Five dimensions of scaling democratic deliberation: With and beyond AI

Curated on Posted on by Stefaan Verhulst

Paper by Sammy McKinney and Claudia Chwalisz: “In the study and practice of deliberative democracy, academics and practitioners are increasingly exploring the role that Artificial Intelligence (AI) can play in scaling democratic deliberation. From claims by leading deliberative democracy scholars that AI can bring deliberation to the ‘mass’, or ‘global’, scale, to cutting-edge innovations from technologists aiming to support scalability in practice, AI’s role in scaling deliberation is capturing the energy and imagination of many leading thinkers and practitioners.

There are many reasons why people may be interested in ‘scaling deliberation’. One is that there is evidence that deliberation has numerous benefits for the people involved in deliberations – strengthening their individual and collective agency, political efficacy, and trust in one another and in institutions. Another is that the decisions and actions that result are arguably higher-quality and more legitimate. Because the benefits of deliberation are so great, there is significant interest around how we could scale these benefits to as many people and decisions as possible.

Another motivation stems from the view that one weakness of small-scale deliberative processes results from their size. Increasing the sheer numbers involved is perceived as a source of legitimacy for some. Others argue that increasing the numbers will also increase the quality of the outputs and outcome.

Finally, deliberative processes that are empowered and/or institutionalised are able to shift political power. Many therefore want to replicate the small-scale model of deliberation in more places, with an emphasis on redistributing power and influencing decision-making.

When we consider how to leverage technology for deliberation, we emphasise that we should not lose sight of the first-order goals of strengthening collective agency. Today there are deep geo-political shifts; in many places, there is a movement towards authoritarian measures, a weakening of civil society, and attacks on basic rights and freedoms. We see the debate about how to ‘scale deliberation’ through this political lens, where our goals are focused on how we can enable a citizenry that is resilient to the forces of autocracy – one that feels and is more powerful and connected, where people feel heard and empathise with others, where citizens have stronger interpersonal and societal trust, and where public decisions have greater legitimacy and better alignment with collective values…(More)”

Generative AI Outlook Report

Curated on Posted on by Stefaan Verhulst

Outlook report, prepared by the European Commission’s Joint Research Centre (JRC): “…examines the transformative role of Generative AI (GenAI) with a specific emphasis on the European Union. It highlights the potential of GenAI for innovation, productivity, and societal change. GenAI is a disruptive technology due to its capability of producing human-like content at an unprecedented scale. As such, it holds multiple opportunities for advancements across various sectors, including healthcare, education, science, and creative industries. At the same time, GenAI also presents significant challenges, including the possibility to amplify misinformation, bias, labour disruption, and privacy concerns. All those issues are cross-cutting and therefore, the rapid development of GenAI requires a multidisciplinary approach to fully understand its implications. Against this context, the Outlook report begins with an overview of the technological aspects of GenAI, detailing their current capabilities and outlining emerging trends. It then focuses on economic implications, examining how GenAI can transform industry dynamics and necessitate adaptation of skills and strategies. The societal impact of GenAI is also addressed, with focus on both the opportunities for inclusivity and the risks of bias and over-reliance. Considering these challenges, the regulatory framework section outlines the EU’s current legislative framework, such as the AI Act and horizontal Data legislation to promote trustworthy and transparent AI practices. Finally, sector-specific ‘deep dives’ examine the opportunities and challenges that GenAI presents. This section underscores the need for careful management and strategic policy interventions to maximize its potential benefits while mitigating the risks. The report concludes that GenAI has the potential to bring significant social and economic impact in the EU, and that a comprehensive and nuanced policy approach is needed to navigate the challenges and opportunities while ensuring that technological developments are fully aligned with democratic values and EU legal framework…(More)”.

Energy and AI Observatory

Curated on Posted on by Stefaan Verhulst

IEA’s Energy and AI Observatory: “… provides up-to-date data and analysis on the growing links between the energy sector and artificial intelligence (AI). The new and fast-moving field of AI requires a new approach to gathering data and information, and the Observatory aims to provide regularly updated data and a comprehensive view of the implications of AI on energy demand (energy for AI) and of AI applications for efficiency, innovation, resilience and competitiveness in the energy sector (AI for energy). This first-of-a-kind platform is developed and maintained by the IEA, with valuable contributions of data and insights from the IEA’s energy industry and tech sector partners, and complements the IEA’s Special Report on Energy and AI…(More)”.

