Target Audience
This volume is intended for a broad and interdisciplinary audience spanning academia, applied research, industry, and policy sectors. Given the profound epistemic transformations triggered by generative artificial intelligence, quantum-inspired models, and post-digital infrastructures, the book addresses all scholars and professionals engaged in knowledge production, data-intensive inquiry, and methodological innovation.
The primary audience includes:
Social Sciences and Humanities
Researchers in sociology, anthropology, political science, psychology, philosophy, science and technology studies (STS), communication studies, and cultural studies interested in:
- epistemic regimes of digital and post-digital knowledge;
- methodological redesign for digital research;
- AI-assisted social inquiry;
- ethical and ontological debates around datafication and automated cognition.
Media and Communications
Scholars and practitioners focused on:
- algorithmic mediation and platform governance,
- computational propaganda and misinformation,
- AI-generated media, content automation, and synthetic publics.
Computer Science and Information Technology
Professionals and researchers working on:
- AI engineering and explainability;
- human–AI interaction;
- data science and machine learning for social inquiry;
- quantum-inspired computation applied to modelling complex socio-digital systems.
Business and Management
- Executives, analysts, and consultants dealing with:
- organisational knowledge management in AI-enhanced environments;
- algorithmic decision-making and automated workflows;
- innovation management and strategic foresight under quantum-augmented predictive models.
Government and Law
- Policy makers, regulators, and legal scholars concerned with:
- governance of AI systems;
- data protection and ethical oversight;
- algorithmic accountability, transparency, and public-sector automation;
- implications of AI/quantum technologies for democratic processes.
Security and Forensics
Experts operating in:
- cyber-security,
- digital forensics,
- risk analysis of algorithmic systems,
- adversarial AI and quantum cryptography threats.
Education
Educators, instructional designers, and researchers focusing on:
- AI-driven learning environments;
- pedagogical shifts in data literacy;
- epistemic transformations in academic knowledge production and scholarly communication.
Library and Information Science
Professionals engaged in:
- information curation and preservation in the era of synthetic data;
- digital archiving, metadata systems, and algorithmic retrieval;
- challenges of authenticity, provenance, and record integrity in post-digital contexts.
Medicine and Healthcare
Researchers and practitioners exploring:
- AI-supported diagnosis, care models, and clinical decision-making;
- ethical and epistemic implications of data-driven health analytics;
- modelling uncertainty and probabilistic reasoning in medical AI systems.
Life Sciences
Scientists and interdisciplinary teams working on:
- modelling complex biological or ecological systems using AI and quantum-inspired tools;
- cross-domain methodological integration between computational and empirical life-science research.
Physical Sciences and Engineering
Engineers and applied scientists investigating:
- quantum technologies and their epistemic implications;
- computational modelling across physical and social domains;
- human–machine collaboration in hybrid design environments.
Rationale
The target audience reflects the cross-cutting nature of contemporary epistemic shifts. The book positions itself as a transversal reference for all communities confronting:
- the transformation of inference logics through generative and quantum-augmented models;
- the reconfiguration of methodological standards, validity regimes, and interpretive practices;
- the ethical, social, organisational, and regulatory implications of AI-driven knowledge ecologies.
By spanning disciplinary boundaries, the volume aims to shape a shared conceptual vocabulary and foster interdisciplinary dialogue, ensuring that emerging understandings of post-digital knowledge production benefit from a wide and inclusive conversation across sectors.