Paper by Ekaterina Gilman et al: “Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest…(More)”.
Artificial Intelligence, Scientific Discovery, and Product Innovation
Paper by Aidan Toner-Rodgers: “… studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization…(More)”.
Privacy during pandemics: Attitudes to public use of personal data
Paper by Eleonora Freddi and Ole Christian Wasenden: “In this paper we investigate people’s attitudes to privacy and sharing of personal data when used to help society combat a contagious disease, such as COVID-19. Through a two-wave survey, we investigate the role of personal characteristics, and the effect of information, in shaping privacy attitudes. By conducting the survey in Norway and Sweden, which adopted very different strategies to handle the COVID-19 pandemic, we analyze potential differences in privacy attitudes due to policy changes. We find that privacy concern is negatively correlated with allowing public use of personal data. Trust in the entity collecting data and collectivist preferences are positively correlated with this type of data usage. Providing more information about the public benefit of sharing personal data makes respondents more positive to the use of their data, while providing additional information about the costs associated with data sharing does not change attitudes. The analysis suggests that stating a clear purpose and benefit for the data collection makes respondents more positive about sharing. Despite very different policy approaches, we do not find any major differences in privacy attitudes between Norway and Sweden. Findings are also similar between the two survey waves, suggesting a minor role for contextual changes…(More)”
Voice and Access in AI: Global AI Majority Participation in Artificial Intelligence Development and Governance
Paper by Sumaya N. Adan et al: “Artificial intelligence (AI) is rapidly emerging as one of the most transformative technologies in human history, with the potential to profoundly impact all aspects of society globally. However, access to AI and participation in its development and governance is concentrated among a few countries with advanced AI capabilities, while the ‘Global AI Majority’ – defined as the population of countries primarily encompassing Africa, Latin America, South and Southeast Asia, and parts of Eastern Europe – is largely excluded. These regions, while diverse, share common challenges in accessing and influencing advanced AI technologies.
This white paper investigates practical remedies to increase voice in and access to AI governance and capabilities for the Global AI Majority, while addressing the security and commercial concerns of frontier AI states. We examine key barriers facing the Global AI Majority, including limited access to digital and compute infrastructure, power concentration in AI development, Anglocentric data sources, and skewed talent distributions. The paper also explores the dual-use dilemma of AI technologies and how it motivates frontier AI states to implement restrictive policies.
We evaluate a spectrum of AI development initiatives, ranging from domestic model creation to structured access to deployed models, assessing their feasibility for the Global AI Majority. To resolve governance dilemmas, we propose three key approaches: interest alignment, participatory architecture, and safety assurance…(More)”.
Uniting the UK’s Health Data: A Huge Opportunity for Society’
The Sudlow Review (UK): “…Surveys show that people in the UK overwhelmingly support the use of their health data with appropriate safeguards to improve lives. One of the review’s recommendations calls for continued engagement with patients, the public, and healthcare professionals to drive forward developments in health data research.
The review also features several examples of harnessing health data for public benefit in the UK, such as the national response to the COVID-19 pandemic. But successes like these are few and far between due to complex systems and governance. The review reveals that:
- Access to datasets is difficult or slow, often taking months or even years.
- Data is accessible for analysis and research related to COVID-19, but not to tackle other health conditions, such as other infectious diseases, cancer, heart disease, stroke, diabetes and dementia.
- More complex types of health data generally don’t have national data systems (for example, most lab testing data and radiology imaging).
- Barriers like these can delay or prevent hundreds of studies, holding back progress that could improve lives…
The Sudlow Review’s recommendations provide a pathway to establishing a secure and trusted health data system for the UK:
- Major national public bodies with responsibility for or interest in health data should agree a coordinated joint strategy to recognise England’s health data for what they are: a critical national infrastructure.
- Key government health, care and research bodies should establish a national health data service in England with accountable senior leadership.
- The Department of Health and Social Care should oversee and commission ongoing, coordinated, engagement with patients, public, health professionals, policymakers and politicians.