AI alone cannot solve the productivity puzzle

Curated on Posted on by Stefaan Verhulst

Article by Carl Benedikt Frey: “Each time fears of AI-driven job losses flare up, optimists reassure us that artificial intelligence is a productivity tool that will help both workers and the economy. Microsoft chief Satya Nadella thinks autonomous AI agents will allow users to name their goal while the software plans, executes and learns across every system. A dream tool — if efficiency alone was enough to solve the productivity problem.

History says it is not. Over the past half-century we have filled offices and pockets with ever-faster computers, yet labour-productivity growth in advanced economies has slowed from roughly 2 per cent a year in the 1990s to about 0.8 per cent in the past decade. Even China’s once-soaring output per worker has stalled.

The shotgun marriage of the computer and the internet promised more than enhanced office efficiency — it envisioned a golden age of discovery. By placing the world’s knowledge in front of everyone and linking global talent, breakthroughs should have multiplied. Yet research productivity has sagged. The average scientist now produces fewer breakthrough ideas per dollar than their 1960s counterpart.

What went wrong? As economist Gary Becker once noted, parents face a quality-versus-quantity trade-off: the more children they have, the less they can invest in each child. The same might be said for innovation.

Large-scale studies of inventive output confirm the result: researchers juggling more projects are less likely to deliver breakthrough innovations. Over recent decades, scientific papers and patents have become increasingly incremental. History’s greats understood why. Isaac Newton kept a single problem “constantly before me . . . till the first dawnings open slowly, by little and little, into a full and clear light”. Steve Jobs concurred: “Innovation is saying no to a thousand things.”

Human ingenuity thrives where precedent is thin. Had the 19th century focused solely on better looms and ploughs, we would enjoy cheap cloth and abundant grain — but there would be no antibiotics, jet engines or rockets. Economic miracles stem from discovery, not repeating tasks at greater speed.

Large language models gravitate towards the statistical consensus. A model trained before Galileo would have parroted a geocentric universe; fed 19th-century texts it would have proved human flight impossible before the Wright brothers succeeded. A recent Nature review found that while LLMs lightened routine scientific chores, the decisive leaps of insight still belonged to humans. Even Demis Hassabis, whose team at Google DeepMind produced AlphaFold — a model that can predict the shape of a protein and is arguably AI’s most celebrated scientific feat so far — admits that achieving genuine artificial general intelligence systems that can match or surpass humans across the full spectrum of cognitive tasks may require “several more innovations”…(More)”.

Community-Aligned A.I. Benchmarks

Curated on Posted on by Stefaan Verhulst

White Paper by the Aspen Institute: “…When people develop machine learning models for AI products and services, they iterate to improve performance. 

What it means to “improve” a machine learning model depends on what you want the model to do, like correctly transcribe an audio sample or generate a reliable summary of a long document.

Machine learning benchmarks are similar to standardized tests that AI researchers and builders can score their work against. Benchmarks allow us to both see if different model tweaks improve the performance for the intended task and compare similar models against one another.

Some famous benchmarks in AI include ImageNet and the Stanford Question Answering Dataset (SQuAD).

Benchmarks are important, but their development and adoption has historically been somewhat arbitrary. The capabilities that benchmarks measure should reflect the priorities for what the public wants AI tools to be and do. 

We can build positive AI futures, ones that emphasize what the public wants out of these emerging technologies. As such, it’s imperative that we build benchmarks worth striving for…(More)”.

Manipulation: What It Is, Why It’s Bad, What to Do About It

Curated on Posted on by Stefaan Verhulst

Book by Cass Sunstein: “New technologies are offering companies, politicians, and others unprecedented opportunity to manipulate us. Sometimes we are given the illusion of power – of freedom – through choice, yet the game is rigged, pushing us in specific directions that lead to less wealth, worse health, and weaker democracy. In, Manipulation, nudge theory pioneer and New York Times bestselling author, Cass Sunstein, offers a new definition of manipulation for the digital age, explains why it is wrong; and shows what we can do about it. He reveals how manipulation compromises freedom and personal agency, while threatening to reduce our well-being; he explains the difference between manipulation and unobjectionable forms of influence, including ‘nudges’; and he lifts the lid on online manipulation and manipulation by artificial intelligence, algorithms, and generative AI, as well as threats posed by deepfakes, social media, and ‘dark patterns,’ which can trick people into giving up time and money. Drawing on decades of groundbreaking research in behavioral science, this landmark book outlines steps we can take to counteract manipulation in our daily lives and offers guidance to protect consumers, investors, and workers…(More)”.

