Contemporary Literature

Where Hi-Centric-AI sits in the literature.

Hi-Centric-AI is not unprecedented. It locates itself within four streams of contemporary work. We identify them by name because a discipline that conceals its intellectual neighbors is not a discipline; it is a brand.

1. Literature stream

   Human-centered AI (HCAI)

   The body of academic and institutional work centered on the proposition that artificial intelligence should be designed with human concerns, capabilities, and values at the center. Stanford HAI under Fei-Fei Li and James Landay; Ben Shneiderman's Human-Centered AI (Oxford, 2022); Wei Xu's HCAI methodological frameworks; the broader IEEE and ACM HCAI literature. Hi-Centric-AI relates to HCAI as a more architecturally specific and methodologically committed extension — sharing the philosophical orientation while specifying the structural commitments HCAI generally leaves abstract.

2. Literature stream

   Intelligence augmentation (IA)

   The contemporary continuation of the Engelbart–Licklider tradition. Distill.pub's Artificial Intelligence Augmentation (Carter and Nielsen, 2017) formalized AIA as a synthesis field. Recent work in cognitive amplification, augmented analytics, and decision-support systems carries the IA framing into modern computational contexts. Hi-Centric-AI explicitly inherits the IA lineage and extends it with disciplinary commitments around named authority and bounded knowledge.

3. Literature stream

   Hybrid intelligence and human-AI teaming

   Jarrahi and colleagues on hybrid human-AI cognition; the broader literature on human-AI teaming in safety-critical domains; work on calibrated trust, automation bias, and the cognitive ergonomics of human-machine partnerships. This literature documents what goes well and what goes wrong when humans and AI systems share cognitive labor — empirical grounding for the Hi-Centric-AI commitments to explicit authority hierarchy.

4. Literature stream

   Regulated and professional applied AI

   Specialized literatures on AI in clinical practice (clinical decision support, regulatory frameworks for medical AI), AI in legal practice (the contested boundaries of unauthorized practice of law, attorney supervision of AI tools), AI in fiduciary advisory (regulatory positions on AI-generated investment advice), and AI in scientific research (reproducibility, attribution, the proper role of AI in published work). Hi-Centric-AI's applied stance draws directly on these literatures.

Adjacent, but distinct.

Hi-Centric-AI shares philosophical territory with each of the streams above. It departs from them by being more architecturally specific than HCAI, more contemporary in its applied stance than classical IA, more disciplinary in its framing than the empirical human-AI teaming literature, and more methodologically committed than the regulated-AI literature alone. The distinction is not opposition — it is specification.