Automated Organization ProfileCyprus Center for Algorithmic Transparency, Open University of Cyprus
Cyprus Center for Algorithmic Transparency, Open University of Cyprus
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 12.1 (sum of 27 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Image analysis algorithms have become an indispensable tool in our information ecosystem, facilitating new forms of visual communication and information sharing. At the same time, they enable large-scale socio-technical research which would otherwise be difficult to carry out. However, their outputs may exhibit social bias, especially when analyzing people images. Since most algorithms are proprietary and opaque, we pro-pose a method of auditing their outputs for social biases. To be able to compare how algorithms interpret a controlled set of people images, we collected descriptions across six image tagging APIs. In order to com-pare these results to human behavior, we also collected descriptions on the same images from crowdworkers in two anglophone regions. While the APIs do not output explicitly offensive descriptions, as humans do, future work should consider if and how they reinforce social inequalities in implicit ways. Beyond computer vision auditing, the dataset of human- and machine-produced tags, and the typology of tags, can be used to explore a range of research questions related to both algorithmic and human behaviors.
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna
No description available
Authors
- Barlas, Pinar ;
- Kyriakou, Kyriakos ;
- Kleanthous, Styliani ;
- Otterbacher, Jahna