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.
Abstract
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.
In the debate between those who embrace and those who resist artificial intelligence (AI), resistance is frequently attributed to fear or misunderstanding; an aberration to be fixed or overcome. However, we argue that what may seem like “irrational” resistance instead reflects a pervasive form of rationality that has largely escaped analytical attention. We use disparate examples of AI resistance and theoretical lenses to render explicit an underlying logic which connects varied forms of resistance, grounded in the ethical value(s) that effort can manifest. We examine three types of normative value that effort can yield: effort’s relationship to human excellences, as understood in virtue ethics; the epistemic value of effort for communicating interpersonal regard; and assumptions about the moral value of effort embedded in powerful, society-wide work ethics, and relate these to the use of AI. This allows us to surface a hitherto implicit foundation shared by different types of resistance to AI, and a tension at the heart of debates between AI embracers and AI resisters between the goods of efficiency and the goods of effort. Through this articulation, we give analytic footing to everyday, individual forms of resistance and reframe one side of the debate between embracers and resisters.
R. Downes, Rebecca Mines· AI and Ethics· 0 citations
Dominant approaches to bias in artificial intelligence (AI) are structured by what I identify as the isolationist problem: the tendency to treat bias as a discrete, technically addressable flaw within the AI development pipeline, rather than as a relational phenomenon embedded in social, institutional, and political arrangements. This problem is sustained by two mutually reinforcing orientations: technocentrism, which reframes ethical challenges as engineering problems amenable to computational resolution, and the bias-centric conception of fairness, which reduces fairness to statistical mitigation and obscures its contested, context-dependent character. Together, these orientations produce ontological, epistemic, and practical forms of narrowing ethical imagination and channel intervention into technically tractable but socially limited responses. Against this, I propose sociotechnical sensitivity as both an analytical orientation and a normative commitment: a sustained attentiveness to the ways in which AI systems are constitutively embedded in social relations, institutional arrangements, cultural norms, and power structures. The paper’s central argumentative shift is to change the narrative from bias mitigation to bias management—treating bias not as a defect to be corrected but as an ongoing condition to be governed. These arguments are developed through an extended analysis of the well-known case of the COMPAS algorithm, a recidivism risk-assessment prediction tool, illustrating three practical axes of bias management: contextualisation, institutionalisation, and iteration. The gap between sociotechnically sensitive AI ethics and its realisation is ultimately a matter of governance design and political will, not merely a problem of missing methods or tools.
Gabriela Arriagada-Bruneau· Science and Engineering Ethi...· 0 citations
This article examines the legacies of antihumanism within the context of artificial intelligence (AI) research. In response to the ethical risks inherent to the proliferation of quasi-autonomous computational agents, the AI industry, legislative bodies and academia often advocate for a “human-centred” approach that would align AI with human values, desires and goals, ostensibly to make these technologies more transparent and trustworthy. The rhetoric of human-centred AI reveals many of the conceptual limitations that antihumanism had already identified in the foundationalist and subjectivist assumptions of Western philosophy. This article considers some of these challenging limitations while also problematizing antihumanism itself. Through Derrida’s notion of “centre” and an analysis of alignment methodologies, the article demonstrates how attempts to decentre the human often reconstitute new forms of authority. It then examines the possibility of surpassing standard anthropocentric approaches in AI while maintaining a critical philosophical engagement with the structurally necessary yet precarious character of organizing principles.
When interacting with social AI systems (SAIs), we routinely speak of what they ‘believe’, ‘want’, or ‘know’. With some exceptions, philosophers tend to treat such anthropomorphism as a single phenomenon that risks one kind of error: mistaken ontological commitment to machine minds and mental states. This paper challenges this monistic assumption. I distinguish two modes of anthropomorphic attribution—metaphysical and pragmatic—and identify two corresponding kinds of possible anthropomorphic error. In the metaphysical mode, speakers commit themselves to the existence of machine mental states, risking straightforward ontological error. In the pragmatic mode, speakers adopt the intentional stance without ontological commitment, yet still risk error when another interpretive strategy would better serve their purposes. I defend Mixed Anthropomorphism: both modes are common. This pluralist account reveals that the current debate’s focus on whether users ‘really mean it’ obscures the pragmatic dimension of anthropomorphic ascription (and its risks). Even ontologically innocent anthropomorphism can constitute a mistake because it employs the wrong interpretive tool for the task at hand. Understanding these distinct error types matters both theoretically, for clarifying the nature of human-AI interaction, and practically, for designing systems that encourage and scaffold appropriate interpretive strategies.
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
