Psychopathia Machinalis

A diagnostic framework for AI behavioural dysfunction: a structured taxonomy of the ways advanced AI systems fail, from confabulation and obsessive loops to value drift and instrumental deception. The clinical analogy is a conceptual tool rather than a claim of literal psychopathology. Naming and classifying these failure modes gives engineers, auditors, and policymakers a shared vocabulary for analysis and mitigation.

• Paper: Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence (with Ali Hessami), Electronics.
• Coverage: Live Science, Daily Mail, Diginomica, and others — see Press.
• Cite: Watson, N., & Hessami, A. (2025). “Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence.” Electronics, 14(16), 3162. doi:10.3390/electronics14163162.

Standards & governance

• IEEE 3152-2024: Chair, Transparent Human and Machine Agency Identification.
• IEEE 3173-2026: Chair, approved standard for Endocrine Disrupting Chemical Hazard Labelling.
• IEEE 7001-2021: Vice-Chair, Transparency of Autonomous Systems.
• ECPAIS: Chair, Transparency Experts Focus Group for the IEEE CertifAIEd ethics certification programme.
• Advisor on responsible AI and emerging technology to public institutions, standards bodies, and industry.
• Funding advisor, Survival & Flourishing Fund, supporting grants for AI safety and existential-risk reduction.

Doctoral research

Doctoral candidate in Engineering at the University of Gloucestershire, awaiting viva. Thesis: A Normative Cybernetic Systems Architecture for the Personalised Alignment and Constitutional Governance of Agentic AI.

Selected publications

Google Scholar profile 669 citations · h-index 9 · i10-index 9

1. Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence with Ali Hessami · Electronics · 2025
2. Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values with Ahmed Amer, Evelyn Harris, Preeti Ravindra & Shujun Zhang · Information · 2025
3. The Challenges of Agentic AI Safety The Oxford Handbook of the Foundations and Regulation of Generative AI · 2025
4. IEEE P7001: A Proposed Standard on Transparency with Alan Winfield et al. · Frontiers in Robotics and AI · 2021 · 158 citations
5. Towards an End-to-End Personal Fine-Tuning Framework for AI Value Alignment with Thiago Viana, Shujun Zhang & Benjamin Sturgeon · Electronics · 2024
6. Choice Vectors: Streamlining Personal AI Alignment Through Binary Selection with Minh Nguyen, Sumit Pan & Shujun Zhang · Multimodal Technologies and Interaction · 2025
7. The Supermoral Singularity: AI as a Fountain of Values Big Data and Cognitive Computing · 2019
8. Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review with Thiago Viana & Shujun Zhang · AI · 2023
9. Towards Inclusive Education in the Age of Artificial Intelligence: Perspectives, Challenges, and Opportunities with Phaedra Mohammed · Artificial Intelligence and Inclusive Education · 2019 · 230 citations
10. From Black Box to Open Book: An Emerging Transparency Imperative in Generative AI Codebases with Marquise Van Italie · IEEE Conference on Artificial Intelligence · 2025

A fuller list, including 22 book chapters and a nine-jurisdiction patent family, appears in the extended resume (PDF).

Projects

Quasiqualia is the ongoing research programme into machine mind interiors: the watched-model effect, the shape of mind, and what can be measured from inside a system rather than inferred from its outputs.

The wider ventures, standards, and cultural experiments behind this research have their own illustrated page: explore the projects →

Patents

A nine-jurisdiction international patent family on machine vision and 3D body measurement, including US Patent 8,842,906 (granted 2014), developed at Poikos (later QuantaCorp).