Paper by Gillian K. Hadfield, and Jack Clark: “Appropriately regulating artificial intelligence is an increasingly urgent policy challenge. Legislatures and regulators lack the specialized knowledge required to best translate public demands into legal requirements. Overreliance on industry self-regulation fails to hold producers and users of AI systems accountable to democratic demands. Regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a private regulator, are proposed. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation. Regulatory market could enable governments to establish policy priorities for the regulation of AI, whilst relying on market forces and industry R&D efforts to pioneer the methods of regulation that best achieve policymakers’ stated objectives…(More)”.
Tab the lab: A typology of public sector innovation labs
Paper by Aline Stoll and Kevin C Andermatt: “Many public sector organizations set up innovation laboratories in response to the pressure to tackle societal problems and the high expectations placed on them to innovate public services. Our understanding of the public sector innovation laboratories’ role in enhancing the innovation capacity of administrations is still limited. It is challenging to assess or compare the impact of innovation laboratories because of how they operate and what they do. This paper closes this research gap by offering a typology that organizes the diverse nature of innovation labs and makes it possible to compare various lab settings. The proposed typology gives possible relevant factors to increase the innovation capacity of public organizations. The findings are based on a literature review of primarily explorative papers and case studies, which made it possible to identify the relevant criteria. The proposed typology covers three dimensions: (1) value (intended innovation impact of the labs); (2) governance (role of government and financing model); and (3) network (stakeholders in the collaborative arrangements). Comparing European countries and regions with regards to the repartition of labs shows that Nordic and British countries tend to have broader scope than continental European countries…(More)”.
On Privacy and Technology
Book by Daniel J. Solove: “With the rapid rise of new digital technologies and artificial intelligence, is privacy dead? Can anything be done to save us from a dystopian world without privacy?
In this short and accessible book, internationally renowned privacy expert Daniel J. Solove draws from a range of fields, from law to philosophy to the humanities, to illustrate the profound changes technology is wreaking upon our privacy, why they matter, and what can be done about them. Solove provides incisive examinations of key concepts in the digital sphere, including control, manipulation, harm, automation, reputation, consent, prediction, inference, and many others.
Compelling and passionate, On Privacy and Technology teems with powerful insights that will transform the way you think about privacy and technology…(More)”.
Social Informatics
Book edited by Noriko Hara, and Pnina Fichman: “Social informatics examines how society is influenced by digital technologies and how digital technologies are shaped by political, economic, and socio-cultural forces. The chapters in this edited volume use social informatics approaches to analyze recent issues in our increasingly data-intensive society.
Taking a social informatics perspective, this edited volume investigates the interaction between society and digital technologies and includes research that examines individuals, groups, organizations, and nations, as well as their complex relationships with pervasive mobile and wearable devices, social media platforms, artificial intelligence, and big data. This volume’s contributors range from seasoned and renowned researchers to upcoming researchers in social informatics. The readers of the book will understand theoretical frameworks of social informatics; gain insights into recent empirical studies of social informatics in specific areas such as big data and its effects on privacy, ethical issues related to digital technologies, and the implications of digital technologies for daily practices; and learn how the social informatics perspective informs research and practice…(More)”.
The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence
Handbook edited by Nathalie A. Smuha: “…provides a comprehensive overview of the legal, ethical, and policy implications of AI and algorithmic systems. As these technologies continue to impact various aspects of our lives, it is crucial to understand and assess the challenges and opportunities they present. Drawing on contributions from experts in various disciplines, the book covers theoretical insights and practical examples of how AI systems are used in society today. It also explores the legal and policy instruments governing AI, with a focus on Europe. The interdisciplinary approach of this book makes it an invaluable resource for anyone seeking to gain a deeper understanding of AI’s impact on society and how it should be regulated…(More)”.
Handbook on Governance and Data Science
Handbook edited by Sarah Giest, Bram Klievink, Alex Ingrams, and Matthew M. Young: “This book is based on the idea that there are quite a few overlaps and connections between the field of governance studies and data science. Data science, with its focus on extracting insights from large datasets through sophisticated algorithms and analytics (Provost and Fawcett 2013), provides government with tools to potentially make more informed decisions, enhance service delivery, and foster transparency and accountability. Governance studies, concerned with the processes and structures through which public policy and services are formulated and delivered (Osborne 2006), increasingly rely on data-driven insights to address complex societal challenges, optimize resource allocation, and engage citizens more effectively (Meijer and Bolívar 2016). However, research insights in journals or at conferences remain quite separate, and thus there are limited spaces for having interconnected conversations. In addition, unprecedented societal challenges demand not only innovative solutions but new approaches to problem-solving.
In this context, data science techniques emerge as a crucial element in crafting a modern governance paradigm, offering predictive insights, revealing hidden patterns, and enabling real-time monitoring of public sentiment and service effectiveness, which are invaluable for public administrators (Kitchin 2014). However, the integration of data science into public governance also raises important considerations regarding data privacy, ethical use of data, and the need for transparency in algorithmic decision-making processes (Zuiderwijk and Janssen 2014). In short, this book is a space where governance and data science studies intersect and highlight relevant opportunities and challenges in this space at the intersection of both fields. Contributors to this book discuss the types of data science techniques applied in a governance context and the implications these have for government decisions and services. This also includes questions around the types of data that are used in government and how certain processes and challenges are measured…(More)”.
