The New Fire: War, Peace, and Democracy in the Age of AI


Book by Ben Buchanan and Andrew Imbrie: “Artificial intelligence is revolutionizing the modern world. It is ubiquitous—in our homes and offices, in the present and most certainly in the future. Today, we encounter AI as our distant ancestors once encountered fire. If we manage AI well, it will become a force for good, lighting the way to many transformative inventions. If we deploy it thoughtlessly, it will advance beyond our control. If we wield it for destruction, it will fan the flames of a new kind of war, one that holds democracy in the balance. As AI policy experts Ben Buchanan and Andrew Imbrie show in The New Fire, few choices are more urgent—or more fascinating—than how we harness this technology and for what purpose.

The new fire has three sparks: data, algorithms, and computing power. These components fuel viral disinformation campaigns, new hacking tools, and military weapons that once seemed like science fiction. To autocrats, AI offers the prospect of centralized control at home and asymmetric advantages in combat. It is easy to assume that democracies, bound by ethical constraints and disjointed in their approach, will be unable to keep up. But such a dystopia is hardly preordained. Combining an incisive understanding of technology with shrewd geopolitical analysis, Buchanan and Imbrie show how AI can work for democracy. With the right approach, technology need not favor tyranny…(More)”.

AI-Powered Urban Innovations Bring Promise, Risk to Future Cities


Article by Anthony Townsend and Hubert Beroche: “Red lights are obsolete. That seems to be the thinking behind Google’s latest fix for cities, which rolled out late last year in a dozen cities around the world, from Seattle to Jakarta. Most cities still collect the data to determine the timing of traffic signals by hand. But Project Green Light replaced clickers and clipboards with mountains of location data culled from smartphones. Artificial intelligence crunched the numbers, adjusting the signal pattern to smooth the flow of traffic. Motorists saw 30% fewer delays. There’s just one catch. Even as pedestrian deaths in the US reached a 40-year high in 2022, Google engineers omitted pedestrians and cyclists from their calculations.

Google’s oversight threatens to undo a decade of progress on safe streets and is a timely reminder of the risks in store when AI invades the city. Mayors across global cities have embraced Vision Zero pledges to eliminate pedestrian deaths. They are trying to slow traffic down, not speed it up. But Project Green Light’s website doesn’t even mention road safety. Still, the search giant’s experiment demonstrates AI’s potential to help cities. Tailpipe greenhouse gas emissions at intersections fell by 10%. Imagine what AI could do if we used it to empower people in cities rather than ignore them.

Take the technocratic task of urban planning and the many barriers to participation it creates. The same technology that powers chatbots and deepfakes is rapidly bringing down those barriers. Real estate developers have mastered the art of using glossy renderings to shape public opinion. But UrbanistAI, a tool developed by Helsinki-based startup SPIN Unit and the Milanese software company Toretei, puts that power in the hands of residents: It uses generative AI to transform text prompts into photorealistic images of alternative designs for controversial projects. Another startup, the Barcelona-based Aino, wraps a chatbot around a mapping tool. Using such computer aids, neighborhood activists no longer need to hire a data scientist to produce maps from census data to make their case…(More)”.

Artificial Intelligence: A Threat to Climate Change, Energy Usage and Disinformation


Press Release: “Today, partners in the Climate Action Against Disinformation coalition released a report that maps the risks that artificial intelligence poses to the climate crisis.

Topline points:

  • AI systems require an enormous amount of energy and water, and consumption is expanding quickly. Estimates suggest a doubling in 5-10 years.
  • Generative AI has the potential to turbocharge climate disinformation, including climate change-related deepfakes, ahead of a historic election year where climate policy will be central to the debate. 
  • The current AI policy landscape reveals a concerning lack of regulation on the federal level, with minor progress made at the state level, relying on voluntary, opaque and unenforceable pledges to pause development, or provide safety with its products…(More)”.

A World Divided Over Artificial Intelligence


Article by Aziz Huq: “…Through multinational communiqués and bilateral talks, an international framework for regulating AI does seem to be coalescing. Take a close look at U.S. President Joe Biden’s October 2023 executive order on AI; the EU’s AI Act, which passed the European Parliament in December 2023 and will likely be finalized later this year; or China’s slate of recent regulations on the topic, and a surprising degree of convergence appears. They have much in common. These regimes broadly share the common goal of preventing AI’s misuse without restraining innovation in the process. Optimists have floated proposals for closer international management of AI, such as the ideas presented in Foreign Affairs by the geopolitical analyst Ian Bremmer and the entrepreneur Mustafa Suleyman and the plan offered by Suleyman and Eric Schmidt, the former CEO of Google, in the Financial Times in which they called for the creation of an international panel akin to the UN’s Intergovernmental Panel on Climate Change to “inform governments about the current state of AI capabilities and make evidence-based predictions about what’s coming.”

But these ambitious plans to forge a new global governance regime for AI may collide with an unfortunate obstacle: cold reality. The great powers, namely, China, the United States, and the EU, may insist publicly that they want to cooperate on regulating AI, but their actions point toward a future of fragmentation and competition. Divergent legal regimes are emerging that will frustrate any cooperation when it comes to access to semiconductors, the setting of technical standards, and the regulation of data and algorithms. This path doesn’t lead to a coherent, contiguous global space for uniform AI-related rules but to a divided landscape of warring regulatory blocs—a world in which the lofty idea that AI can be harnessed for the common good is dashed on the rocks of geopolitical tensions…(More)”.

Synthetic Data and the Future of AI


Paper by Peter Lee: “The future of artificial intelligence (AI) is synthetic. Several of the most prominent technical and legal challenges of AI derive from the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real-world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emergence of a seemingly paradoxical technical creation that can mitigate—though not completely eliminate—these concerns: synthetic data. Increasingly, data scientists are using simulated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is being used to train artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, sidestep privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promise, however, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm.

