Navigating Generative AI in Government

Curated on Posted on by Stefaan Verhulst

Report by the IBM Center for The Business of Government: “Generative AI refers to algorithms that can create realistic content such as images, text, music, and videos by learning from existing data patterns. Generative AI does more than just create content, it also serves as a user-friendly interface for other AI tools, making complex results easy to understand and use. Generative AI transforms analysis and prediction results into personalized formats, improving explainability by converting complicated data into understandable content. As Generative AI evolves, it plays an active role in collaborative processes, functioning as a vital collaborator by offering strengths that complement human abilities.

Generative AI has the potential to revolutionize government agencies by enhancing efficiency, improving decision making, and delivering better services to citizens, while maintaining agility and scalability. However, in order to implement generative AI solutions effectively, government agencies must address key questions—such as what problems AI can solve, data governance frameworks, and scaling strategies, to ensure a thoughtful and effective AI strategy. By exploring generic use cases, agencies can better understand the transformative potential of generative AI and align it with their unique needs and ethical considerations.

This report, which distills perspectives from two expert roundtable of leaders in Australia, presents 11 strategic pathways for integrating generative AI in government. The strategies include ensuring coherent and ethical AI implementation, developing adaptive AI governance models, investing in a robust data infrastructure, and providing comprehensive training for employees. Encouraging innovation and prioritizing public engagement and transparency are also essential to harnessing the full potential of AI…(More)”

The Emerging Age of AI Diplomacy

Curated on Posted on by Stefaan Verhulst

Article by Sam Winter-Levy: “In a vast conference room, below chandeliers and flashing lights, dozens of dancers waved fluorescent bars in an intricately choreographed routine. Green Matrix code rained down in the background on a screen that displayed skyscrapers soaring from a desert landscape. The world was witnessing the emergence of “a sublime and transcendent entity,” a narrator declared: artificial intelligence. As if to highlight AI’s transformative potential, a digital avatar—Artificial Superintelligence One—approached a young boy and together they began to sing John Lennon’s “Imagine.” The audience applauded enthusiastically. With that, the final day dawned on what one government minister in attendance described as the “world’s largest AI thought leadership event.”

This surreal display took place not in Palo Alto or Menlo Park but in Riyadh, Saudi Arabia, at the third edition of the city’s Global AI Summit, in September of this year. In a cavernous exhibition center next to the Ritz Carlton, where Crown Prince Mohammed bin Salman imprisoned hundreds of wealthy Saudis on charges of corruption in 2017,robots poured tea and mixed drinks. Officials in ankle-length white robes hailed Saudi Arabia’s progress on AI. American and Chinese technology companies pitched their products and announced memorandums of understanding with the government. Attendantsdistributed stickers that declared, “Data is the new oil.”

For Saudi Arabia and its neighbor, the United Arab Emirates (UAE), AI plays an increasingly central role in their attempts to transform their oil wealth into new economic models before the world transitions away from fossil fuels. For American AI companies, hungry for capital and energy, the two Gulf states and their sovereign wealth funds are tantalizing partners. And some policymakers in Washington see a once-in-a-generation opportunity to promise access to American computing power in a bid to lure the Gulf states away from China and deepen an anti-Iranian coalition in the Middle East….The two Gulf states’ interest in AI is not new, but it has intensified in recent months. Saudi Arabia plans to create a $40 billion fund to invest in AI and has set up Silicon Valley–inspired startup accelerators to entice coders to Riyadh. In 2019, the UAE launched the world’s first university dedicated to AI, and since 2021, the number of AI workers in the country has quadrupled, according to government figures. The UAE has also released a series of open-source large language models that it claims rival those of Google and Meta, and earlier this year it launched an investment firm focused on AI and semiconductors that could surpass $100 billion in assets under management…(More)”.

The New Artificial Intelligentsia

Curated on Posted on by Stefaan Verhulst

Essay by Ruha Benjamin: “In the Fall of 2016, I gave a talk at the Institute for Advanced Study in Princeton titled “Are Robots Racist?” Headlines such as “Can Computers Be Racist? The Human-Like Bias of Algorithms,” “Artificial Intelligence’s White Guy Problem,” and “Is an Algorithm Any Less Racist Than a Human?” had captured my attention in the months before. What better venue to discuss the growing concerns about emerging technologies, I thought, than an institution established during the early rise of fascism in Europe, which once housed intellectual giants like J. Robert Oppenheimer and Albert Einstein, and prides itself on “protecting and promoting independent inquiry.”

My initial remarks focused on how emerging technologies reflect and reproduce social inequities, using specific examples of what some termed “algorithmic discrimination” and “machine bias.” A lively discussion ensued. The most memorable exchange was with a mathematician who politely acknowledged the importance of the issues I raised but then assured me that “as AI advances, it will eventually show us how to address these problems.” Struck by his earnest faith in technology as a force for good, I wanted to sputter, “But what about those already being harmed by the deployment of experimental AI in healthcare, education, criminal justice, and more—are they expected to wait for a mythical future where sentient systems act as sage stewards of humanity?”

