This paper proposes A People’s AI as a framework that moves beyond prevailing approaches to AI democratization. While contemporary initiatives emphasize expanding access and participation, they often remain fragmented and reproduce existing power asymmetries by treating democratization as a contingent provision or add-on to business as usual. Western regulatory efforts have likewise fallen short of establishing meaningful democratization, since corporate lobbying has constrained their capacity to secure local control or redistribute authority over knowledge production. A People’s AI advances four core principles: (1) the elevation of local knowledge, which challenges the primacy of technical expertise; (2) meaningful local control across the AI lifecycle; (3) collectivity, which recognizes that AI systems shape communal rather than individual outcomes; and (4) reflexive grounding that requires ongoing accountability through continuous communal evaluation. Drawing on critiques of technological determinism and Cartesian dualism, this framework reconceptualizes AI as a human expression rather than an autonomous technology. This shift grounds the legitimacy of A People’s AI in collective oversight, continuous consent, and the enduring capacity for communal refusal. The paper then illustrates how these principles can be enacted in real-world contexts to advance cultural preservation, social equity, and collective self-determination beyond immediate political interests.

Currently, AI is not living up to the hype because of the regular generation of hallucinations. The remedy proposed typically is scaling to increase the data base of these models. With the inclusion of more information, and a tighter isomorphism with daily reality, the assumption is that these miscues can be reduced. This remedy, however, is sustained by a philosophy that completely distorts everyday life and how persons make decisions. Scaling will thus not solve the problem of hallucinations. Only a non-Cartesian philosophy will enable AI models to come closer to how persons interpret and design their lives and render this technology more useful. This shift in philosophy requires a different view of this technology, so that AI does not undermine everyday life. AI must be viewed in a dialogical manner.

For some time, common sense has been viewed as a valuable source of knowledge in AI. Recently, a significant push is underway to accumulate a body of knowledge representative of common sense that can guide AI in a more socially-relevant manner. AI models are currently divorced from how everyday persons understand themselves, others, and their surroundings, and thus tend to hallucinate, or make inappropriate responses. The assumption is that a good dose of common sense may bring AI closer to how persons actually think and behave. Yet the search for common sense is guided by Cartesianism, and thus the resulting common sense does not reflect the daily lives of persons. In the Cartesian framework, the aim is to accumulate a large amount of objective data that allegedly represent the daily lives of persons. This essay advances an alternative, less dualistic philosophy that can bring AI closer to persons and how they make decisions. AI should be grounded in the lifeworld (Lebenswelt) of persons, a notion borrowed from phenomenology, so that this technology may become more individually and socially-attuned and relevant. FlourishingAI, a project in Bogotá, Colombia, is an example of how this philosophy can be put into practice. This essay envisions a more socially-responsible and less alienating AI that does not pose a threat to human existence.

In an era defined by the global surge in the adoption of AI-enabled technologies within public administration, the promises of efficiency and progress are being overshadowed by instances of deepening social inequality, particularly among vulnerable populations. To address this issue, we argue that democratizing AI is a pivotal step toward fostering trust, equity, and fairness within our societies. This article navigates the existing debates surrounding AI democratization but also endeavors to revive and adapt the historical social justice framework, maximum feasible participation, for contemporary participatory applications in deploying AI-enabled technologies in public administration. In our exploration of the multifaceted dimensions of AI’s impact on public administration, we provide a roadmap that can lead beyond rhetoric to practical solutions in the integration of AI in public administration.

AI is considered to be very abstract to a range of critics. In this regard, algorithms are referred to regularly as black boxes and divorced from human intervention. A particular philosophical maneuver supports this outcome. The aim of this article is to (1) bring the philosophy to the surface that has contributed to this distance between AI and people and (2) offer an alternative philosophical position that can bring this technology closer to individuals and communities. The overall goal of the analysis in this paper is the humanising of AI by addressing the shortcomings of conceptualising algorithms as black boxes.