In light of the enormous value and importance of synthetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible development. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality synthetic data, encourage disclosure of both synthetic data and processes for generating it, and promote multiple sources of innovation. This Article then examines a suite of “innovation mechanisms” that can advance these objectives, ranging from open source production to proprietary approaches based on patents, trade secrets, and copyrights. Throughout, it suggests policy and doctrinal reforms to enhance innovation, transparency, and democratic access to synthetic data. Just as AI will have enormous legal implications, law and policy can play a central role in shaping the future of AI…(More)”.

Prompting Diverse Ideas: Increasing AI Idea Variance


Paper by Lennart Meincke, Ethan Mollick, and Christian Terwiesch: “Unlike routine tasks where consistency is prized, in creativity and innovation the goal is to create a diverse set of ideas. This paper delves into the burgeoning interest in employing Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process. While previous studies have found that the average quality of AI ideas is quite high, prior research also has pointed to the inability of AI-based brainstorming to create sufficient dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and the speed with which the idea space gets exhausted. We do this in the domain of developing a new product development for college students, priced under $50. In this context, we find that (1) pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects (2) the diversity of AI generated ideas can be substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the highest diversity of ideas of all prompts we evaluated and was able to come close to what is achieved by groups of human subjects. It also was capable of generating the highest number of unique ideas of any prompt we studied…(More)”

Trust in AI companies drops to 35 percent in new study


Article by Filip Timotija: “Trust in artificial intelligence (AI) companies has dipped to 35 percent over a five-year period in the U.S., according to new data.

The data, released Tuesday by public relations firm Edelman, found that trust in AI companies also dropped globally by eight points, going from 61 percent to 53 percent. 

The dwindling confidence in the rapidly-developing tech industry comes as regulators in the U.S. and across the globe are brainstorming solutions on how to regulate the sector. 

When broken down my political party, researchers found Democrats showed the most trust in AI companies at 38 percent — compared to Republicans’ 24 percent and independents’ 25 percent, per the study.

Multiple factors contributed to the decline in trust toward the companies polled in the data, according to Justin Westcott, Edelman’s chair of global technology.

“Key among these are fears related to privacy invasion, the potential for AI to devalue human contributions, and apprehensions about unregulated technological leaps outpacing ethical considerations,” Westcott said, adding “the data points to a perceived lack of transparency and accountability in how AI companies operate and engage with societal impacts.”

Technology as a whole is losing its lead in trust among sectors, Edelman said, highlighting the key findings from the study.

“Eight years ago, technology was the leading industry in trust in 90 percent of the countries we study,” researchers wrote, referring to the 28 countries. “Now it is most trusted only in half.”

Westcott argued the findings should be a “wake up call” for AI companies to “build back credibility through ethical innovation, genuine community engagement and partnerships that place people and their concerns at the heart of AI developments.”

As for the impacts on the future for the industry as a whole, “societal acceptance of the technology is now at a crossroads,” he said, adding that trust in AI and the companies producing it should be seen “not just as a challenge, but an opportunity.”

Priorities, Westcott continued, should revolve around ethical practices, transparency and a “relentless focus” on the benefits to society AI can provide…(More)”.

The AI data scraping challenge:  How can we proceed responsibly?


Article by Lee Tiedrich: “Society faces an urgent and complex artificial intelligence (AI) data scraping challenge.  Left unsolved, it could threaten responsible AI innovation.  Data scraping refers to using web crawlers or other means to obtain data from third-party websites or social media properties.  Today’s large language models (LLMs) depend on vast amounts of scraped data for training and potentially other purposes.  Scraped data can include facts, creative content, computer code, personal information, brands, and just about anything else.  At least some LLM operators directly scrape data from third-party sites.  Common CrawlLAION, and other sites make scraped data readily accessible.  Meanwhile, Bright Data and others offer scraped data for a fee. 

In addition to fueling commercial LLMs, scraped data can provide researchers with much-needed data to advance social good.  For instance, Environmental Journal explains how scraped data enhances sustainability analysis.  Nature reports that scraped data improves research about opioid-related deaths.  Training data in different languages can help make AI more accessible for users in Africa and other underserved regions.  Access to training data can even advance the OECD AI Principles by improving safety and reducing bias and other harms, particularly when such data is suitable for the AI system’s intended purpose…(More)”.

Evaluating LLMs Through a Federated, Scenario-Writing Approach


Article by Bogdana “Bobi” Rakova: “What do screenwriters, AI builders, researchers, and survivors of gender-based violence have in common? I’d argue they all imagine new, safe, compassionate, and empowering approaches to building understanding.

In partnership with Kwanele South Africa, I lead an interdisciplinary team, exploring this commonality in the context of evaluating large language models (LLMs) — more specifically, chatbots that provide legal and social assistance in a critical context. The outcomes of our engagement are a series of evaluation objectives and scenarios that contribute to an evaluation protocol with the core tenet that when we design for the most vulnerable, we create better futures for everyone. In what follows I describe our process. I hope this methodological approach and our early findings will inspire other evaluation efforts to meaningfully center the margins in building more positive futures that work for everyone…(More)”

Generative AI: Navigating Intellectual Property


Factsheet by WIPO: “Generative artificial intelligence (AI) tools are rapidly being adopted by many businesses and organizations for the purpose of content generation. Such tools represent both a substantial opportunity to assist business operations and a significant legal risk due to current uncertainties, including intellectual property (IP) questions.

Many organizations are seeking to put guidance in place to help their employees mitigate these risks. While each business situation and legal context will be unique, the following Guiding Principles and Checklist are intended to assist organizations in understanding the IP risks, asking the right questions, and considering potential safeguards…(More)”.