Data Model for Computer Vision Explainability, Fairness, and Robustness

Data Model for Computer Vision Explainability, Fairness, and Robustness

Karnani, Simran (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Web Information Systems)

Yang, J. (mentor)
Balayn, A.M.A. (mentor)
Corti, L. (mentor)
Anand, A. (graduation committee)
Wang, Q. (graduation committee)

Delft University of Technology

Computer Science | Data Science and Technology

2023-09-11

In recent years, there has been a growing interest among researchers in the explainability, fairness, and robustness of Computer Vision models. While studies have explored the usability of these models for end users, limited research has delved into the challenges and requirements faced by researchers investigating these requirements. This study addresses this gap through a mixed-method approach, involving 20 semi-structured interviews with researchers and a comprehensive literature analysis.
Through this investigation, we have identified a practical need for a data model that encompasses the essential information researchers require to enhance explainability, fairness, and robustness in Computer Vision applications. We developed a data model that holds the potential to improve transparency and reproducibility within this field, speed up the research process, and facilitate comprehensive evaluations, whether quantitative or qualitative, of proposed methodologies. To refine and demonstrate the practicality of the data model, we have populated it with four existing datasets. Additionally, we have conducted two user studies to validate the model's usability. We found that participants were enthusiastic about using the data model. Some potential uses described by the participants were comparing models and datasets, accessing (niche) datasets and models, creating and exploring datasets, and having access to ground truth explanations. However, participants also had concerns about the data model, mainly with its usability being restricted to people with database knowledge and the richness of data in the database. Nonetheless, hope that this research constitutes the first step for data modelling for researchers in the field of Trustworthy AI.

Artificial intelligence (AI)
Computer Vision
Explainable AI
Fairness
Robustness
Human in the loop
Mixed Methods Approach
Data modelling

http://resolver.tudelft.nl/uuid:0ca58308-43e7-4ded-b538-b4ef25f6d0cb

https://github.com/delftcrowd/CV_datamodel Code on Github

Student theses

master thesis

© 2023 Simran Karnani