- The health and social care departments in the four UK nations should set a UK-wide approach to streamline data access processes and foster proportionate, trustworthy data governance.
- National health data organisations and statistical authorities in the four UK nations should develop a UK-wide system for standards and accreditation of secure data environments (SDEs) holding data from the health and care system…(More)”.
The need for climate data stewardship: 10 tensions and reflections regarding climate data governance
Paper by Stefaan Verhulst: “Datafication—the increase in data generation and advancements in data analysis—offers new possibilities for governing and tackling worldwide challenges such as climate change. However, employing data in policymaking carries various risks, such as exacerbating inequalities, introducing biases, and creating gaps in access. This paper articulates 10 core tensions related to climate data and its implications for climate data governance, ranging from the diversity of data sources and stakeholders to issues of quality, access, and the balancing act between local needs and global imperatives. Through examining these tensions, the article advocates for a paradigm shift towards multi-stakeholder governance, data stewardship, and equitable data practices to harness the potential of climate data for the public good. It underscores the critical role of data stewards in navigating these challenges, fostering a responsible data ecology, and ultimately contributing to a more sustainable and just approach to climate action and broader social issues…(More)”.
Normative Foundations for International Data Governance: Goals and Principles
Proposed Foundations by the United Nations System Chief Executives Board for Coordination: “…suggests a set of common goals and principles that could form the normative basis for international data governance. The document does not present universally agreed goals and principles but seeks to provide an input to ongoing and future deliberations on the topic, including within the context of follow-up to the Global Digital Compact and other intergovernmental processes. Prepared by the HLCP Working Group on international data governance, the document seeks to complement existing standards and principles and builds on the Group’s previous paper “International Data Governance: Pathways to Progress”, which outlined a vision and steps towards the promotion of data governance grounded in human rights and sustainable development through a multistakeholder consultative approach.
Central to the document are three overarching goals: value, trust, and equity. Value highlights the necessity for an enabling environment that fosters responsible data use and reuse, alongside the critical importance of interoperability to facilitate effective data sharing. Trust is cultivated through a human rights-based approach, prioritizing data protection and privacy, while ensuring accountability and high standards of data quality throughout the lifecycle. Equity is promoted by empowering individuals and communities exercise control over their personal data and ensuring that the benefits of data access are distributed fairly, particularly to vulnerable and marginalized groups…(More)”.
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Paper by Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, and Alex Pentland: “In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders’ profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis…(More)”.
Towards effective governance of justice data
OECD working paper: “…explores the role of data governance in advancing people-centred justice systems. It outlines the objectives, values, and practices necessary to harness data effectively, drawing on OECD policy instruments. The paper provides actionable insights for policymakers aiming to implement data-driven justice reforms. It also addresses the challenges and opportunities presented by digital transformation in the justice sector, advocating for a strategic approach that balances innovation with the protection of fundamental rights. It incorporates lessons from data governance activities and experiences in justice and other relevant sectors. This paper is essential reading for those involved in modernisation of justice and data governance…(More)”.
The Rise of AI-Generated Content in Wikipedia
Paper by Creston Brooks, Samuel Eggert, and Denis Peskoff: “The rise of AI-generated content in popular information sources raises significant concerns about accountability, accuracy, and bias amplification. Beyond directly impacting consumers, the widespread presence of this content poses questions for the long-term viability of training language models on vast internet sweeps. We use GPTZero, a proprietary AI detector, and Binoculars, an open-source alternative, to establish lower bounds on the presence of AI-generated content in recently created Wikipedia pages. Both detectors reveal a marked increase in AI-generated content in recent pages compared to those from before the release of GPT-3.5. With thresholds calibrated to achieve a 1% false positive rate on pre-GPT-3.5 articles, detectors flag over 5% of newly created English Wikipedia articles as AI-generated, with lower percentages for German, French, and Italian articles. Flagged Wikipedia articles are typically of lower quality and are often self-promotional or partial towards a specific viewpoint on controversial topics…(More)”