Jonathan Mccabe
Biographic Data
| ID | 3848341 |
|---|---|
| NAME | Jonathan Mccabe |
| GIVEN NAMES | Jonathan |
| FAMILY NAME | Mccabe |
| SIGNATURE | MCCABE J |
| AFFILIATIONS | Jonathan McCabe (generative artist), 87 Wattle St, O'Connor, ACT 2602, Australia.. |
| ORCID | 0009-0004-2141-9676 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Machine learning for mental health diagnosis: Power and extensions of epistemic injustice
Following the article of Ugar and Malele, Pozzi and De Proost provide a necessary addition to the discussion around machine learning (ML) in mental health diagnosis. However, their analysis of contributory injustice, a type of injustice in which the dominant social group does not recognise the epistemic offerings of the minority, provides only an introduction to the significant harm that can stem from the intersection of ML and culture. Not only …
Ten Questions Concerning Generative Computer Art
In this paper the authors pose 10 questions they consider the most important for understanding generative computer art. For each question, the authors briefly discuss its implications and suggest how it might form the basis for further discussion
Ten Questions Concerning Generative Computer Art
In this paper the authors pose 10 questions they consider the most important for understanding generative computer art. For each question, the authors briefly discuss its implications and suggest how it might form the basis for further discussion
Ten Questions Concerning Generative Computer Art
In this paper the authors pose 10 questions they consider the most important for understanding generative computer art. For each question, the authors briefly discuss its implications and suggest how it might form the basis for further discussion
Machine learning for mental health diagnosis: Power and extensions of epistemic injustice
Following the article of Ugar and Malele, Pozzi and De Proost provide a necessary addition to the discussion around machine learning (ML) in mental health diagnosis. However, their analysis of contributory injustice, a type of injustice in which the dominant social group does not recognise the epistemic offerings of the minority, provides only an introduction to the significant harm that can stem from the intersection of ML and culture. Not only …
Epistemology (2 works) · Philosophy (2 works) · Psychology (2 works) · Aesthetics (1 works) · Architecture and Computational Design (1 works) · Art, Technology, and Culture (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Basis (linear algebra) (1 works) · Cognitive science (1 works)