Advanced Flood Hub features for aid organizations and govern

Curated on Posted on by Stefaan Verhulst

Announcement by Alex Diaz: “Floods continue to devastate communities worldwide, and many are pursuing advancements in AI-driven flood forecasting, enabling faster, more efficient detection and response. Over the past few years, Google Research has focused on harnessing AI modeling and satellite imagery to dramatically accelerate the reliability of flood forecasting — while working with partners to expand coverage for people in vulnerable communities around the world.

Today, we’re rolling out new advanced features in Flood Hub designed to allow experts to understand flood risk in a given region via inundation history maps, and to understand how a given flood forecast on Flood Hub might propagate throughout a river basin. With the inundation history maps, Flood Hub expert users can view flood risk areas in high resolution over the map regardless of a current flood event. This is useful for cases where our flood forecasting does not include real time inundation maps or for pre-planning of humanitarian work. You can find more explanations about the inundation history maps and more in the Flood Hub Help Center…(More)”.

Policymaking assessment framework

Curated on Posted on by Stefaan Verhulst

Guide by the Susan McKinnon Foundation: “This assessment tool supports the measurement of the quality of policymaking processes – both existing and planned – across  sectors. It provides a flexible framework for rating public policy processes using information available in the public domain. The framework’s objective is to simplify the path towards best practice, evidence-informed policy.

It is intended to accommodate the complexity of policymaking processes and reflect the realities and context within which policymaking is undertaken. The criteria can be tailored for different policy problems and policy types and applied across sectors and levels of government.

The framework is structured around five key domains:

  1. understanding the problem
  2. engagement with stakeholders and partners
  3. outcomes focus
  4. evidence for the solution, and
  5. design and communication…(More)”.

Intellectual property issues in artificial intelligence trained on scraped data

Curated on Posted on by Stefaan Verhulst

OECD Report: “Recent technological advances in artificial intelligence (AI), especially the rise of generative AI, have raised questions regarding the intellectual property (IP) landscape. As the demand for AI training data surges, certain data collection methods give rise to concerns about the protection of IP and other rights. This report provides an overview of key issues at the intersection of AI and some IP rights. It aims to facilitate a greater understanding of data scraping — a primary method for obtaining AI training data needed to develop many large language models. It analyses data scraping techniques, identifies key stakeholders, and worldwide legal and regulatory responses. Finally, it offers preliminary considerations and potential policy approaches to help guide policymakers in navigating these issues, ensuring that AI’s innovative potential is unleashed while protecting IP and other rights…(More)”.

Building AI for the pluralistic society

Curated on Posted on by Stefaan Verhulst

Paper by Aida Davani and Vinodkumar Prabhakaran: “Modern artificial intelligence (AI) systems rely on input from people. Human feedback helps train models to perform useful tasks, guides them toward safe and responsible behavior, and is used to assess their performance. While hailing the recent AI advancements, we should also ask: which humans are we actually talking about? For AI to be most beneficial, it should reflect and respect the diverse tapestry of values, beliefs, and perspectives present in the pluralistic world in which we live, not just a single “average” or majority viewpoint. Diversity in perspectives is especially relevant when AI systems perform subjective tasks, such as deciding whether a response will be perceived as helpful, offensive, or unsafe. For instance, what one value system deems as offensive may be perfectly acceptable within another set of values.

Since divergence in perspectives often aligns with socio-cultural and demographic lines, preferentially capturing certain groups’ perspectives over others in data may result in disparities in how well AI systems serve different social groups. For instance, we previously demonstrated that simply taking a majority vote from human annotations may obfuscate valid divergence in perspectives across social groups, inadvertently marginalizing minority perspectives, and consequently performing less reliably for groups marginalized in the data. How AI systems should deal with such diversity in perspectives depends on the context in which they are used. However, current models lack a systematic way to recognize and handle such contexts.

With this in mind, here we describe our ongoing efforts in pursuit of capturing diverse perspectives and building AI for the pluralistic society in which we live… (More)”.

