The Good and Bad of Anticipating Migration

Curated on Posted on by Stefaan Verhulst

Article by Sara Marcucci, Stefaan Verhulst, María Esther Cervantes, Elena Wüllhorst: “This blog is the first in a series that will be published weekly, dedicated to exploring innovative anticipatory methods for migration policy. Over the coming weeks, we will delve into various aspects of these methods, delving into their value, challenges, taxonomy, and practical applications. 

This first blog serves as an exploration of the value proposition and challenges inherent in innovative anticipatory methods for migration policy. We delve into the various reasons why these methods hold promise for informing more resilient, and proactive migration policies. These reasons include evidence-based policy development, enabling policymakers to ground their decisions in empirical evidence and future projections. Decision-takers, users, and practitioners can benefit from anticipatory methods for policy evaluation and adaptation, resource allocation, the identification of root causes, and the facilitation of humanitarian aid through early warning systems. However, it’s vital to acknowledge the challenges associated with the adoption and implementation of these methods, ranging from conceptual concerns such as fossilization, unfalsifiability, and the legitimacy of preemptive intervention, to practical issues like interdisciplinary collaboration, data availability and quality, capacity building, and stakeholder engagement. As we navigate through these complexities, we aim to shed light on the potential and limitations of anticipatory methods in the context of migration policy, setting the stage for deeper explorations in the coming blogs of this series…(More)”.

Towards a Considered Use of AI Technologies in Government 

Curated on Posted on by Stefaan Verhulst

Report by the Institute on Governance and Think Digital: “… undertook a case study-based research project, where 24 examples of AI technology projects and governance frameworks across a dozen jurisdictions were scanned. The purpose of this report is to provide policymakers and practitioners in government with an overview of controversial deployments of Artificial Intelligence (AI) technologies in the public sector, and to highlight some of the approaches being taken to govern the responsible use of these technologies in government. 

Two environmental scans make up the majority of the report. The first scan presents relevant use cases of public sector applications of AI technologies and automation, with special attention given to controversial projects and program/policy failures. The second scan surveys existing governance frameworks employed by international organizations and governments around the world. Each scan is then analyzed to determine common themes across use cases and governance frameworks respectively. The final section of the report provides risk considerations related to the use of AI by public sector institutions across use cases…(More)”.

How ChatGPT and other AI tools could disrupt scientific publishing

Curated on Posted on by Stefaan Verhulst

Article by Gemma Conroy: “When radiologist Domenico Mastrodicasa finds himself stuck while writing a research paper, he turns to ChatGPT, the chatbot that produces fluent responses to almost any query in seconds. “I use it as a sounding board,” says Mastrodicasa, who is based at the University of Washington School of Medicine in Seattle. “I can produce a publication-ready manuscript much faster.”

Mastrodicasa is one of many researchers experimenting with generative artificial-intelligence (AI) tools to write text or code. He pays for ChatGPT Plus, the subscription version of the bot based on the large language model (LLM) GPT-4, and uses it a few times a week. He finds it particularly useful for suggesting clearer ways to convey his ideas. Although a Nature survey suggests that scientists who use LLMs regularly are still in the minority, many expect that generative AI tools will become regular assistants for writing manuscripts, peer-review reports and grant applications.

Those are just some of the ways in which AI could transform scientific communication and publishing. Science publishers are already experimenting with generative AI in scientific search tools and for editing and quickly summarizing papers. Many researchers think that non-native English speakers could benefit most from these tools. Some see generative AI as a way for scientists to rethink how they interrogate and summarize experimental results altogether — they could use LLMs to do much of this work, meaning less time writing papers and more time doing experiments…(More)”.

The growing energy footprint of artificial intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Alex de Vries: “Throughout 2022 and 2023, artificial intelligence (AI) has witnessed a period of rapid expansion and extensive, large-scale application. Prominent tech companies such as Alphabet and Microsoft significantly increased their support for AI in 2023, influenced by the successful launch of OpenAI’s ChatGPT, a conversational generative AI chatbot that reached 100 million users in an unprecedented 2 months. In response, Microsoft and Alphabet introduced their own chatbots, Bing Chat and Bard, respectively.

 This accelerated development raises concerns about the electricity consumption and potential environmental impact of AI and data centers. In recent years, data center electricity consumption has accounted for a relatively stable 1% of global electricity use, excluding cryptocurrency mining. Between 2010 and 2018, global data center electricity consumption may have increased by only 6%.

 There is increasing apprehension that the computational resources necessary to develop and maintain AI models and applications could cause a surge in data centers’ contribution to global electricity consumption.

