The past few decades have been marked by the ever-increasing presence of digital technology. This growth, often called digital transformation, places a heavy burden on our environment. We are now facing a potential new phase of digital transformation,6 represented by the emergence of generative AI (GenAI), a subfield of artificial intelligence focused on generating content, such as human-like text, code, and images.14 In particular, the deployment of GenAI as a service, such as ChatGPT or Stable Diffusion, is raising questions around sustainability. The sustainability of any computing technology, however, cannot be addressed without a way to evaluate its environmental impact.

The new essay collection "Searches: Selfhood in the Digital Age," by Vauhini Vara, opens with a transcript. "If I paste some writing here, can we talk about it?" Her interlocutor, the large language model ChatGPT, responds, "Of course!" The chatbot asks what specific themes it should focus on. "Nothing in particular," Vara replies.

In an increasingly digital world, connectivity is a necessity. Yet, nearly a third of the global population remains offline, unable to access the services vital to participating in our global digital economy and society. The Edison Alliance at the World Economic Forum has worked to change that by delivering digital connectivity and access to financial, healthcare, and education services to those who need them most. Our partnerships with governments, industries, and non-governmental organizations drive lasting systemic change. The World Economic Forum played a pivotal role in launching and guiding the Alliance's work, providing a platform for stakeholders to come together and commit to a vision with actionable ideas and plans.

The landscape of digital servitization in the manufacturing sector is evolving, marked by a strategic shift from traditional product-centric to platform business models (BMs). Manufacturing firms often employ a blend of approaches to develop business-to-business (B2B) platforms, leading to significant reconfigurations in their BMs. However, they frequently encounter failures in their B2B platform development initiatives, leading them to abandon initial efforts and pivot to alternative platform strategies. Therefore, this study, through an in-depth case study of a manufacturer in the energy sector, articulates a three-phase pivoting framework for B2B platform BMs, including platform development and platform strategy. Initially, the manufacturer focused on asset-based product sales supplemented by asset maintenance services and followed an emergent platformization strategy characterized by the rise of multiple, independent B2B platforms catering to diverse functions. Next, focusing on the imposed customer journey strategy, the firm shifted towards a strategic multi-platform integration into an all-encompassing platform supported by artificial intelligence (AI), signaling a maturation of the platform BM to combine a wide range of services into an energy-performance-based contract. Finally, the last step of the firm's platform BM evolution consisted of a deliberate platform strategy open to external stakeholders and enveloping its data-driven offerings within a broader platform ecosystem. This article advances B2B platform BMs and digital servitization literature, highlighting the efficacy of a progressive approach and strategic pivoting.

This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digital citizenship, the chapter examines how AI can improve online engagement and promote ethical behaviour while posing privacy risks and fostering identity stereotyping. Regarding political participation, it highlights AI's dual role in mobilising civic actions and spreading misinformation. Regarding representation, AI's involvement in electoral processes can enhance voter registration, e-voting, and the efficiency of result tabulation but raises concerns regarding privacy and public trust. Also, AI's predictive capabilities shift the dynamics of political competition, posing ethical questions about manipulation and the legitimacy of democracy. Finally, the chapter examines how integrating AI and digital technologies can facilitate democratic political advocacy and personalised communication. However, this also comes with higher risks of misinformation and targeted propaganda.

As world leaders gather in New York City for the United Nations General Assembly, there's a lot to get done, with just six years left to achieve the bold ambitions laid out for the world's 2030 agenda. When world governments agreed to the 2030 plan back in 2015, a decade and a half seemed like plenty of time to achieve the 17 Sustainable Development Goals (SDGs) designed to create a more prosperous, safe and fair global society. While amazing progress has been made, we are in danger of falling short. I believe the U.N.'s goals can be attained through a collaborative commitment to make digital networks available to everybody in the world. Mobility, broadband and the cloud are the infrastructure of 21st century life and everybody should have that opportunity.

Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design.

Audrey Tang, Taiwan's 43-year-old minister of digital affairs, has a powerful effect on people. At a panel discussion at Northeastern University in Boston, 20-year-old student Diane Grant is visibly moved, describing Tang's talk as the best she's been to in her undergraduate career. Later that day, a German tourist recognizes Tang leaving the Boston Museum of Science and requests a photo, saying she's "starstruck." At the Massachusetts Institute of Technology, a trio of world-leading economists bashfully ask Tang to don a baseball cap emblazoned with the name of their research center and pose for a group photo. Political scientist and former gubernatorial candidate Danielle Allen, confesses to Tang that, although others often tell her that she is a source of inspiration to them, she rarely feels inspired by others.

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-created content and ability of LLMs to answer questions, it is not well understood how unclear or nonstandard language queries affect the model outputs. We propose the creation of a dataset that captures questions of digital newcomers and outsiders, utilizing data we have compiled from a decade's worth of one-on-one tutoring. In this paper we lay out our planned efforts and some potential uses of this dataset.

Hush-hush meetings, often never made public. For centuries, the art of conflict mediation has relied on nuanced human skills: from elements as simple as how to make eye contact and listen carefully to detecting shifts in emotions and subtle signals from opponents. Now, a growing set of entrepreneurs and experts are pitching a dramatic new set of tools into the world of dispute resolution – relying increasingly on artificial intelligence (AI). "Groundbreaking technological advancements are revolutionising the frontier of peace and mediation," said Sama al-Hamdani, programme director of Hala System, a private company using AI and data analysis to gather unencrypted intelligence in conflict zones, among other war-related tasks. "We are witnessing an era where AI transforms mediators into powerhouses of efficiency and insight," al-Hamdani said.