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)”.

Two Paths for A.I.


Essay by Joshua Rothman: “Last spring, Daniel Kokotajlo, an A.I.-safety researcher working at OpenAI, quit his job in protest. He’d become convinced that the company wasn’t prepared for the future of its own technology, and wanted to sound the alarm. After a mutual friend connected us, we spoke on the phone. I found Kokotajlo affable, informed, and anxious. Advances in “alignment,” he told me—the suite of techniques used to insure that A.I. acts in accordance with human commands and values—were lagging behind gains in intelligence. Researchers, he said, were hurtling toward the creation of powerful systems they couldn’t control.

Kokotajlo, who had transitioned from a graduate program in philosophy to a career in A.I., explained how he’d educated himself so that he could understand the field. While at OpenAI, part of his job had been to track progress in A.I. so that he could construct timelines predicting when various thresholds of intelligence might be crossed. At one point, after the technology advanced unexpectedly, he’d had to shift his timelines up by decades. In 2021, he’d written a scenario about A.I. titled “What 2026 Looks Like.” Much of what he’d predicted had come to pass before the titular year. He’d concluded that a point of no return, when A.I. might become better than people at almost all important tasks, and be trusted with great power and authority, could arrive in 2027 or sooner. He sounded scared.

Around the same time that Kokotajlo left OpenAI, two computer scientists at Princeton, Sayash Kapoor and Arvind Narayanan, were preparing for the publication of their book, “AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference.” In it, Kapoor and Narayanan, who study technology’s integration with society, advanced views that were diametrically opposed to Kokotajlo’s. They argued that many timelines of A.I.’s future were wildly optimistic; that claims about its usefulness were often exaggerated or outright fraudulent; and that, because of the world’s inherent complexity, even powerful A.I. would change it only slowly. They cited many cases in which A.I. systems had been called upon to deliver important judgments—about medical diagnoses, or hiring—and had made rookie mistakes that indicated a fundamental disconnect from reality. The newest systems, they maintained, suffered from the same flaw.Recently, all three researchers have sharpened their views, releasing reports that take their analyses further. The nonprofit AI Futures Project, of which Kokotajlo is the executive director, has published “AI 2027,” a heavily footnoted document, written by Kokotajlo and four other researchers, which works out a chilling scenario in which “superintelligent” A.I. systems either dominate or exterminate the human race by 2030. It’s meant to be taken seriously, as a warning about what might really happen. Meanwhile, Kapoor and Narayanan, in a new paper titled “AI as Normal Technology,” insist that practical obstacles of all kinds—from regulations and professional standards to the simple difficulty of doing physical things in the real world—will slow A.I.’s deployment and limit its transformational potential. While conceding that A.I. may eventually turn out to be a revolutionary technology, on the scale of electricity or the internet, they maintain that it will remain “normal”—that is, controllable through familiar safety measures, such as fail-safes, kill switches, and human supervision—for the foreseeable future. “AI is often analogized to nuclear weapons,” they argue. But “the right analogy is nuclear power,” which has remained mostly manageable and, if anything, may be underutilized for safety reasons.

The Agentic State: How Agentic AI Will Revamp 10 Functional Layers of Public Administration


Whitepaper by the Global Government Technology Centre Berlin: “…explores how agentic AI will transform ten functional layers of government and public administration. The Agentic State signifies a fundamental shift in governance, where AI systems can perceive, reason, and act with minimal human intervention to deliver public value. Its impact on  key functional layers of government will be as follows…(More)”.

Unlock Your City’s Hidden Solutions


Article by Andreas Pawelke, Basma Albanna and Damiano Cerrone: “Cities around the world face urgent challenges — from climate change impacts to rapid urbanization and infrastructure strain. Municipal leaders struggle with limited budgets, competing priorities, and pressure to show quick results, making traditional approaches to urban transformation increasingly difficult to implement.

Every city, however, has hidden success stories — neighborhoods, initiatives, or communities that are achieving remarkable results despite facing similar challenges as their peers.

These “positive deviants” often remain unrecognized and underutilized, yet they contain the seeds of solutions that are already adapted to local contexts and constraints.

Data-Powered Positive Deviance (DPPD) combines urban data, advanced analytics, and community engagement to systematically uncover these bright spots and amplify their impact. This new approach offers a pathway to urban transformation that is not only evidence-based but also cost-effective and deeply rooted in local realities.

DPPD is particularly valuable in resource-constrained environments, where expensive external solutions often fail to take hold. By starting with what’s already working, cities can make strategic investments that build on existing strengths rather than starting from scratch. Leveraging AI tools that improve community engagement, the approach becomes even more powerful — enabling cities to envision potential futures, and engage citizens in meaningful co-creation…(More)”

AI in Urban Life


Book by Patricia McKenna: “In exploring artificial intelligence (AI) in urban life, this book brings together and extends thinking on how human-AI interactions are continuously evolving. Through such interactions, people are aided on the one hand, while becoming more aware of their own capabilities and potentials on the other hand, pertaining, for example, to creativity, human sensing, and collaboration.