This commentary explores initial research on AI electricity consumption and assesses the potential implications of widespread AI technology adoption on global data center electricity use. The piece discusses both pessimistic and optimistic scenarios and concludes with a cautionary note against embracing either extreme…(More)”.

FickleFormulas: The Political Economy of Macroeconomic Measurement

Curated on Posted on by Stefaan Verhulst

About: “Statistics about economic activities are critical to governance. Measurements of growth, unemployment and inflation rates, public debts – they all tell us ‘how our economies are doing’ and inform policy. Citizens punish politicians who fail to deliver on them.

FickleFormulas has integrated two research projects at the University of Amsterdam that ran from 2014 to 2020. Its researchers have studied the origins of the formulas behind these indicators: why do we measure our economies the way we do? After all, it is far from self-evident how to define and measure economic indicators. Our choices have deeply distributional consequences, producing winners and losers, and they shape our future, for example when GDP figures hide the cost of environmental destruction.

Criticisms of particular measures are hardly new. GDP in particular has been denounced as a deeply deficient measure of production at best and a fundamentally misleading guidepost for human development at worst. But also measures of inflation, balances of payments and trade, unemployment figures, productivity or public debt hide unsolved and maybe insoluble problems. In FickleFormulas we have asked: which social, political and economic factors shape the formulas used to calculate macroeconomic indicators?

In our quest for answers we have mobilized scholarship and expertise scattered across academic disciplines – a wealth of knowledge brought together for example here. We have reconstructed expert-deliberations of past decades, but mostly we wanted to learn from those who actually design macroeconomic indicators: statisticians at national statistical offices or organizations such as the OECD, the UN, the IMF, or the World Bank. For us, understanding macroeconomic indicators has been impossible without talking to the people who live and breathe them….(More)”.

Google’s Expanded ‘Flood Hub’ Uses AI to Help Us Adapt to Extreme Weather

Curated on Posted on by Stefaan Verhulst

Article by Jeff Young: “Google announced Tuesday that a tool using artificial intelligence to better predict river floods will be expanded to the U.S. and Canada, covering more than 800 North American riverside communities that are home to more than 12 million people. Google calls it Flood Hub, and it’s the latest example of how AI is being used to help adapt to extreme weather events associated with climate change.

“We see tremendous opportunity for AI to solve some of the world’s biggest challenges, and climate change is very much one of those,” Google’s Chief Sustainability Officer, Kate Brandt, told Newsweek in an interview.

At an event in Brussels on Tuesday, Google announced a suite of new and expanded sustainability initiatives and products. Many of them involve the use of AI, such as tools to help city planners find the best places to plant trees and modify rooftops to buffer against city heat, and a partnership with the U.S. Forest Service to use AI to improve maps related to wildfires.

Google Flood Hub Model AI extreme weather
A diagram showing the development of models used in Google’s Flood Hub, now available for 800 riverside locations in the U.S. and Canada. Courtesy of Google Research…

Brandt said Flood Hub’s engineers use advanced AI, publicly available data sources and satellite imagery, combined with hydrologic models of river flows. The results allow flooding predictions with a longer lead time than was previously available in many instances…(More)”.

Towards a Holistic EU Data Governance

Curated on Posted on by Stefaan Verhulst

SITRA Publication: “The European Union’s ambitious data strategy aims to establish the EU as a leader in a data-driven society by creating a single market for data while fully respecting European policies on privacy, data protection, and competition law. To achieve the strategy’s bold aims, Europe needs more practical business cases where data flows across the organisations.

Reliable data sharing requires new technical, governance and business solutions. Data spaces address these needs by providing soft infrastructure to enable trusted and easy data flows across organisational boundaries.

Striking the right balance between regulation and innovation will be critical to creating a supportive environment for data-sharing business cases to flourish. In this working paper, we take an in-depth look at the governance issues surrounding data sharing and data spaces.

Data sharing requires trust. Trust can be facilitated by effective governance, meaning the rules for data sharing. These rules come from different arenas. The European Commission is establishing new regulations related to data, and member states also have their laws and authorities that oversee data-sharing activities. Ultimately, data spaces need local rules to enable interoperability and foster trust between participants. The governance framework for data spaces is called a rulebook, which codifies legal, business, technical, and ethical rules for data sharing.

The extensive discussions and interviews with experts reveal confusion in the field. People developing data sharing in practice or otherwise involved in data governance issues struggle to know who does what and who decides what. Data spaces also struggle to create internal governance structures in line with the regulatory environment. The interviews conducted for this study indicate that coordination at the member state level could play a decisive role in coordinating the EU-level strategy with concrete local data space initiatives.

