WILDTRACK

Transparency and accountability 🙈

The VGG Face dataset is seen to suffer from several transparency limitations.

• Data collection. There's limited information publicly available about how the images were collected and curated. This lack of transparency makes it difficult to assess potential biases in the data gathering process.

• Demographic representation. The dataset may not have a balanced representation across different demographics such as age, gender, ethnicity, and geographical regions. Without clear documentation of the demographic breakdown, it's challenging to evaluate the dataset's diversity and potential biases.

• Consent and privacy. It is unclear how consent was obtained for the individuals whose images are included in the dataset, raising ethical questions about privacy and data usage rights.

• Annotation process. Details about the annotation process, including how identities were verified and labeled, are not fully disclosed, affecting the reliability of the ground truth labels.

Risks and harms 🛑

The WILDTRACK pedestrian detection dataset has been accused of violating privacy and enabling potentially unethical research and applications in China and elsewhere.

Research, advocacy 🧮

• Harvey, A., LaPlace, J. (2019). Exposing.ai

• Zhu H., Qi Y., Shi H., Li N., Zhou H. (2018). Human Detection Under UAV: an Improved Faster R-CNN Approach

• Chakraborty I., Hua G. (2019). Priming Deep Pedestrian Detection with Geometric Context