Legitimacy, Authority, and Democratic Duties of Explanation

The Dispersion of Power: A Critical Realist Theory of Democracy

Abstract The Dispersion of Power is an urgent call to rethink centuries of conventional wisdom about what democracy is, why it matters, and how to make it better. Drawing from history, social science, psychology, and critical theory, it explains why elections do not and cannot realize the classic ideal of popular rule, and why prevailing strategies of democratic reform often make things worse. Instead, Bagg argues, we should see democracy as a way of protecting public power from capture—an alternative vision that is at once more realistic and more inspiring. Despite their many shortcomings, real-world elections do prevent the most extreme forms of tyranny, and are therefore indispensable. In dealing with the vast inequalities that remain, however, we cannot rely on standard solutions such as electoral reform, direct democracy, deliberation, and participatory governance. Instead, Bagg shows, protecting and enriching democracy requires addressing underlying inequalities of power directly. In part, this entails substantive policies attacking the advantages of wealthy elites. Even more crucially, deepening democracy requires the organization of oppositional, countervailing power among ordinary people. Neither task is easy, but historical precedents exist in both cases—and if democracy is to survive contemporary crises, leaders and citizens alike must find ways to revive and reinvent these essential democratic practices for the twenty-first century.

Human-Computer Insurrection: Notes on an Anarchist HCI

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy - an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower - systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.

Human-Computer Insurrection: Notes on an Anarchist HCI

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy - an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower - systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.

On Authority

A number of Socialists have latterly launched a regular crusade against what they call the principle of authority. It suffices to tell them that this or that act is authoritarian for it to be condemned. This summary mode of procedure is being abused to such an extent that it has become necessary to look into the matter somewhat more closely.

SimPolitics

For more than six decades, the public has been promised that computers will revolutionize politics, both nationally and internationally. In SimPolitics, Fenw...

Operationalizing Legitimacy

Legitimacy is widely invoked as a condition, cause, and outcome of other social phenomena, yet measuring legitimacy is a persistent challenge. In this article, I synthesize existing approaches to conceptualizing legitimacy across the social sciences to identify widely agreed upon definitional properties. I then build on these points of consensus to develop a generalizable approach to operationalization. Legitimacy implies specific relationships among three empirical elements: an object of legitimacy, an audience that confers legitimacy, and a relationship between the two. Together, these empirical elements constitute a dyad (i.e., a single unit consisting of two nodes and a tie). I identify three necessary conditions for legitimacy— expectations, assent, and conformity—that specify how elements of the dyad interact. I detail how these conditions can be used to empirically establish legitimacy (and illegitimacy), distinguishing it from dissimilar phenomena that often appear similar empirically. Followed to its logical conclusion, this operationalization has novel implications for understanding the effects of legitimacy. I discuss these implications, and how they inform debates over the relevance of legitimacy as an explanation for socially significant outcomes.

The Computational Democracy Project

Tools for deliberative democracy.

Protocols: Power Hides in Boring Places

The fights over AI scraping and encrypted browsing are being settled in obscure standards bodies, and advocates need to be in the room, Mallory Knodel argues.

Modular Politics: Toward a Governance Layer for Online Communities

Governance in online communities is an increasingly high-stakes challenge, and yet many basic features of offline governance legacies--juries, political parties, term limits, and formal debates, to name a few--are not in the feature-sets of the software most community platforms use. Drawing on the paradigm of Institutional Analysis and Development, this paper proposes a strategy for addressing this lapse by specifying basic features of a generalizable paradigm for online governance called Modular Politics. Whereas classical governance typologies tend to present a choice among wholesale ideologies, such as democracy or oligarchy, Modular Politics would enable platform operators and their users to build bottom-up governance processes from computational components that are modular and composable, highly versatile in their expressiveness, portable from one context to another, and interoperable across platforms. This kind of approach could implement pre-digital governance systems as well as accelerate innovation in uniquely digital techniques. As diverse communities share and connect their components and data, governance could occur through a ubiquitous network layer. To that end, this paper proposes the development of an open standard for networked governance.

Artificial Intelligence and the Purpose of Social Systems

The law and ethics of Western democratic states have their basis in liberalism. This extends to regulation and ethical discussion of technology and businesses doing data processing. Liberalism relies on the privacy and autonomy of individuals, their ordering through a public market, and, more recently, a measure of equality guaranteed by the state. We argue that these forms of regulation and ethical analysis are largely incompatible with the techno-political and techno-economic dimensions of artificial intelligence. By analyzing liberal regulatory solutions in the form of privacy and data protection, regulation of public markets, and fairness in AI, we expose how the data economy and artificial intelligence have transcended liberal legal imagination. Organizations use artificial intelligence to exceed the bounded rationality of individuals and each other. This has led to the private consolidation of markets and an unequal hierarchy of control operating mainly for the purpose of shareholder value. An artificial intelligence will be only as ethical as the purpose of the social system that operates it. Inspired by the science of artificial life as an alternative to artificial intelligence, we consider data intermediaries: sociotechnical systems composed of individuals associated around collectively pursued purposes. An attention cooperative, that prioritizes its incoming and outgoing data flows, is one model of a social system that could form and maintain its own autonomous purpose.