The root cause of many of the pain points we identify is the problem of gaps, duplication and overlapping of roles between the different actors at all levels. To address these challenges and cultivate effective governance, a holistic data governance framework is proposed. This framework combines the existing approach of rulebooks with a new tool called the rolebook, which serves as a register of roles and bodies involved in data sharing. The rolebook aims to increase clarity and empower stakeholders at all levels to understand the current data governance structures.

In conclusion, effective governance is crucial for the success of the EU data strategy and the development of data spaces. By implementing the proposed holistic data governance framework, the EU can promote trust, balanced regulation and innovation, and support the growth of data spaces across sectors…(More)”.

The emergence of non-personal data markets

Curated on Posted on by Stefaan Verhulst

Report by the Think Tank of the European Parliament: “The European Commission’s Data Strategy aims to create a single market for data, open to data from across the world, where personal and non-personal data, including sensitive business data, are secure. The EU Regulation on the free flow of non-personal data allows non-personal data to be stored and processed anywhere in the EU without unjustified restrictions, with limited exceptions based on grounds of public security. The creation of multiple common sector-specific European data spaces aims to ensure Europe’s global competitiveness and data sovereignty. The Data Act proposed by the Commission aims to remove barriers to data access for both consumers and businesses and to establish common rules to govern the sharing of data generated using connected products or related services.

The aim of the study is to provide an in-depth, comprehensive, and issue-specific analysis of the emergence of non-personal data markets in Europe. The study seeks to identify the potential value of the non-personal data market, potential challenges and solutions, and the legislative/policy measures necessary to facilitate the further development of non-personal data markets. The study also ranks the main non-personal data markets by size and growth rate and provides a sector-specific analysis for the mobility and transport, energy, and manufacturing sectors…(More)”.

Generative AI, Jobs, and Policy Response

Curated on Posted on by Stefaan Verhulst

Paper by the Global Partnership on AI: “Generative AI and the Future of Work remains notably absent from the global AI governance dialogue. Given the transformative potential of this technology in the workplace, this oversight suggests a significant gap, especially considering the substantial implications this technology has for workers, economies and society at large. As interest grows in the effects of Generative AI on occupations, debates centre around roles being replaced or enhanced by technology. Yet there is an incognita, the “Big Unknown”, an important number of workers whose future depends on decisions yet to be made
In this brief, recent articles about the topic are surveyed with special attention to the “Big Unknown”. It is not a marginal number: nearly 9% of the workforce, or 281 million workers worldwide, are in this category. Unlike previous AI developments which focused on automating narrow tasks, Generative AI models possess the scope, versatility, and economic viability to impact jobs across multiple industries and at varying skill levels. Their ability to produce human-like outputs in areas like language, content creation and customer interaction, combined with rapid advancement and low deployment costs, suggest potential near-term impacts that are much broader and more abrupt than prior waves of AI. Governments, companies, and social partners should aim to minimize any potential negative effects from Generative AI technology in the world of work, as well as harness potential opportunities to support productivity growth and decent work. This brief presents concrete policy recommendations at the global and local level. These insights, are aimed to guide the discourse towards a balanced and fair integration of Generative AI in our professional landscape To navigate this uncertain landscape and ensure that the benefits of Generative AI are equitably distributed, we recommend 10 policy actions that could serve as a starting point for discussion and implementation…(More)”.

Four Questions to Guide Decision-Making for Data Sharing and Integration

Curated on Posted on by Stefaan Verhulst

Paper by the Actionable Intelligence for Social Policy Center: “This paper presents a Four Question Framework to guide data integration partners in building a strong governance and legal foundation to support ethical data use. While this framework was developed based on work in the United States that routinely integrates public data, it is meant to be a simple, digestible tool that can be adapted to any context. The framework was developed through a series of public deliberation workgroups and 15 years of field experience working with a diversity of data integration efforts across the United States.
The Four Questions – Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)? – should be considered within an established data governance framework and alongside core partners to determine whether and how to move forward when building an Integrated Data System (IDS) and also at each stage of a specific data project. We discuss these questions in depth, with a particular focus on the role of governance in establishing legal and ethical data use. In addition, we provide example data governance structures from two IDS sites and hypothetical scenarios that illustrate key considerations for the Four Question Framework.
A robust governance process is essential for determining whether data sharing and integration is legal, ethical, and a good idea within the local context. This process is iterative and as relational as it is technical, which means authentic collaboration across partners should be prioritized at each stage of a data use project. The Four Questions serve as a guide for determining whether to undertake data sharing and integration and should be regularly revisited throughout the life of a project…(More)”.