Reading List

The founding text. Establishes control, feedback, and communication as a unified mathematical framework spanning biological and machine systems.

Bounded systems, homeostasis, and the architecture of designed cognitive entities. Source of the bounded-knowledge framing the discipline inherits.

Wiener's accessible articulation of the cybernetic framework and its consequences for human work and accountability.

The foundational paper articulating that the productive future of computation lies in close coupling between human cognition and machine processing.

The conceptual document that defined the goal of computation as the augmentation of human capability. The direct philosophical predecessor to Hi-Centric-AI.

The earliest formal vision of computation as an extension of human memory and reasoning. Pre-cybernetic but constitutive of the augmentation tradition.

The philosophical foundation for treating expert judgment as irreducibly tacit. Source of the discipline's commitment to named human authority.

Polanyi's compact later statement of the tacit-knowledge thesis. The argument that grounds the impossibility of fully encoding expert judgment into autonomous systems.

The classical critique of strong AI. Forty years on, its arguments concerning embodied expertise, situated practice, and the limits of formalized knowledge remain consequential for the discipline.

Establishes that designed systems are subject to architectural analysis, and that bounded rationality, not optimization, governs how cognitive systems operate within domains.

The earlier articulation of bounded rationality as the cognitive principle of decision-making in designed systems.

The empirical-quantitative foundation of HCI as a research discipline. The model of the human as a processor with named cognitive parameters.

The argument that human practice is not reducible to formal procedure. Source of the discipline's commitment to architecting around situated expert judgment rather than around encoded plans.

Distributed cognition as an empirical observation: human cognition operates across humans, tools, and environments as a coupled system.

The argument that artifacts are constitutive of human cognition rather than peripheral to it. Foundational to the way the discipline thinks about the human-AI interface.

The canonical contemporary textbook. Articulates the principles of building AI with human concerns at the center across design, deployment, and evaluation.

The methodological scaffold for human-centered AI as a research and design practice. The body of work to which Hi-Centric-AI offers architectural specification.

The institutional locus of contemporary human-centered AI research. Annual reports, white papers, and policy work shape the broader field.

The contemporary formal articulation of AI augmentation as a synthesis field. Direct contemporary heir of the Engelbart–Licklider lineage.

Hybrid intelligence as a framework for organizational AI deployment. Empirical grounding for the discipline's account of how named expertise and machine cognition share decision-making labor.

Microsoft Research's eighteen guidelines for human-AI interaction. A working contemporary specification for many of the commitments Hi-Centric-AI articulates philosophically.

The applied case for human-centered AI in clinical practice. Topol's argument is structurally adjacent to Hi-Centric-AI in the medical domain.

The regulatory position papers articulating constraints on autonomous AI in clinical decision-making. Empirical evidence that named-authority architectures are mandated by regulation in the medical domain.

The contested boundaries of unauthorized practice of law, attorney supervision of AI tools, and named-attorney accountability — the regulatory backdrop for Hi-Centric-AI in legal practice.