popAI is raising awareness about AI in policing through a Photo & Caption Competition
The Student Photo and Caption Competition organised by the popAI project is now open!
The competition is addressed to students of all European Universities from any discipline, i.e., STEM, Humanities, Social Studies, and any level of higher education, i.e., Bachelor’s, Master’s an PhD.
The contestants will be evaluated by members of the popAI project and its Advisory Board and the winners will be displayed both on the winning Universities and the popAI website.
The objective of popAI is to raise awareness and trust on the use of Artificial Intelligence in the security domain whilst bringing together all involved stakeholders so as to produce targeted and appropriate recommendations for minimizing emerging risks.
The three (3) winners of the popAI Student Photo & Caption Competition will receive, correspondingly:
- 250 euro (1st winner)
- 150 euro (2nd winner)
- 100 euro (3rd winner)
Application deadline for submission of proposals is 15 February 2023! So if you are a student and would like to apply, visit the webpage: https://www.pop-ai.eu/photo-competition/
Thematic areas and frames
Concrete guidelines are defined to frame the depiction of abstract thought in a photo:
- All photos should have at least one thematic focus
- At least one feature of the photo should be linked with a fresh look at technology use in AI usage and policing data
- The caption should be a more descriptive paragraph to indicate accurately the link between the notion of the user and its depiction (translation of abstract thought to a photo).
- All captions should be written in a clear format and language including the main keywords that detail each photo interpretation.
Eligibility Criteria
- Entry submission until 15th February 2023
- The photo must be accompanied by a caption (max 500 characters) reflecting on the captured topic
- The language of the applications must be English
- The image(s) file type must be in .jpg or .png.
- Only one photo can be uploaded on the application by each participant.