Main argument
Thesis: alignment should be reconceptualized as the STRUCTURAL INTEGRATION of AI within human normative domains rather than as machine internalization of inferred values - because 'human values' are historically evolving and contested, not static parameters. Three-step architecture: (1) EXTENDED HUMAN AGENCY - AI is a teleological extension of human purposiveness (neither autonomous moral subject nor neutral optimizer; both standard framings miss AI's embeddedness in human purposive contexts); (2) PRACTICAL AUTONOMY - the human capacity to act for reasons and assume responsibility within justificatory structures grounds the integration; (3) the NORMATIVE INTERFACE - a design-level mediating architecture connecting machine behavior to human norms, ensuring teleological coherence, normative intelligibility, and accountability, with LAW as the paradigm case (meaning through application, contestability before courts, role-based responsibility structures). Alignment happens through participation in norm-governed action spaces 'where human agents remain the ultimate bearers of responsibility'. Data-driven value inference risks encoding culturally dominant preferences (O'Neil/Noble/Benjamin/Bender line). Human-rights alignment is a paradigmatic INSTANCE of this architecture, not an alternative.
Why it matters here
The continental/Kantian synthesis of themes the coded set reached piecemeal: AI as teleological EXTENSION of human agency (not moral subject, not neutral tool), alignment as integration into norm-governed justificatory practices (not value-internalization), law as the paradigm domain, humans as 'ultimate bearers of responsibility'. Converges independently with Sanwoolu (constrained-not-accountable), Zhi-Xuan (role norms), Brophy (externalized deliberation) - the convergence itself is citable.
Reading notes
Close read of abstract, intro, sec 2 opening (11pp). Cologne + Munich. Cites the critical-algorithm-studies canon (O'Neil, Noble, Benjamin, Bender) - a literature bridge the other coded sources lack. Explicitly methodologically 'reconstructive rather than implementation-oriented' - the actionability gap McKinlay's SLR flags applies to it.
Josifović, S., & Noller, J. (2026). Agency and alignment: toward a normative architecture for human-AI interaction. AI & Society. https://doi.org/10.1007/s00146-026-02950-w
Close reading — 7 coded units
#1
· pp. 2
· argument
“Too often, AI is conceptualized in one of two limiting ways: either as a potential moral subject capable of internalizing ethical reasoning structures, or as a neutral optimization device whose behavior is driven primarily by external incentives. Both conceptions, however, underrepresent a central feature of AI's increasingly real-world function: its profound embeddedness in human social, institutional, and purposive contexts.”
#2
· pp. 2
· claim
“Our central thesis is that alignment does not require a machine's internalization of human values, not least because the very definition of 'human values' is exceptionally difficult. Human values are not static or universally given; they evolve historically and are often shaped by conflict and contestation. Instead, it demands the integration of machine behavior into human normative domains, where actions can be justified, evaluated, and controlled.”
#3
· pp. 2
· definition
“By viewing AI not as an autonomous subject but as a teleological extension of human purposiveness, we recognize its function as an embedded mediator of action. At the same time, this integration must be situated within a framework of practical autonomy—the human capacity to respond to reasons, to act under self-given principles and norms, and to assume responsibility within shared justificatory structures.”
#4
· pp. 2
· definition
“This approach leads us to the concept of a normative interface, a design-level structure that facilitates the embedding of AI in action spaces where reasons matter, outputs can be contested, and human agents remain the bearers of final responsibility.”
#5
· pp. 1–2
· argument
“They tend to conceptualize human values as parameters to be inferred or optimized rather than as elements embedded in justified and representative normative contexts. This approach risks reflecting culturally dominant preferences and thereby reinforcing systemic inequities [...] large-scale language models tend to reproduce dominant linguistic and cultural patterns rather than reasoned normative commitments.”
#6
· pp. 1
· argument
“[Human rights gain] meaning through their application in concrete cases, their contestability before courts and administrative bodies, and their integration into role-based structures of responsibility. Accordingly, alignment grounded in human rights is best understood not as an alternative to the proposed normative architecture, but as a paradigmatic instance of it.”
#7
· pp. 2
· argument
“law functions here as a normatively explicit domain that makes visible the structural conditions under which AI systems can be integrated into practices of justification, interpretation, and responsibility.”