Runtime Governance for AI Agents: Policies on Paths

AI agents -- systems that plan, reason, and act using large language models -- produce non-deterministic, path-dependent behavior that cannot be fully governed at design time, where with governed we mean striking the right balance between as high as possible successful task completion rate and the legal, data-breach, reputational and other costs associated with running agents. We argue that the execution path is the central object for effective runtime governance and formalize compliance policies as deterministic functions mapping agent identity, partial path, proposed next action, and organizational state to a policy violation probability. We show that prompt-level instructions (and "system prompts"), and static access control are special cases of this framework: the former shape the distribution over paths without actually evaluating them; the latter evaluates deterministic policies that ignore the path (i.e., these can only account for a specific subset of all possible paths). In our view, runtime evaluation is the general case, and it is necessary for any path-dependent policy. We develop the formal framework for analyzing AI agent governance, present concrete policy examples (inspired by the AI act), discuss a reference implementation, and identify open problems including risk calibration and the limits of enforced compliance.

Political Neutrality in AI Is Impossible- But Here Is How to Approximate It

AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.

Political Neutrality in AI Is Impossible- But Here Is How to Approximate It

AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.

AI, Decomputing and the Interregnum

This paper treats AI as diagnostic for the deeper changes taking place in the existing order of things. It uses AI's alignment with both the political economy and with the dualisms that underpin it, including race, gender and anthropocentrism, to highlight the nihilistic character of the current restructuring. AI's scaling and accelerationism are taken as examples of the wider tactics being invoked by hegemonic power to maintain control under changing conditions. From this perspective, the massive build-out of data centres isn't simply a seizure of energy resources but a manifestation of an aggressive and misogynist technopolitics. The paper argues that a liberal push for digital sovereignty doesn't interrupt these dynamics but plays into the hands of emerging technofascism. It proposes instead the prefigurative tactic of 'decomputing', which draws on degrowth, deautomatisation and a convivial approach to technology. It explores decomputing as a means to mitigate both material and relational harms and as a decisive turn towards infrastructuring the common good. The paper concludes that AI is the contradiction that reveals many others, not least the gap between claims to legitimacy and the actuality of destructive violence, and proposes an alternative technopolitics of reciprocity that prioritises care and sustainability.

AI, Decomputing and the Interregnum

This paper treats AI as diagnostic for the deeper changes taking place in the existing order of things. It uses AI's alignment with both the political economy and with the dualisms that underpin it, including race, gender and anthropocentrism, to highlight the nihilistic character of the current restructuring. AI's scaling and accelerationism are taken as examples of the wider tactics being invoked by hegemonic power to maintain control under changing conditions. From this perspective, the massive build-out of data centres isn't simply a seizure of energy resources but a manifestation of an aggressive and misogynist technopolitics. The paper argues that a liberal push for digital sovereignty doesn't interrupt these dynamics but plays into the hands of emerging technofascism. It proposes instead the prefigurative tactic of 'decomputing', which draws on degrowth, deautomatisation and a convivial approach to technology. It explores decomputing as a means to mitigate both material and relational harms and as a decisive turn towards infrastructuring the common good. The paper concludes that AI is the contradiction that reveals many others, not least the gap between claims to legitimacy and the actuality of destructive violence, and proposes an alternative technopolitics of reciprocity that prioritises care and sustainability.

Psychological Perspectives on Legitimacy and Legitimation

Legitimacy is a psychological property of an authority, institution, or social arrangement that leads those connected to it to believe that it is appropriate, proper, and just. Because of legitimacy, people feel that they ought to defer to decisions and rules, following them voluntarily out of obligation rather than out of fear of punishment or anticipation of reward. Being legitimate is important to the success of authorities, institutions, and institutional arrangements since it is difficult to exert influence over others based solely upon the possession and use of power. Being able to gain voluntary acquiescence from most people, most of the time, due to their sense of obligation increases effectiveness during periods of scarcity, crisis, and conflict. The concept of legitimacy has a long history within social thought and social psychology, and it has emerged as increasingly important within recent research on the dynamics of political, legal, and social systems.