Public participation in policymaking: exploring and understanding impact

Curated on Posted on by Stefaan Verhulst

Report by the Scottish Government: “This research builds on that framework and seeks to explore how Scottish Government might better understand the impact of public participation on policy decision-making. As detailed above, there is a plethora of potential, and anticipated, benefits which may arise from increased citizen participation in policy decision-making, as well as lots of participatory activity already taking place across the organisation. Now is an opportune time to consider impact, to support the design and delivery of participatory engagements that are impactful and that are more likely to realise the benefits of public participation. Through a review of academic and grey literature along with stakeholder engagement, this study aims to answer the following questions:

  • 1. How is impact conceptualised in literature related to public participation, and what are some practice examples?
  • 2. How is impact conceptualised by stakeholders and what do they perceive as the current blockers, challenges or facilitators in a Scottish Government setting?
  • 3. What evaluation tools or frameworks are used to evaluate the impact of public participation processes, and which ones might be applicable /usable in a Scottish Government setting?…(More)”.

Economic Implications of Data Regulation

Curated on Posted on by Stefaan Verhulst

OECD Report: “Cross-border data flows are the lifeblood of today’s social and economic interactions, but they also raise a range of new challenges, including for privacy and data protection, national security, cybersecurity, digital protectionism and regulatory reach. This has led to a surge in regulation conditioning (or prohibiting) its flow or mandating that data be stored or processed domestically (data localisation). However, the economic implications of these measures are not well understood. This report provides estimates on what is at stake, highlighting that full fragmentation could reduce global GDP by 4.5%. It also underscores the benefits associated with open regimes with safeguards which could see global GDP increase by 1.7%. In a world where digital fragmentation is growing, global discussions on these issues can help harness the benefits of an open and safeguarded internet…(More)”.

Sandboxes for AI

Curated on Posted on by Stefaan Verhulst

Report by Datasphere Initiative: “The Sandboxes for AI report explores the role of regulatory sandboxes in the development and governance of artificial intelligence. Originally presented as a working paper at the Global Sandbox Forum Inaugural Meeting in July 2024, the report was further refined through expert consultations and an online roundtable in December 2024. It examines sandboxes that have been announced, are under development, or have been completed, identifying common patterns in their creation, timing, and implementation. By providing insights into why and how regulators and companies should consider AI sandboxes, the report serves as a strategic guide for fostering responsible innovation.

In a rapidly evolving AI landscape, traditional regulatory processes often struggle to keep pace with technological advancements. Sandboxes offer a flexible and iterative approach, allowing policymakers to test and refine AI governance models in a controlled environment. The report identifies 66 AI, data, or technology-related sandboxes globally, with 31 specifically designed for AI innovation across 44 countries. These initiatives focus on areas such as machine learning, data-driven solutions, and AI governance, helping policymakers address emerging challenges while ensuring ethical and transparent AI development…(More)”.

Recommendations for Better Sharing of Climate Data

Curated on Posted on by Stefaan Verhulst

Creative Commons: “…the culmination of a nine-month research initiative from our Open Climate Data project. These guidelines are a result of collaboration between Creative Commons, government agencies and intergovernmental organizations. They mark a significant milestone in our ongoing effort to enhance the accessibility, sharing, and reuse of open climate data to address the climate crisis. Our goal is to share strategies that align with existing data sharing principles and pave the way for a more interconnected and accessible future for climate data.

Our recommendations offer practical steps and best practices, crafted in collaboration with key stakeholders and organizations dedicated to advancing open practices in climate data. We provide recommendations for 1) legal and licensing terms, 2) using metadata values for attribution and provenance, and 3) management and governance for better sharing.

Opening climate data requires an examination of the public’s legal rights to access and use the climate data, often dictated by copyright and licensing. This legal detail is sometimes missing from climate data sharing and legal interoperability conversations. Our recommendations suggest two options: Option A: CC0 + Attribution Request, in order to maximize reuse by dedicating climate data to the public domain, plus a request for attribution; and Option B: CC BY 4.0, for retaining data ownership and legal enforcement of attribution. We address how to navigate license stacking and attribution stacking for climate data hosts and for users working with multiple climate data sources.

