Michael Poznic
Biographic Data
| ID | 5997127 |
|---|---|
| NAME | Michael Poznic |
| GIVEN NAMES | Michael |
| FAMILY NAME | Poznic |
| SIGNATURE | POZNIC M |
| AFFILIATIONS | Karlsruhe Institute of Technology |
| ORCID | 0000-0003-2762-1272 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Non-Representational Models and Objectual Understanding
This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in diff…
What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI)
The increasing ubiquity of machine learning (ML) motivates research on algorithms to “explain” models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models wi…
Machine Learning and the Future of Scientific Explanation
Thin versus thick accounts of scientific representation
Modeling Organs with Organs on Chips
Technological models
The program Global Perspectives: Engineering Ethics Across International and Academic Borders provided a stimulating environment for interdisciplinary exchange between engineering and philosophy gr
Technological models
The program Global Perspectives: Engineering Ethics Across International and Academic Borders provided a stimulating environment for interdisciplinary exchange between engineering and philosophy gr
Modeling Organs with Organs on Chips
Thin versus thick accounts of scientific representation
Machine Learning and the Future of Scientific Explanation
What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI)
The increasing ubiquity of machine learning (ML) motivates research on algorithms to “explain” models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models wi…
Non-Representational Models and Objectual Understanding
This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in diff…
Epistemology (6 works) · Philosophy (4 works) · Philosophy of science (4 works) · Computer Science (3 works) · Political science (3 works) · Psychology (3 works) · Artificial Intelligence (2 works) · Cognitive science (2 works) · Engineering (2 works) · Engineering ethics (2 works)