AI is Like… A Literature Review of AI Metaphors and Why They Matter for Policy

Curated on Posted on by Stefaan Verhulst

Paper by Matthijs M. Maas: “As AI systems have become increasingly capable and impactful, there has been significant public and policymaker debate over this technology’s impacts—and the appropriate legal or regulatory responses. Within these debates many have deployed—and contested—a dazzling range of analogies, metaphors, and comparisons for AI systems, their impact, or their regulation.

This report reviews why and how metaphors matter to both the study and practice of AI governance, in order to contribute to more productive dialogue and more reflective policymaking. It first reviews five stages at which different foundational metaphors play a role in shaping the processes of technological innovation, the academic study of their impacts; the regulatory agenda, the terms of the policymaking process, and legislative and judicial responses to new technology. It then surveys a series of cases where the choice of analogy materially influenced the regulation of internet issues, as well as (recent) AI law issues. The report then provides a non-exhaustive survey of 55 analogies that have been given for AI technology, and some of their policy implications. Finally, it discusses the risks of utilizing unreflexive analogies in AI law and regulation.

By disentangling the role of metaphors and frames in these debates, and the space of analogies for AI, this survey does not aim to argue against the use or role of analogies in AI regulation—but rather to facilitate more reflective and productive conversations on these timely challenges…(More)”.

AI Adoption in America: Who, What, and Where

Curated on Posted on by Stefaan Verhulst

Paper by Kristina McElheran: “…We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more-educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups’ adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist…(More)”.

How language gaps constrain generative AI development

Curated on Posted on by Stefaan Verhulst

Article by Regina Ta and Nicol Turner Lee: “Prompt-based generative artificial intelligence (AI) tools are quickly being deployed for a range of use cases, from writing emails and compiling legal cases to personalizing research essays in a wide range of educational, professional, and vocational disciplines. But language is not monolithic, and opportunities may be missed in developing generative AI tools for non-standard languages and dialects. Current applications often are not optimized for certain populations or communities and, in some instances, may exacerbate social and economic divisions. As noted by the Austrian linguist and philosopher Ludwig Wittgenstein, “The limits of my language mean the limits of my world.” This is especially true today, when the language we speak can change how we engage with technology, and the limits of our online vernacular can constrain the full and fair use of existing and emerging technologies.

As it stands now, the majority of the world’s speakers are being left behind if they are not part of one of the world’s dominant languages, such as English, French, German, Spanish, Chinese, or Russian. There are over 7,000 languages spoken worldwide, yet a plurality of content on the internet is written in English, with the largest remaining online shares claimed by Asian and European languages like Mandarin or Spanish. Moreover, in the English language alone, there are over 150 dialects beyond “standard” U.S. English. Consequently, large language models (LLMs) that train AI tools, like generative AI, rely on binary internet data that serve to increase the gap between standard and non-standard speakers, widening the digital language divide.

Among sociologists, anthropologists, and linguists, language is a source of power and one that significantly influences the development and dissemination of new tools that are dependent upon learned, linguistic capabilities. Depending on where one sits within socio-ethnic contexts, native language can internally strengthen communities while also amplifying and replicating inequalities when coopted by incumbent power structures to restrict immigrant and historically marginalized communities. For example, during the transatlantic slave trade, literacy was a weapon used by white supremacists to reinforce the dependence of Blacks on slave masters, which resulted in many anti-literacy laws being passed in the 1800s in most Confederate states…(More)”.

The Future of AI Is GOMA

Curated on Posted on by Stefaan Verhulst

Article by Matteo Wong: “A slate of four AI companies might soon rule Silicon Valley…Chatbots and their ilk are still in their early stages, but everything in the world of AI is already converging around just four companies. You could refer to them by the acronym GOMA: Google, OpenAI, Microsoft, and Anthropic. Shortly after OpenAI released ChatGPT last year, Microsoft poured $10 billion into the start-up and shoved OpenAI-based chatbots into its search engine, Bing. Not to be outdone, Google announced that more AI features were coming to Search, Maps, Docs, and more, and introduced Bard, its own rival chatbot. Microsoft and Google are now in a race to integrate generative AI into just about everything. Meanwhile, Anthropic, a start-up launched by former OpenAI employees, has raised billions of dollars in its own right, including from Google. Companies such as Slack, Expedia, Khan Academy, Salesforce, and Bain are integrating ChatGPT into their products; many others are using Anthropic’s chatbot, Claude. Executives from GOMA have also met with leaders and officials around the world to shape the future of AI’s deployment and regulation. The four have overlapping but separate proposals for AI safety and regulation, but they have joined together to create the Frontier Model Forum, a consortium whose stated mission is to protect against the supposed world-ending dangers posed by terrifyingly capable models that do not yet exist but, it warns, are right around the corner. That existential language—about bioweapons and nuclear robots—has since migrated its way into all sorts of government proposals and language. If AI is truly reshaping the world, these companies are the sculptors…”…(More)”.

Policy brief: Generative AI

Curated on Posted on by Stefaan Verhulst

Policy Brief by Ann Kristin Glenster, and Sam Gilbert: “The rapid rollout of generative AI models, and public attention to Open AI’s ChatGPT, has raised concerns about AI’s impact on the economy and society. In the UK, policy-makers are looking to large language models and other so-called foundation models as ways to potentially improve economic productivity.

This policy brief outlines which policy levers could support those goals. The authors argue that the UK should pursue becoming a global leader in applying generative AI to the economy. Rather than use public support for building new foundation models, the UK could support the growing ecosystem of startups that develop new applications for these models, creating new products and services.

