Book by Kashmir Hill: “… was skeptical when she got a tip about a mysterious app called Clearview AI that claimed it could, with 99 percent accuracy, identify anyone based on just one snapshot of their face. The app could supposedly scan a face and, in just seconds, surface every detail of a person’s online life: their name, social media profiles, friends and family members, home address, and photos that they might not have even known existed. If it was everything it claimed to be, it would be the ultimate surveillance tool, and it would open the door to everything from stalking to totalitarian state control. Could it be true?
In this riveting account, Hill tracks the improbable rise of Clearview AI, helmed by Hoan Ton-That, an Australian computer engineer, and Richard Schwartz, a former Rudy Giuliani advisor, and its astounding collection of billions of faces from the internet. The company was boosted by a cast of controversial characters, including conservative provocateur Charles C. Johnson and billionaire Donald Trump backer Peter Thiel—who all seemed eager to release this society-altering technology on the public. Google and Facebook decided that a tool to identify strangers was too radical to release, but Clearview forged ahead, sharing the app with private investors, pitching it to businesses, and offering it to thousands of law enforcement agencies around the world.
Facial recognition technology has been quietly growing more powerful for decades. This technology has already been used in wrongful arrests in the United States. Unregulated, it could expand the reach of policing, as it has in China and Russia, to a terrifying, dystopian level.
Your Face Belongs to Us is a gripping true story about the rise of a technological superpower and an urgent warning that, in the absence of vigilance and government regulation, Clearview AI is one of many new technologies that challenge what Supreme Court Justice Louis Brandeis once called “the right to be let alone.”…(More)”.
Choosing AI’s Impact on the Future of Work
Article by Daron Acemoglu & Simon Johnson …“Too many commentators see the path of technology as inevitable. But the historical record is clear: technologies develop according to the vision and choices of those in positions of power. As we document in Power and Progress: Our 1,000-Year Struggle over Technology and Prosperity, when these choices are left entirely in the hands of a small elite, you should expect that group to receive most of the benefits, while everyone else bears the costs—potentially for a long time.
Rapid advances in AI threaten to eliminate many jobs, and not just those of writers and actors. Jobs with routine elements, such as in regulatory compliance or clerical work, and those that involve simple data collection, data summary, and writing tasks are likely to disappear.
But there are still two distinct paths that this AI revolution could take. One is the path of automation, based on the idea that AI’s role is to perform tasks as well as or better than people. Currently, this vision dominates in the US tech sector, where Microsoft and Google (and their ecosystems) are cranking hard to create new AI applications that can take over as many human tasks as possible.
The negative impact on people along the “just automate” path is easy to predict from prior waves of digital technologies and robotics. It was these earlier forms of automation that contributed to the decline of American manufacturing employment and the huge increase in inequality over the last four decades. If AI intensifies automation, we are very likely to get more of the same—a gap between capital and labor, more inequality between the professional class and the rest of the workers, and fewer good jobs in the economy….(More)”
Automating Empathy
Open Access Book by Andrew McStay: “We live in a world where artificial intelligence and intensive use of personal data has become normalized. Companies across the world are developing and launching technologies to infer and interact with emotions, mental states, and human conditions. However, the methods and means of mediating information about people and their emotional states are incomplete and problematic.
Automating Empathy offers a critical exploration of technologies that sense intimate dimensions of human life and the modern ethical questions raised by attempts to perform and simulate empathy. It traces the ascendance of empathic technologies from their origins in physiognomy and pathognomy to the modern day and explores technologies in nations with non-Western ethical histories and approaches to emotion, such as Japan. The book examines applications of empathic technologies across sectors such as education, policing, and transportation, and considers key questions of everyday use such as the integration of human-state sensing in mixed reality, the use of neurotechnologies, and the moral limits of using data gleaned through automated empathy. Ultimately, Automating Empathy outlines the key principles necessary to usher in a future where automated empathy can serve and do good…(More)”
Data Equity: Foundational Concepts for Generative AI
WEF Report: “This briefing paper focuses on data equity within foundation models, both in terms of the impact of Generative AI (genAI) on society and on the further development of genAI tools.
GenAI promises immense potential to drive digital and social innovation, such as improving efficiency, enhancing creativity and augmenting existing data. GenAI has the potential to democratize access and usage of technologies. However, left unchecked, it could deepen inequities. With the advent of genAI significantly increasing the rate at which AI is deployed and developed, exploring frameworks for data equity is more urgent than ever.
The goals of the briefing paper are threefold: to establish a shared vocabulary to facilitate collaboration and dialogue; to scope initial concerns to establish a framework for inquiry on which stakeholders can focus; and to shape future development of promising technologies.
The paper represents a first step in exploring and promoting data equity in the context of genAI. The proposed definitions, framework and recommendations are intended to proactively shape the development of promising genAI technologies…(More)”.
Artificial intelligence in government: Concepts, standards, and a unified framework
Paper by Vincent J. Straub, Deborah Morgan, Jonathan Bright, Helen Margetts: “Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI…(More)”.
Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
The White House: “Today, President Biden is issuing a landmark Executive Order to ensure that America leads the way in seizing the promise and managing the risks of artificial intelligence (AI). The Executive Order establishes new standards for AI safety and security, protects Americans’ privacy, advances equity and civil rights, stands up for consumers and workers, promotes innovation and competition, advances American leadership around the world, and more.
As part of the Biden-Harris Administration’s comprehensive strategy for responsible innovation, the Executive Order builds on previous actions the President has taken, including work that led to voluntary commitments from 15 leading companies to drive safe, secure, and trustworthy development of AI…(More)”.
AI is Like… A Literature Review of AI Metaphors and Why They Matter for Policy
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
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
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
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)”.