The Routledge Handbook of Artificial Intelligence and Philanthropy

Curated on Posted on by Stefaan Verhulst

Open Access Book edited by Giuseppe Ugazio and Milos Maricic: “…acts as a catalyst for the dialogue between two ecosystems with much to gain from collaboration: artificial intelligence (AI) and philanthropy. Bringing together leading academics, AI specialists, and philanthropy professionals, it offers a robust academic foundation for studying both how AI can be used and implemented within philanthropy and how philanthropy can guide the future development of AI in a responsible way.

The contributors to this Handbook explore various facets of the AI‑philanthropy dynamic, critically assess hurdles to increased AI adoption and integration in philanthropy, map the application of AI within the philanthropic sector, evaluate how philanthropy can and should promote an AI that is ethical, inclusive, and responsible, and identify the landscape of risk strategies for their limitations and/or potential mitigation. These theoretical perspectives are complemented by several case studies that offer a pragmatic perspective on diverse, successful, and effective AI‑philanthropy synergies.

As a result, this Handbook stands as a valuable academic reference capable of enriching the interactions of AI and philanthropy, uniting the perspectives of scholars and practitioners, thus building bridges between research and implementation, and setting the foundations for future research endeavors on this topic…(More)”.

Annoyed Redditors tanking Google Search results illustrates perils of AI scrapers

Curated on Posted on by Stefaan Verhulst

Article by Scharon Harding: “A trend on Reddit that sees Londoners giving false restaurant recommendations in order to keep their favorites clear of tourists and social media influencers highlights the inherent flaws of Google Search’s reliance on Reddit and Google’s AI Overview.

In May, Google launched AI Overviews in the US, an experimental feature that populates the top of Google Search results with a summarized answer based on an AI model built into Google’s web rankings. When Google first debuted AI Overview, it quickly became apparent that the feature needed work with accuracy and its ability to properly summarize information from online sources. AI Overviews are “built to only show information that is backed up by top web results,” Liz Reid, VP and head of Google Search, wrote in a May blog post. But as my colleague Benj Edwards pointed out at the time, that setup could contribute to inaccurate, misleading, or even dangerous results: “The design is based on the false assumption that Google’s page-ranking algorithm favors accurate results and not SEO-gamed garbage.”

As Edwards alluded to, many have complained about Google Search results’ quality declining in recent years, as SEO spam and, more recently, AI slop float to the top of searches. As a result, people often turn to the Reddit hack to make Google results more helpful. By adding “site:reddit.com” to search results, users can hone their search to more easily find answers from real people. Google seems to understand the value of Reddit and signed an AI training deal with the company that’s reportedly worth $60 million per year…(More)”.

South Korea leverages open government data for AI development

Curated on Posted on by Stefaan Verhulst

Article by Si Ying Thian: “In South Korea, open government data is powering artificial intelligence (AI) innovations in the private sector.

Take the case of TTCare which may be the world’s first mobile application to analyse eye and skin disease symptoms in pets.

AI Hub allows users to search by industry, data format and year (top row), with the data sets made available based on the particular search term “pet” (bottom half of the page). Image: AI Hub, provided by courtesy of Baek

The AI model was trained on about one million pieces of data – half of the data coming from the government-led AI Hub and the rest collected by the firm itself, according to the Korean newspaper Donga.

AI Hub is an integrated platform set up by the government to support the country’s AI infrastructure.

TTCare’s CEO Heo underlined the importance of government-led AI training data in improving the model’s ability to diagnose symptoms. The firm’s training data is currently accessible through AI Hub, and any Korean citizen can download or use it.

Pushing the boundaries of open data

Over the years, South Korea has consistently come up top in the world’s rankings for Open, Useful, and Re-usable data (OURdata) Index.

The government has been pushing the boundaries of what it can do with open data – beyond just making data usable by providing APIs. Application Programming Interfaces, or APIs, make it easier for users to tap on open government data to power their apps and services.

There is now rising interest from public sector agencies to tap on such data to train AI models, said South Korea’s National Information Society Agency (NIA)’s Principal Manager, Dongyub Baek, although this is still at an early stage.

Baek sits in NIA’s open data department, which handles policies, infrastructure such as the National Open Data Portal, as well as impact assessments of the government initiatives…(More)”

Trust in artificial intelligence makes Trump/Vance a transhumanist ticket

Curated on Posted on by Stefaan Verhulst

Article by Filip Bialy: “AI plays a central role in the 2024 US presidential election, as a tool for disinformation and as a key policy issue. But its significance extends beyond these, connecting to an emerging ideology known as TESCREAL, which envisages AI as a catalyst for unprecedented progress, including space colonisation. After this election, TESCREALism may well have more than one representative in the White House, writes Filip Bialy

In June 2024, the essay Situational Awareness by former OpenAI employee Leopold Aschenbrenner sparked intense debate in the AI community. The author predicted that by 2027, AI would surpass human intelligence. Such claims are common among AI researchers. They often assert that only a small elite – mainly those working at companies like OpenAI – possesses inside knowledge of the technology. Many in this group hold a quasi-religious belief in the imminent arrival of artificial general intelligence (AGI) or artificial superintelligence (ASI)…

These hopes and fears, however, are not only religious-like but also ideological. A decade ago, Silicon Valley leaders were still associated with the so-called Californian ideology, a blend of hippie counterculture and entrepreneurial yuppie values. Today, figures like Elon Musk, Mark Zuckerberg, and Sam Altman are under the influence of a new ideological cocktail: TESCREAL. Coined in 2023 by Timnit Gebru and Émile P. Torres, TESCREAL stands for Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism.

While these may sound like obscure terms, they represent ideas developed over decades, with roots in eugenics. Early 20th-century eugenicists such as Francis Galton promoted selective breeding to enhance future generations. Later, with advances in genetic engineering, the focus shifted from eugenics’ racist origins to its potential to eliminate genetic defects. TESCREAL represents a third wave of eugenics. It aims to digitise human consciousness and then propagate digital humans into the universe…(More)”

Open-Access AI: Lessons From Open-Source Software

Curated on Posted on by Stefaan Verhulst

Article by Parth Nobel, Alan Z. Rozenshtein, Chinmayi Sharma: “Before analyzing how the lessons of open-source software might (or might not) apply to open-access AI, we need to define our terms and explain why we use the term “open-access AI” to describe models like Llama rather than the more commonly used “open-source AI.” We join many others in arguing that “open-source AI” is a misnomer for such models. It’s misleading to fully import the definitional elements and assumptions that apply to open-source software when talking about AI. Rhetoric matters, and the distinction isn’t just semantic; it’s about acknowledging the meaningful differences in access, control, and development. 

The software industry definition of “open source” grew out of the free software movement, which makes the point that “users have the freedom to run, copy, distribute, study, change and improve” software. As the movement emphasizes, one should “think of ‘free’ as in ‘free speech,’ not as in ‘free beer.’” What’s “free” about open-source software is that users can do what they want with it, not that they initially get it for free (though much open-source software is indeed distributed free of charge). This concept is codified by the Open Source Initiative as the Open Source Definition (OSD), many aspects of which directly apply to Llama 3.2. Llama 3.2’s license makes it freely redistributable by license holders (Clause 1 of the OSD) and allows the distribution of the original models, their parts, and derived works (Clauses 3, 7, and 8). ..(More)”.

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