Structuring dimensions for collaborative systems evaluation
By P. Antunes, Valeria Herskovic, S. Ochoa, J. Pino. 2012
93 citations recorded by the source corpus.
Subfields: Information Systems, Social Psychology, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Fernando Ortega
- Antonio Hernando
- Abraham Gutiérrez
- Finale Doshi-Velez
- Jesús Bobadilla
- Grgoire Montavon
- K. Müller
- Andrea Vedaldi
In Social Psychology
In Human-Computer Interaction
- K. Müller
- Wojciech Samek
- Grgoire Montavon
- Been Kim
- Klaus‐Robert Müller
- Lars Kai Hansen
- Finale Doshi-Velez
- Andrea Vedaldi
Citations in the corpus
- A Roadmap for a Rigorous Science of Interpretability (2017, methodology): “As HCI community recognizes [Antunes et al., 2012], this is not an easy evaluation metric.”
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019, background): “In Chapter 3, Hansen and Rieger [3] comment on the history of explainable AI, in particular, the prevalence of the question of explanation in the earlier research on expert systems, attempts in this context to characterize explanations in terms of desiderata, and the need to quantify uncertainty ass”
- Towards Explainable Artificial Intelligence (2019, background): “Explanations help to build trust in a relationship between humans, and should therefore be also part of human-machine interactions [3].”
- Towards A Rigorous Science of Interpretable Machine Learning (2017, methodology): “As HCI community recognizes [Antunes et al., 2012], this is not an easy evaluation metric.”
- Recommender systems survey (2013, background): “[12] presents an evaluation framework assuming that evaluation is an evolving process during the system lifecicle.”