On the epistemic structure of voluntary servitude in the infosphere

Free Speech on the Internet: The Crisis of Epistemic Authority

Abstract. Much of our knowledge of the world comes not from direct sensory experience, but from reliance on epistemic authorities: individuals or institutions that tell us what we ought to believe. For example, what most of us believe about natural selection, climate change, or the Holocaust comes from our reliance on epistemic authorities (scientists, historians). Sustaining epistemic authority depends, crucially, on social institutions that inculcate reliable second-order norms about whom to believe about what. The traditional media were crucial, in the age of mass democracy, with promulgating and sustaining such norms. The internet has obliterated the intermediaries who made that possible, and, in the process, undermined the epistemic standing of actual experts. This essay considers some possible changes to existing free speech doctrine to remedy the epistemological crisis brought about by the internet.

There is no fresh air: A problem with the concept of echo chambers

Standardly, echo chambers are thought to be structures that we should avoid. Agents should keep away from them, to be able to assess a fuller range of evidence and avoid having their confidence in that information manipulated. This paper argues against that standard view. Not only can echo chambers be neutral or good for us, but the existing definitions apply so widely that such chambers are unavoidable. We are all in large numbers of echo chambers at any time – they can be found not just on social media or in political groups, but in almost every social or epistemic group we could categorise ourselves into. Because we are finite and fallible, we cannot escape them and need to exist in them just to get by. The concept, then, does not actually capture something as structurally problematic as the paradigmatic cases would suggest. Our way of using the term in social epistemology needs to change.

Architecting Trust in Artificial Epistemic Agents

Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively shape our shared knowledge environment. They curate the information we receive, often supplanting traditional search-based methods, and are frequently used to generate both personal and deeply specialized advice. How they perform these functions, including whether they are reliable and properly calibrated to both individual and collective epistemic norms, is therefore highly consequential for the choices we make. We argue that the potential impact of epistemic AI agents on practices of knowledge creation, curation and synthesis, particularly in the context of complex multi-agent interactions, creates new informational interdependencies that necessitate a fundamental shift in evaluation and governance of AI. While a well-calibrated ecosystem could augment human judgment and collective decision-making, poorly aligned agents risk causing cognitive deskilling and epistemic drift, making the calibration of these models to human norms a high-stakes necessity. To ensure a beneficial human-AI knowledge ecosystem, we propose a framework centered on building and cultivating the trustworthiness of epistemic AI agents; aligning AI these agents with human epistemic goals; and reinforcing the surrounding socio-epistemic infrastructure. In this context, trustworthy AI agents must demonstrate epistemic competence, robust falsifiability, and epistemically virtuous behaviors, supported by technical provenance systems and "knowledge sanctuaries" designed to protect human resilience. This normative roadmap provides a path toward ensuring that future AI systems act as reliable partners in a robust and inclusive knowledge ecosystem.

ECHO CHAMBERS AND EPISTEMIC BUBBLES

Discussion of the phenomena of post-truth and fake news often implicates the closed epistemic networks of social media. The recent conversation has, however, blurred two distinct social epistemic phenomena. An epistemic bubble is a social epistemic structure in which other relevant voices have been left out, perhaps accidentally. An echo chamber is a social epistemic structure from which other relevant voices have been actively excluded and discredited. Members of epistemic bubbles lack exposure to relevant information and arguments. Members of echo chambers, on the other hand, have been brought to systematically distrust all outside sources. In epistemic bubbles, other voices are not heard; in echo chambers, other voices are actively undermined. It is crucial to keep these phenomena distinct. First, echo chambers can explain the post-truth phenomena in a way that epistemic bubbles cannot. Second, each type of structure requires a distinct intervention. Mere exposure to evidence can shatter an epistemic bubble, but may actually reinforce an echo chamber. Finally, echo chambers are much harder to escape. Once in their grip, an agent may act with epistemic virtue, but social context will pervert those actions. Escape from an echo chamber may require a radical rebooting of one's belief system.