This policy brief answers three key questions:

  1. What policy infrastructure and social capacity does the UK need to lead and manage deployment of responsible generative AI (over the long term)?
  2. What national capability does the UK need for large-scale AI systems in the short- and medium-term?
  3. What governance capacity does the UK need to deal with fast moving technologies, in which large uncertainties are a feature, not a bug?…(More)”.

Towards an Inclusive Data Governance Policy for the Use of Artificial Intelligence in Africa

Curated on Posted on by Stefaan Verhulst

Paper by Jake Okechukwu Effoduh, Ugochukwu Ejike Akpudo and Jude Dzevela Kong: “This paper proposes five ideas that the design of data governance policies for the inclusive use of artificial intelligence (AI) in Africa should consider. The first is for African states to carry out an assessment of their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance which involves data processing practices that protect security of personal data and privacy of data subjects; ensures that personal data is processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity and Personal Data Protection. The fourth is for states to be critical about the extent that AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensuring a transparent procurement process for AI systems from abroad where no local options exist…(More)”

Setting Democratic Ground Rules for AI: Civil Society Strategies

Curated on Posted on by Stefaan Verhulst

Report by Beth Kerley: “…analyzes priorities, challenges, and promising civil society strategies for advancing democratic approaches to governing artificial intelligence (AI). The report is based on conversations from a private Forum workshop in Buenos Aires, Argentina that brought together Latin American and global researchers and civil society practitioners.

With recent leaps in the development of AI, we are experiencing a seismic shift in the balance of power between people and governments, posing new challenges to democratic principles such as privacy, transparency, and non-discrimination. We know that AI will shape the political world we inhabit–but how can we ensure that democratic norms and institutions shape the trajectory of AI?

Drawing on global civil society perspectives, this report surveys what stakeholders need to know about AI systems and the human relationships behind them. It delves into the obstacles– from misleading narratives to government opacity to gaps in technical expertise–that hinder democratic engagement on AI governance, and explores how new thinking, new institutions, and new collaborations can better equip societies to set democratic ground rules for AI technologies…(More)”.

Our Planet Powered by AI: How We Use Artificial Intelligence to Create a Sustainable Future for Humanity

Curated on Posted on by Stefaan Verhulst

Book by Mark Minevich: “…You’ll learn to create sustainable, effective competitive advantage by introducing previously unheard-of levels of adaptability, resilience, and innovation into your company.

Using real-world case studies from a variety of well-known industry leaders, the author explains the strategic archetypes, technological infrastructures, and cultures of sustainability you’ll need to ensure your firm’s next-level digital transformation takes root. You’ll also discover:

  • How AI can enable new business strategies, models, and ecosystems of innovation and growth
  • How to develop societal impact and powerful organizational benefits with ethical AI implementations that incorporate transparency, fairness, privacy, and reliability
  • What it means to enable all-inclusive artificial intelligence

An engaging and hands-on exploration of how to take your firm to new levels of dynamism and growth, Our Planet Powered by AI will earn a place in the libraries of managers, executives, directors, and other business and technology leaders seeking to distinguish their companies in a new age of astonishing technological advancement and fierce competition….(More)”.

Generative AI is set to transform crisis management

Curated on Posted on by Stefaan Verhulst

Article by Ben Ellencweig, Mihir Mysore, Jon Spaner: “…Generative AI presents transformative potential, especially in disaster preparedness and response, and recovery. As billion-dollar disasters become more frequent – “billion-dollar disasters” typically costing the U.S. roughly $120 billion each – and “polycrises”, or multiple crises at once proliferate (e.g. hurricanes combined with cyber disruptions), the significant impact that Generative AI can have, especially with proper leadership focus, is a focal point of interest.

Generative AI’s speed is crucial in emergencies, as it enhances information access, decision-making capabilities, and early warning systems. Beyond organizational benefits for those who adopt Generative AI, its applications include real-time data analysis, scenario simulations, sentiment analysis, and simplifying complex information access. Generative AI’s versatility offers a wide variety of promising applications in disaster relief, and opens up facing real time analyses with tangible applications in the real world. 

Early warning systems and sentiment analysis: Generative AI excels in early warning systems and sentiment analysis, by scanning accurate real-time data and response clusters. By enabling connections between disparate systems, Generative AI holds the potential to provide more accurate early warnings. Integrated with traditional and social media, Generative AI can also offer precise sentiment analysis, empowering leaders to understand public sentiment, detect bad actors, identify misinformation, and tailor communications for accurate information dissemination.

Scenario simulations: Generative AI holds the potential to enhance catastrophe modeling for better crisis assessment and resource allocation. It creates simulations for emergency planners, improving modeling for various disasters (e.g., hurricanes, floods, wildfires) using historical data such as location, community impact, and financial consequence. Often, simulators perform work “so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane).” …(More)”

When is a Decision Automated? A Taxonomy for a Fundamental Rights Analysis

Curated on Posted on by Stefaan Verhulst

Paper by Francesca Palmiotto: “This paper addresses the pressing issues surrounding the use of automated systems in public decision-making, with a specific focus on the field of migration, asylum, and mobility. Drawing on empirical research conducted for the AFAR project, the paper examines the potential and limitations of the General Data Protection Regulation and the proposed Artificial Intelligence Act in effectively addressing the challenges posed by automated decision making (ADM). The paper argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications, where automated systems assist human decision-makers rather than replace them entirely. This discrepancy between the legal framework and practical implementation highlights the need for a fundamental rights approach to legal protection in the automation age. To bridge the gap between ADM in law and practice, the paper proposes a taxonomy that provides theoretical clarity and enables a comprehensive understanding of ADM in public decision-making. This taxonomy not only enhances our understanding of ADM but also identifies the fundamental rights at stake for individuals and the sector-specific legislation applicable to ADM. The paper finally calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society…(More)”.