Fast-forward almost 10 years, and we are living in the imagination of AI evangelists racing to build artificial general intelligence (AGI), even as they warn of its potential to destroy us. This gospel of love and fear insists on “aligning” AI with human values to rein in these digital deities. OpenAI, the company behind ChatGPT, echoed the sentiment of my IAS colleague: “We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.” They envision a time when, eventually, “our AI systems can take over more and more of our alignment work and ultimately conceive, implement, study, and develop better alignment techniques than we have now. They will work together with humans to ensure that their own successors are more aligned with humans.” For many, this is not reassuring…(More)”.

Lifecycles, pipelines, and value chains: toward a focus on events in responsible artificial intelligence for health

Curated on Posted on by Stefaan Verhulst

Paper by Joseph Donia et al: “Process-oriented approaches to the responsible development, implementation, and oversight of artificial intelligence (AI) systems have proliferated in recent years. Variously referred to as lifecycles, pipelines, or value chains, these approaches demonstrate a common focus on systematically mapping key activities and normative considerations throughout the development and use of AI systems. At the same time, these approaches risk focusing on proximal activities of development and use at the expense of a focus on the events and value conflicts that shape how key decisions are made in practice. In this article we report on the results of an ‘embedded’ ethics research study focused on SPOTT– a ‘Smart Physiotherapy Tracking Technology’ employing AI and undergoing development and commercialization at an academic health sciences centre. Through interviews and focus groups with the development and commercialization team, patients, and policy and ethics experts, we suggest that a more expansive design and development lifecycle shaped by key events offers a more robust approach to normative analysis of digital health technologies, especially where those technologies’ actual uses are underspecified or in flux. We introduce five of these key events, outlining their implications for responsible design and governance of AI for health, and present a set of critical questions intended for others doing applied ethics and policy work. We briefly conclude with a reflection on the value of this approach for engaging with health AI ecosystems more broadly…(More)”.

Understanding local government responsible AI strategy: An international municipal policy document analysis

Curated on Posted on by Stefaan Verhulst

Paper by Anne David et al: “The burgeoning capabilities of artificial intelligence (AI) have prompted numerous local governments worldwide to consider its integration into their operations. Nevertheless, instances of notable AI failures have heightened ethical concerns, emphasising the imperative for local governments to approach the adoption of AI technologies in a responsible manner. While local government AI guidelines endeavour to incorporate characteristics of responsible innovation and technology (RIT), it remains essential to assess the extent to which these characteristics have been integrated into policy guidelines to facilitate more effective AI governance in the future. This study closely examines local government policy documents (n = 26) through the lens of RIT, employing directed content analysis with thematic data analysis software. The results reveal that: (a) Not all RIT characteristics have been given equal consideration in these policy documents; (b) Participatory and deliberate considerations were the most frequently mentioned responsible AI characteristics in policy documents; (c) Adaptable, explainable, sustainable, and accountable considerations were the least present responsible AI characteristics in policy documents; (d) Many of the considerations overlapped with each other as local governments were at the early stages of identifying them. Furthermore, the paper summarised strategies aimed at assisting local authorities in identifying their strengths and weaknesses in responsible AI characteristics, thereby facilitating their transformation into governing entities with responsible AI practices. The study informs local government policymakers, practitioners, and researchers on the critical aspects of responsible AI policymaking…(More)” See also: AI Localism

AI helped Uncle Sam catch $1 billion of fraud in one year. And it’s just getting started

Curated on Posted on by Stefaan Verhulst

Article by Matt Egan: “The federal government’s bet on using artificial intelligence to fight financial crime appears to be paying off.

Machine learning AI helped the US Treasury Department to sift through massive amounts of data and recover $1 billion worth of check fraud in fiscal 2024 alone, according to new estimates shared first with CNN. That’s nearly triple what the Treasury recovered in the prior fiscal year.

“It’s really been transformative,” Renata Miskell, a top Treasury official, told CNN in a phone interview.

“Leveraging data has upped our game in fraud detection and prevention,” Miskell said.

The Treasury Department credited AI with helping officials prevent and recover more than $4 billion worth of fraud overall in fiscal 2024, a six-fold spike from the year before.

US officials quietly started using AI to detect financial crime in late 2022, taking a page out of what many banks and credit card companies already do to stop bad guys.

The goal is to protect taxpayer money against fraud, which spiked during the Covid-19 pandemic as the federal government scrambled to disburse emergency aid to consumers and businesses.

To be sure, Treasury is not using generative AI, the kind that has captivated users of OpenAI’s ChatGPT and Google’s Gemini by generating images, crafting song lyrics and answering complex questions (even though it still sometimes struggles with simple queries)…(More)”.