Epistemic Infrastructure: Building Shared Truth in an Era of Disaggregation

The Construct of Collective Perception

From the Platform Society to the AI Society: Towards Critical Studies of Generative AI

The era of AI has begun. Generative AI is rapidly reshaping knowledge production, culture, and political authority, giving rise to an emerging AI society. Yet this transformation did not emerge ex nihilo. This paper argues that the AI society can only be understood in relation to the platform society from which it arises. Tracing the transition from platforms to AI, we identify interlinked economic, epistemic, and political shifts. Economically, AI emerges within platform-based rentier capitalism but reconfigures the monopoly mechanisms on which its accumulation depends. Epistemically, LLMs mark a shift from predictive to generative epistemics, entangling theory formation and knowledge production with private research-as-a-service infrastructures. Politically, governance shifts from data politics to alignment politics: from shaping visibility to shaping what can be said, thought, and imagined. Together, these transformations signal a qualitative shift in mediation—from governing interaction to governing cognition itself—and call for a Critical AI Studies.

The Epistemic Politics of AI Anthropomorphism

AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.

Epistemic Alienation and the Division of Labor

The division of cognitive labor leads to what Barry has recently called epistemic alienation: a problematic separation of individuals from epistemic goods. According to Barry, individuals are alienated from epistemic virtues because an efficient division of cognitive labor requires them to manifest a lack of virtues. I argue that this is a mistaken diagnosis of the source of epistemic alienation. Participating in high-functioning collectives does not prevent individuals from being robustly virtuous; rather, our cognitive limitations make it impossible for us to live up to the highest standards of epistemic conduct. Collectives transcend these cognitive limitations to achieve what individuals cannot by harnessing those same limitations in their members. Furthermore, I argue that we should distinguish the epistemic weaknesses that facilitate the division of cognitive labor from those that result from the division of labor. Only the latter are aspects of epistemic alienation. By dividing our cognitive labor over larger populations of agents and artifacts, we have massively accelerated our rate of epistemic productivity but have thereby created conditions that are variously inhospitable for the thinking of individuals by alienating them from the products, processes, and environments of inquiry. These forms of separation are problematic insofar as they amplify intellectual vices, incapacitate reason, and induce cognitive biases.

Reconciling truthfulness and relevance as epistemic and decision-theoretic utility.

Why Crisis Resilience Depends on Epistemic Security

If we want to prevent tomorrow’s crises from spiraling out of control, we must recognize epistemic systems as critical infrastructure, writes Elizabeth Seger.

How malicious AI swarms can threaten democracy

The fusion of agentic AI and LLMs marks a new frontier in information warfare , Advances in artificial intelligence (AI) offer the prospect of manipulating beliefs and behaviors on a population-wide level ( 1 ). Large language models (LLMs) and autonomous agents ( 2 ) let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility ( 3 ) and inexpensively create falsehoods that are rated as more human-like than those written by humans ( 3 , 4 ). Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multiagent architectures ( 2 ), these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

How malicious AI swarms can threaten democracy

The fusion of agentic AI and LLMs marks a new frontier in information warfare , Advances in artificial intelligence (AI) offer the prospect of manipulating beliefs and behaviors on a population-wide level ( 1 ). Large language models (LLMs) and autonomous agents ( 2 ) let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility ( 3 ) and inexpensively create falsehoods that are rated as more human-like than those written by humans ( 3 , 4 ). Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multiagent architectures ( 2 ), these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

The marketplace of rationalizations

Recent work in economics has rediscovered the importance of belief-based utility for understanding human behaviour. Belief ‘choice’ is subject to an important constraint, however: people can only bring themselves to believe things for which they can find rationalizations. When preferences for similar beliefs are widespread, this constraint generates rationalization markets, social structures in which agents compete to produce rationalizations in exchange for money and social rewards. I explore the nature of such markets, I draw on political media to illustrate their characteristics and behaviour, and I highlight their implications for understanding motivated cognition and misinformation.

How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

suzgunmirac/belief-in-the-machine

Belief in the Machine: Investigating Epistemological Blind Spots of Language Models