Participatory Approaches to Responsible Data Reuse and Establishing a Social License

Curated on Posted on by Stefaan Verhulst

Chapter by Stefaan Verhulst, Andrew J. Zahuranec & Adam Zable in Global Public Goods Communication (edited by Sónia Pedro Sebastião and Anne-Marie Cotton): “… examines innovative participatory processes for establishing a social license for reusing data as a global public good. While data reuse creates societal value, it can raise concerns and reinforce power imbalances when individuals and communities lack agency over how their data is reused. To address this, the chapter explores participatory approaches that go beyond traditional consent mechanisms. By engaging data subjects and stakeholders, these approaches aim to build trust and ensure data reuse benefits all parties involved.

The chapter presents case studies of participatory approaches to data reuse from various sectors. This includes The GovLab’s New York City “Data Assembly,” which engaged citizens to set conditions for reusing cell phone data during the COVID-19 response. These examples highlight both the potential and challenges of citizen engagement, such as the need to invest in data literacy and other resources to support meaningful public input. The chapter concludes by considering whether participatory processes for data reuse can foster digital self-determination…(More)”.

Facilitating the secondary use of health data for public interest purposes across borders

Curated on Posted on by Stefaan Verhulst

OECD Paper: “Recent technological developments create significant opportunities to process health data in the public interest. However, the growing fragmentation of frameworks applied to data has become a structural impediment to fully leverage these opportunities. Public and private stakeholders suggest that three key areas should be analysed to support this outcome, namely: the convergence of governance frameworks applicable to health data use in the public interest across jurisdictions; the harmonisation of national procedures applicable to secondary health data use; and the public perceptions around the use of health data. This paper explores each of these three key areas and concludes with an overview of collective findings relating specifically to the convergence of legal bases for secondary data use…(More)”.

Protecting young digital citizens

Curated on Posted on by Stefaan Verhulst

Blog by Pascale Raulin-Serrier: “…As digital tools become more deeply embedded in children’s lives, many young users are unaware of the long-term consequences of sharing personal information online through apps, games, social media platforms and even educational tools. The large-scale collection of data related to their preferences, identity or lifestyle may be used for targeted advertising or profiling. This affects not only their immediate online experiences but can also have lasting consequences, including greater risks of discrimination and exclusion. These concerns underscore the urgent need for stronger safeguards, greater transparency and a child-centered approach to data governance.

CNIL’s initiatives to promote children’s privacy

In response to these challenges, the CNIL introduced eight recommendations in 2021 to provide practical guidance for children, parents and other stakeholders in the digital economy. These are built around several key pillars to promote and protect children’s privacy:

1. Providing specific safeguards

Children have distinct digital rights and must be able to exercise them fully. Under the European General Data Protection Regulation (GDPR), they benefit from special protections, including the right to be forgotten and, in some cases, the ability to consent to the processing of their data.In France, children can only register for social networks or online gaming platforms if they are over 15, or with parental consent if they are younger. CNIL helps hold platforms accountable by offering clear recommendations on how to present terms of service and collect consent in ways that are accessible and understandable to children.

2. Balancing autonomy and protection

The needs and capacities of a 6-year-old child differ greatly from those of a 16-year-old adolescent. It is essential to consider this diversity in online behaviour, maturity and the evolving ability to make informed decisions. The CNIL emphasizes  the importance of offering children a digital environment that strikes a balance between protection and autonomy. It also advocates for digital citizenship education to empower young people with the tools they need to manage their privacy responsibly…(More)”. See also Responsible Data for Children.

Blueprint on Prosocial Tech Design Governance

Curated on Posted on by Stefaan Verhulst

Blueprint by Lisa Schirch: “… lays out actionable recommendations for governments, civil society, researchers, and industry to design digital platforms that reduce harm and increase benefit to society.

The Blueprint on Prosocial Tech Design Governance responds to the crisis in the scale and impact of digital platform harms. Digital platforms are fueling a systemic crisis by amplifying misinformation, harming mental health, eroding privacy, promoting polarization, exploiting children, and concentrating unaccountable power through manipulative design.

Prosocial tech design governance is a framework for regulating digital platforms based on how their design choices— such as algorithms and interfaces—impact society. It shifts focus “upstream” to address the root causes of digital harms and the structural incentives influencing platform design…(More)”.