We also propose standardized human- and machine-readable metadata values that enhance transparency, reduce guesswork, and ensure broader accessibility to climate data. We built upon existing model metadata schemas and standards, including those that address license and attribution information. These recommendations address a gap and provide metadata schema that standardize the inclusion of upfront, clear values related to attribution, licensing and provenance.

Lastly, we highlight four key aspects of effective climate data management: designating a dedicated technical managing steward, designating a legal and/or policy steward, encouraging collaborative data sharing, and regularly revisiting and updating data sharing policies in accordance with parallel open data policies and standards…(More)”.

Net zero: the role of consumer behaviour

Curated on Posted on by Stefaan Verhulst

Horizon Scan by the UK Parliament: “According to research from the Centre for Climate Change and Social Transformation, reaching net zero by 2050 will require individual behaviour change, particularly when it comes to aviation, diet and energy use.

The government’s 2023 Powering Up Britain: Net Zero Growth Plan referred to low carbon choices as ‘green choices’, and described them as public and businesses choosing green products, services, and goods. The plan sets out six principles regarding policies to facilitate green choices. Both the Climate Change Committee and the House of Lords Environment and Climate Change Committee have recommended that government strategies should incorporate greater societal and behavioural change policies and guidance.

Contributors to the horizon scan identified managing consumer behaviour and habits to help achieve net zero as a topic of importance for parliament over the next five years. Change in consumer behaviour could result in approximately 60% of required emission reductions to reach net zero.[5] Behaviour change will be needed from the wealthiest in society, who according to Oxfam typically lead higher-carbon lifestyles.

Incorporating behavioural science principles into policy levers is a well-established method of encouraging desired behaviours. Common examples of policies aiming to influence behaviour include subsidies, regulation and information campaigns (see below).

However, others suggest deliberative public engagement approaches, such as the UK Climate Change Assembly,[7] may be needed to determine which pro-environmental policies are acceptable.[8] Repeated public engagement is seen as key to achieve a just transition as different groups will need different support to enable their green choices (PN 706).

Researchers debate the extent to which individuals should be responsible for making green choices as opposed to the regulatory and physical environment facilitating them, or whether markets, businesses and governments should be the main actors responsible for driving action. They highlight the need for different actions based on the context and the different ways individuals act as consumers, citizens, and within organisations and groups. Health, time, comfort and status can strongly influence individual decisions while finance and regulation are typically stronger motivations for organisations (PN 714)…(More)”

Network architecture for global AI policy

Curated on Posted on by Stefaan Verhulst

Article by Cameron F. Kerry, Joshua P. Meltzer, Andrea Renda, and Andrew W. Wyckoff: “We see efforts to consolidate international AI governance as premature and ill-suited to respond to the immense, complex, novel, challenges of governing advanced AI, and the current diverse and decentralized efforts as beneficial and the best fit for this complex and rapidly developing technology.

Exploring the vast terra incognita of AI, realizing its opportunities, and managing its risks requires governance that can adapt and respond rapidly to AI risks as they emerge, develop deep understanding of the technology and its implications, and mobilize diverse resources and initiatives to address the growing global demand for access to AI. No one government or body will have the capacity to take on these challenges without building multiple coalitions and working closely with experts and institutions in industry, philanthropy, civil society, and the academy.

A distributed network of networks can more effectively address the challenges and opportunities of AI governance than a centralized system. Like the architecture of the interconnected information technology systems on which AI depends, such a decentralized system can bring to bear redundancy, resiliency, and diversity by channeling the functions of AI governance toward the most timely and effective pathways in iterative and diversified processes, providing agility against setbacks or failures at any single point. These multiple centers of effort can harness the benefit of network effects and parallel processing.

We explore this model of distributed and iterative AI governance below…(More)”.