It is the particular focus of research questions developed in relation to awareness, smart cities, autonomy, privacy, transparency, theory, methods, practices, and collective intelligence, along with the wide range of perspectives and opportunities offered, that set this work apart from others. Conceptual frameworks are formulated for each of these areas to guide explorations and understandings in this work and going forward. A synthesis is provided in the final chapter for perspectives, challenges and opportunities, and conceptual frameworks for urban life in an era of AI, opening the way for evolving research and practice directions…(More)”.

WorkflowHub: a registry for computational workflows


Paper by Ove Johan Ragnar Gustafsson et al: “The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered…(More)”

Where Cloud Meets Cement


Report by Hanna Barakat, Chris Cameron, Alix Dunn and Prathm Juneja, and Emma Prest: “This report examines the global expansion of data centers driven by AI and cloud computing, highlighting both their economic promises and the often-overlooked social and environmental costs. Through case studies across five countries, it investigates how governments and tech companies influence development, how communities resist harmful effects, and what support is needed for effective advocacy…(More)”.

Why Generative AI Isn’t Transforming Government (Yet) — and What We Can Do About It


Article by Tiago C. Peixoto: “A few weeks ago, I reached out to a handful of seasoned digital services practitioners, NGOs, and philanthropies with a simple question: Where are the compelling generative AI (GenAI) use cases in public-sector workflows? I wasn’t looking for better search or smarter chatbots. I wanted examples of automation of real public workflows – something genuinely interesting and working. The responses, though numerous, were underwhelming.

That question has gained importance amid a growing number of reports forecasting AI’s transformative impact on government. The Alan Turing Institute, for instance, published a rigorous study estimating the potential of AI to help automate over 140 million government transactions in the UK. The Tony Blair Institute also weighed in, suggesting that a substantive portion of public-sector work could be automated. While the report helped bring welcome attention to the issue, its use of GPT-4 to assess task automatability has sparked a healthy discussion about how best to evaluate feasibility. Like other studies in this area, both reports highlight potential – but stop short of demonstrating real service automation.

Without testing technologies in real service environments – where workflows, incentives, and institutional constraints shape outcomes – and grounding each pilot in clear efficiency or well-being metrics, estimates risk becoming abstractions that underestimate feasibility.

This pattern aligns with what Arvind Narayanan and Sayash Kapoor argue in “AI as Normal Technology:” the impact of AI is realized only when methods translate into applications and diffuse through real-world systems. My own review, admittedly non-representative, confirms their call for more empirical work on the innovation-diffusion lag.

In the public sector, the gap between capability and impact is not only wide but also structural…(More)”

We still don’t know how much energy AI consumes


Article by Sasha Luccioni: “…The AI Energy Score project, a collaboration between Salesforce, Hugging FaceAI developer Cohere and Carnegie Mellon University, is an attempt to shed more light on the issue by developing a standardised approach. The code is open and available for anyone to access and contribute to. The goal is to encourage the AI community to test as many models as possible.

By examining 10 popular tasks (such as text generation or audio transcription) on open-source AI models, it is possible to isolate the amount of energy consumed by the computer hardware that runs them. These are assigned scores ranging between one and five stars based on their relative efficiency. Between the most and least efficient AI models in our sample, we found a 62,000-fold difference in the power required. 

Since the project was launched in February a new tool compares the energy use of chatbot queries with everyday activities like phone charging or driving as a way to help users understand the environmental impacts of the tech they use daily.

The tech sector is aware that AI emissions put its climate commitments in danger. Both Microsoft and Google no longer seem to be meeting their net zero targets. So far, however, no Big Tech company has agreed to use the methodology to test its own AI models.

It is possible that AI models will one day help in the fight against climate change. AI systems pioneered by companies like DeepMind are already designing next-generation solar panels and battery materials, optimising power grid distribution and reducing the carbon intensity of cement production.

Tech companies are moving towards cleaner energy sources too. Microsoft is investing in the Three Mile Island nuclear power plant and Alphabet is engaging with more experimental approaches such as small modular nuclear reactors. In 2024, the technology sector contributed to 92 per cent of new clean energy purchases in the US. 

But greater clarity is needed. OpenAI, Anthropic and other tech companies should start disclosing the energy consumption of their models. If they resist, then we need legislation that would make such disclosures mandatory.

As more users interact with AI systems, they should be given the tools to understand how much energy each request consumes. Knowing this might make them more careful about using AI for superfluous tasks like looking up a nation’s capital. Increased transparency would also be an incentive for companies developing AI-powered services to select smaller, more sustainable models that meet their specific needs, rather than defaulting to the largest, most energy-intensive options…(More)”.

Public AI White Paper – A Public Alternative to Private AI Dominance


White paper by the Bertelsmann Stiftung and Open Future: “Today, the most advanced AI systems are developed and controlled by a small number of private companies. These companies hold power not only over the models themselves but also over key resources such as computing infrastructure. This concentration of power poses not only economic risks but also significant democratic challenges.

The Public AI White Paper presents an alternative vision, outlining how open and public-interest approaches to AI can be developed and institutionalized. It advocates for a rebalancing of power within the AI ecosystem – with the goal of enabling societies to shape AI actively, rather than merely consume it…(More)”.