Ai And Democracy Scholars Unpack The Intersection Of Technology And

Bonisiwe Shabane

-Dec 5, 2025, 4:11 AM

ai and democracy scholars unpack the intersection of technology and

When Facebook launched in 2004, it took 10 months to reach 1 million users. Twitter took two years. Spotify took five months, and Instagram took 2.5 months. When ChatGPT launched in 2022, it reached 1 million users in five days. One year later, the large-language model (LLM) generative artificial intelligence (AI) chatbot had 100 million weekly users. As of March, that number is 500 million.

“AI algorithms are becoming more powerful and affordable,” said Shir Raviv, a postdoctoral research fellow at Columbia University and a nonresident fellow with ISPS’s Democratic Innovations program. “Millions of people now use these tools daily, reshaping how citizens access and process information, communicate with elected officials, organize politically, and participate in society. The stakes and implications of this technology for democracy are far-reaching.” Earlier this month, Raviv organized a conference bringing together a diverse group of scholars to explore the various ways in which AI and democracy increasingly intersect: the challenges AI poses to democratic processes, effective... Democratic Innovations aims to identify and test new ideas for improving the quality of democratic representation and governance. Professor of Public Policy, James Bryant Conant University Professor

Director, Ash Center for Democratic Governance and Innovation; Winthrop Laflin McCormack Professor of Citizenship and Self-Government Professor of the Practice of Public Policy, HKS; Gordon McKay Professor of the Practice of Computer Science, SEAS Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action. Public engagement has long been too time-consuming and costly for governments to sustain, but AI offers tools to make participation more systematic and impactful. Our new Reboot Democracy Workshop Series replaces lectures with hands-on sessions that teach the practical “how-to’s” of AI-enhanced engagement. Together with leading practitioners and partners at InnovateUS and the Allen Lab at Harvard, we’ll explore how AI can help institutions tap the collective intelligence of our communities more efficiently and effectively.

You have full access to this open access article This paper examines the integration of Artificial Intelligence (AI) into democratic governance, focusing on the tension between democracy’s epistemic shortcomings—often manifested as voter ignorance—and AI’s capacity to improve decision-making. Building on the Input-Process-Output (IPO) model, the paper distinguishes AI applications into four categories based on the democratic source of their inputs (i.e., whether they originate from the citizenry) and the binding nature of... Each category—democratic binding AI, undemocratic binding AI, democratic unbinding AI, and undemocratic unbinding AI—is then evaluated against core democratic elements: inclusive and equal participation, quality of decisions, deliberation, and the autonomy of citizens to... While some undemocratic binding AI risks centralizing power into the hands of a few, certain forms of AI, such as AI advisers, AI delegates with deliberative consent, and AI nudger, can enhance democratic processes... The paper concludes that carefully implemented AI has the potential to enhance democratic governance while preserving its core ideals.

Avoid common mistakes on your manuscript. Recent advancements of Artificial Intelligence (AI) projects its becoming an integral part of our life for its unequaled epistemic abilities in processing vast data sets, detecting patterns, or aggregating dispersed information, raising profound questions... Simultaneously, democracy faces challenges in good governance due to the epistemic limit of citizens in informed monitoring of politicians, often called the problem of voter ignorance. These seemingly disparate phenomena prompt critical questions like “Can AI help alleviate voter ignorance?” and “If so, what should the democratic application of AI look like?”. This paper argues that democracy can benefit from the integration of AI, particularly in addressing the epistemic challenges posed by voter ignorance. While the deliberative model of democracy is not without criticism, particularly from philosophers like Chantal Mouffe, Jacques Rancière, and Iris Marion Young, the deliberative approach remains uniquely valuable in contexts involving complex epistemic challenges,...

In the deliberative and, more recently, epistemic terms, a key function of democratic governance is not only to uphold the ideal of self-governance among political equals but also to ensure decisions of good quality... However, voter ignorance undermines the ability of democracies to make well-informed decisions (Guerrero, 2024). AI, with its epistemic power in memorizing contents (Panigrahi et al., 2018), predicting accurately (Agrawal et al., 2019), processing and analyzing vast amounts of data consistently over different situations (LeCun et al., 2015), offers... UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted by all Member States in November 2021, is the first global policy framework for artificial intelligence (AI) and outlines different aspects of this technology that... The initial considerations of the Recommendation outline the potential ramifications of AI across diverse domains, notably its implications for democracy. This report builds on these analyses and recommendations, aligning with the core values and principles outlined in the Recommendation.

