia Gerassimenko and Olle Järv: “The European Strategy for Data aims to create a unified environment for accessing, sharing, and reusing data across sectors, institutions, and individuals, with a focus on areas like mobility and smart cities. While significant progress has been made in the technical interoperability and legislative frameworks for data spaces, critical gaps persist in the bottom-up processes, particularly in fostering social collaboration and citizen-driven initiatives. What is often overlooked is the need for effective citizen engagement and collaborative governance models to ensure the long-term viability and inclusivity of these data spaces. In addition, although principles for successful data sharing are well-established in sectors like healthcare, they remain underdeveloped and more challenging to implement in areas such as mobility. This article addresses these gaps by exploring how gamification can drive bottom-up data space formation, engaging citizens in data-sharing and fostering collaboration among private companies, local governments, and academic institutions. Using bicycle usage as an example, it illustrates how gamification can incentivise citizens to share mobility data for social good, promoting more active and sustainable transportation in cities. Drawing on a case study from Tallinn (Estonia), the paper demonstrates how gamification can improve data collection, highlighting the vital role of citizen participation in urban planning. The article emphasises that while technological solutions for data spaces are advancing, understanding collaborative governance models for data sharing remains crucial for ensuring the success of the European Union’s data space agenda and driving sustainable innovation in urban environments…(More)”.
How Being Watched Changes How You Think
Article by Simon Makin: “In 1785 English philosopher Jeremy Bentham designed the perfect prison: Cells circle a tower from which an unseen guard can observe any inmate at will. As far as a prisoner knows, at any given time, the guard may be watching—or may not be. Inmates have to assume they’re constantly observed and behave accordingly. Welcome to the Panopticon.
Many of us will recognize this feeling of relentless surveillance. Information about who we are, what we do and buy and where we go is increasingly available to completely anonymous third parties. We’re expected to present much of our lives to online audiences and, in some social circles, to share our location with friends. Millions of effectively invisible closed-circuit television (CCTV) cameras and smart doorbells watch us in public, and we know facial recognition with artificial intelligence can put names to faces.
So how does being watched affect us? “It’s one of the first topics to have been studied in psychology,” says Clément Belletier, a psychologist at University of Clermont Auvergne in France. In 1898 psychologist Norman Triplett showed that cyclists raced harder in the presence of others. From the 1970s onward, studies showed how we change our overt behavior when we are watched to manage our reputation and social consequences.
But being watched doesn’t just change our behavior; decades of research show it also infiltrates our mind to impact how we think. And now a new study reveals how being watched affects unconscious processing in our brain. In this era of surveillance, researchers say, the findings raise concerns about our collective mental health…(More)”.
Measuring the Shade Coverage of Trees and Buildings in Cambridge, Massachusetts
Paper by Amirhosein Shabrang, Mehdi Pourpeikari Heris, and Travis Flohr: “We investigated the spatial shade patterns of trees and buildings on sidewalks and bike lanes in Cambridge, Massachusetts. We used Lidar data and 3D modeling to analyze the spatial and temporal shade distribution across the City. Our analysis shows significant shade variations throughout the City. Western city areas receive more shade from trees, and the eastern regions receive more shade from buildings. The City’s northern areas lack shade, but natural and built sources of shade can improve shade coverage integration. This study’s findings help identify shade coverage gaps, which have implications for urban planning and design for more heat-resilient cities…(More)”