External Researcher Access to Closed Foundation Models

Curated on Posted on by Stefaan Verhulst

Report by Esme Harrington and Dr. Mathias Vermeulen: “…addresses a pressing issue: independent researchers need better conditions for accessing and studying the AI models that big companies have developed. Foundation models — the core technology behind many AI applications — are controlled mainly by a few major players who decide who can study or use them.

What’s the problem with access?

  • Limited access: Companies like OpenAI, Google and others are the gatekeepers. They often restrict access to researchers whose work aligns with their priorities, which means independent, public-interest research can be left out in the cold.
  • High-end costs: Even when access is granted, it often comes with a hefty price tag that smaller or less-funded teams can’t afford.
  • Lack of transparency: These companies don’t always share how their models are updated or moderated, making it nearly impossible for researchers to replicate studies or fully understand the technology.
  • Legal risks: When researchers try to scrutinize these models, they sometimes face legal threats if their work uncovers flaws or vulnerabilities in the AI systems.

The research suggests that companies need to offer more affordable and transparent access to improve AI research. Additionally, governments should provide legal protections for researchers, especially when they are acting in the public interest by investigating potential risks…(More)”.

How Artificial Intelligence Can Support Peace

Curated on Posted on by Stefaan Verhulst

Essay by Adam Zable, Marine Ragnet, Roshni Singh, Hannah Chafetz, Andrew J. Zahuranec, and Stefaan G. Verhulst: “In what follows we provide a series of case studies of how AI can be used to promote peace, leveraging what we learned at the Kluz Prize for PeaceTech and NYU Prep and Becera events. These case studies and applications of AI are limited to what was included in these initiatives and are not fully comprehensive. With these examples of the role of technology before, during, and after a conflict, we hope to broaden the discussion around the potential positive uses of AI in the context of today’s global challenges.

Ai for Peace Blog GraphicThe table above summarizes the how AI may be harnessed throughout the conflict cycle and the supporting examples from the Kluz Prize for PeaceTech and NYU PREP and Becera events

(1) The Use of AI Before a Conflict

AI can support conflict prevention by predicting emerging tensions and supporting mediation efforts. In recent years, AI-driven early warning systems have been used to identify patterns that precede violence, allowing for timely interventions. 

For instance, The Violence & Impacts Early-Warning System (VIEWS), developed by a research consortium at Uppsala University in Sweden and the Peace Research Institute Oslo (PRIO) in Norway, employs AI and machine learning algorithms to analyze large datasets, including conflict history, political events, and socio-economic indicators—supporting negative peace and peacebuilding efforts. These algorithms are trained to recognize patterns that precede violent conflict, using both supervised and unsupervised learning methods to make predictions about the likelihood and severity of conflicts up to three years in advance. The system also uses predictive analytics to identify potential hotspots, where specific factors—such as spikes in political unrest or economic instability—suggest a higher risk of conflict…(More)”.

WikiProject AI Cleanup

Curated on Posted on by Stefaan Verhulst

Article by Emanuel Maiberg: “A group of Wikipedia editors have formed WikiProject AI Cleanup, “a collaboration to combat the increasing problem of unsourced, poorly-written AI-generated content on Wikipedia.”

The group’s goal is to protect one of the world’s largest repositories of information from the same kind of misleading AI-generated information that has plagued Google search results, books sold on Amazon, and academic journals.

“A few of us had noticed the prevalence of unnatural writing that showed clear signs of being AI-generated, and we managed to replicate similar ‘styles’ using ChatGPT,” Ilyas Lebleu, a founding member of WikiProject AI Cleanup, told me in an email. “Discovering some common AI catchphrases allowed us to quickly spot some of the most egregious examples of generated articles, which we quickly wanted to formalize into an organized project to compile our findings and techniques.”…(More)”.

Machines of Loving Grace

Curated on Posted on by Stefaan Verhulst

Essay by Dario Amodei: “I think and talk a lot about the risks of powerful AI. The company I’m the CEO of, Anthropic, does a lot of research on how to reduce these risks. Because of this, people sometimes draw the conclusion that I’m a pessimist or “doomer” who thinks AI will be mostly bad or dangerous. I don’t think that at all. In fact, one of my main reasons for focusing on risks is that they’re the only thing standing between us and what I see as a fundamentally positive future. I think that most people are underestimating just how radical the upside of AI could be, just as I think most people are underestimating how bad the risks could be.

In this essay I try to sketch out what that upside might look like—what a world with powerful AI might look like if everything goes right. Of course no one can know the future with any certainty or precision, and the effects of powerful AI are likely to be even more unpredictable than past technological changes, so all of this is unavoidably going to consist of guesses. But I am aiming for at least educated and useful guesses, which capture the flavor of what will happen even if most details end up being wrong. I’m including lots of details mainly because I think a concrete vision does more to advance discussion than a highly hedged and abstract one…(More)”.