It delves into the current and potential impact of artificial intelligence on democracy and the benefits that both artificial intelligence and digitalization, in general, could bring to enhancing collective decision-making processes. This analysis is structured around four key topics: Finally, this report offers recommendations for the democratic governance of artificial intelligence aimed at mitigating neative impacts and fostering a more democratic approach to AI governance. Langdon Winner’s classic essay ‘Do Artifacts Have Politics?’ resists a widespread but naïve view of the role of technology in human life: that technology is neutral, and all depends on use.Footnote 1 He does... Instead, Winner distinguishes two ways for artefacts to have ‘political qualities’. First, devices or systems might be means for establishing patterns of power or authority, but the design is flexible: such patterns can turn out one way or another.

An example is traffic infrastructure, which can assist many people but also keep parts of the population in subordination, say, if they cannot reach suitable workplaces. Secondly, devices or systems are strongly, perhaps unavoidably, tied to certain patterns of power. Winner’s example is atomic energy, which requires industrial, scientific, and military elites to provide and protect energy sources. Artificial Intelligence (AI), I argue, is political the way traffic infrastructure is: It can greatly strengthen democracy, but only with the right efforts. Understanding ‘the politics of AI’ is crucial since Xi Jinping’s China loudly champions one-party rule as a better fit for our digital century. AI is a key component in the contest between authoritarian and democratic rule.

Unlike conventional programs, AI algorithms learn by themselves. Programmers provide data, which a set of methods, known as machine learning, analyze for trends and inferences. Owing to their sophistication and sweeping applications, these technologies are poised to dramatically alter our world. Specialized AI is already broadly deployed. At the high end, one may think of AI mastering Chess or Go. More commonly we encounter it in smartphones (Siri, Google Translate, curated newsfeeds), home devices (Alexa, Google Home, Nest), personalized customer services, or GPS systems.

Specialized AI is used by law enforcement, the military, in browser searching, advertising and entertainment (e.g., recommender systems), medical diagnostics, logistics, finance (from assessing credit to flagging transactions), in speech recognition producing transcripts, trade... Governments track people using AI in facial, voice, or gait recognition. Smart cities analyze traffic data in real time or design services. COVID-19 accelerated use of AI in drug discovery. Natural language processing – normally used for texts – interprets genetic changes in viruses. Amazon Web Services, Azure, or Google Cloud’s low- and no-code offerings could soon let people create AI applications as easily as websites.Footnote 2

General AI approximates human performance across many domains. Once there is general AI smarter than we are, it could produce something smarter than itself, and so on, perhaps very fast. That moment is the singularity, an intelligence explosion with possibly grave consequences. We are nowhere near anything like that. Imitating how mundane human tasks combine agility, reflection, and interaction has proven challenging. However, ‘nowhere near’ means ‘in terms of engineering capacities’.

A few breakthroughs might accelerate things enormously. Inspired by how millions of years of evolution have created the brain, neural nets have been deployed in astounding ways in machine learning. Such research indicates to many observers that general AI will emerge eventually.Footnote 3 This essay is located at the intersection of political philosophy, philosophy of technology, and political history. My purpose is to reflect on medium and long-term prospects and challenges for democracy from AI, emphasizing how critical a stage this is. Social theorist Bruno Latour, a key figure in Science, Technology and Society Studies, has long insisted no entity matters in isolation but attains meaning through numerous, changeable relations.

Human activities tend to depend not only on more people than the protagonists who stand out, but also on non-human entities. Latour calls such multitudes of relations actor-networks.Footnote 4 This perspective takes the materiality of human affairs more seriously than is customary, the ways they critically involve artefacts, devices, or systems. This standpoint helps gauge AI’s impact on democracy. Political theorists treat democracy as an ideal or institutional framework, instead of considering its materiality. Modern democracies involve structures for collective choice that periodically empower relatively few people to steer the social direction for everybody. As in all forms of governance, technology shapes how this unfolds.

Technology explains how citizens obtain information that delineates their participation (often limited to voting) and frees up people’s time to engage in collective affairs to begin with. Devices and mechanisms permeate campaigning and voting. Technology shapes how politicians communicate and bureaucrats administer decisions. Specialized AI changes the materiality of democracy, not just in the sense that independently given actors deploy new tools. AI changes how collective decision making unfolds and what its human participants are like: how they see themselves in relation to their environment, what relationships they have and how those are designed, and generally... arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

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Ai And Democracy Scholars Unpack The Intersection Of Technology And

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