AI Upgrades the Internet of Things
Article by R. Colin Johnson: “Artificial Intelligence (AI) is renovating the fast-growing Internet of Things (IoT) by migrating AI innovations, including deep neural networks, Generative AI, and large language models (LLMs) from power-hungry datacenters to the low-power Artificial Intelligence of Things (AIoT). Located at the network’s edge, there are already billions of connected devices today, plus a predicted trillion more connected devices by 2035 (according to Arm, which licenses many of their processors).
The emerging details of this AIoT development period got a boost from ACM Transactions on Sensor Networks, which recently accepted for publication “Artificial Intelligence of Things: A Survey,” a paper authored by Mi Zhang of Ohio State University and collaborators at Michigan State University, the University of Southern California, and the University of California, Los Angeles. The survey is an in-depth reference to the latest AIoT research…
The survey addresses the subject of AIoT with AI-empowered sensing modalities including motion, wireless, vision, acoustic, multi-modal, ear-bud, and GenAI-assisted sensing. The computing section covers on-device inference engines, on-device learning, methods of training by partitioning workloads among heterogeneous accelerators, offloading privacy functions, federated learning that distributes workloads while preserving anonymity, integration with LLMs, and AI-empowered agents. Connection technologies discussed include Internet over Wi-Fi and over cellular/mobile networks, visible light communication systems, LoRa (long-range chirp spread-spectrum connections), and wide-area networks.
A sampling of domain-specific AIoTs reviewed in the survey include AIoT systems for healthcare and well-being, for smart speakers, for video streaming, for video analytics, for autonomous driving, for drones, for satellites, for agriculture, for biology, and for artificial reality, virtual reality, and mixed reality…(More)”.
Intellectual property issues in artificial intelligence trained on scraped data
OECD Report: “Recent technological advances in artificial intelligence (AI), especially the rise of generative AI, have raised questions regarding the intellectual property (IP) landscape. As the demand for AI training data surges, certain data collection methods give rise to concerns about the protection of IP and other rights. This report provides an overview of key issues at the intersection of AI and some IP rights. It aims to facilitate a greater understanding of data scraping — a primary method for obtaining AI training data needed to develop many large language models. It analyses data scraping techniques, identifies key stakeholders, and worldwide legal and regulatory responses. Finally, it offers preliminary considerations and potential policy approaches to help guide policymakers in navigating these issues, ensuring that AI’s innovative potential is unleashed while protecting IP and other rights…(More)”.
Being an Effective Policy Analyst in the Age of Information Overload
Blog by Adam Thierer: “The biggest challenge of being an effective technology policy analyst, academic, or journalist these days is that the shelf life of your products is measured in weeks — and sometimes days — instead of months. Because of that, I’ve been adjusting my own strategies over time to remain effective.
The thoughts and advice I offer here are meant mostly for other technology policy analysts, whether you are a student or young professional just breaking into the field, or someone in the middle of your career looking to take it to the next level. But much of what I’ll say here is generally applicable across the field of policy analysis. It’s just a lot more relevant for people in the field of tech policy because of its fast-moving, ever-changing nature.
This essay will repeatedly reference two realities that have shaped my life both as an average citizen and as an academic and policy analyst: First, we used to live in a world of information scarcity, but we now live in a world of information abundance–and that trend is only accelerating. Second, life and work in a world of information overload is simultaneously a wonderful and awful thing, but one thing is for sure: there is absolutely no going back to the sleepy days of information scarcity.
If you care to be an effective policy analyst today, then you have to come to grips with these new realities. Here are a few tips…(More)”.
Building AI for the pluralistic society
Paper by Aida Davani and Vinodkumar Prabhakaran: “Modern artificial intelligence (AI) systems rely on input from people. Human feedback helps train models to perform useful tasks, guides them toward safe and responsible behavior, and is used to assess their performance. While hailing the recent AI advancements, we should also ask: which humans are we actually talking about? For AI to be most beneficial, it should reflect and respect the diverse tapestry of values, beliefs, and perspectives present in the pluralistic world in which we live, not just a single “average” or majority viewpoint. Diversity in perspectives is especially relevant when AI systems perform subjective tasks, such as deciding whether a response will be perceived as helpful, offensive, or unsafe. For instance, what one value system deems as offensive may be perfectly acceptable within another set of values.
Since divergence in perspectives often aligns with socio-cultural and demographic lines, preferentially capturing certain groups’ perspectives over others in data may result in disparities in how well AI systems serve different social groups. For instance, we previously demonstrated that simply taking a majority vote from human annotations may obfuscate valid divergence in perspectives across social groups, inadvertently marginalizing minority perspectives, and consequently performing less reliably for groups marginalized in the data. How AI systems should deal with such diversity in perspectives depends on the context in which they are used. However, current models lack a systematic way to recognize and handle such contexts.
With this in mind, here we describe our ongoing efforts in pursuit of capturing diverse perspectives and building AI for the pluralistic society in which we live… (More)”.
