Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: Using the random classifier model, the authors attempt to predict the ambulance engagement time while ensuring fairness and privacy.
strengths_and_weaknesses: The authors attempt to solve an interesting problem. 

However, it's hard to understand the authors' contributions as the write-up is not cohesive or concise. The sentences are hard to connect and each raises lots of questions. 
The authors don't define the terms victim and agent but frequently use them in the write-up.

Additionally, the authors don't explain their modeling choices, which makes it hard to appreciate and fairly assess their contributions. For example, why is the number of professional versus volunteer firefighters considered a sensitive attribute? Why do authors only consider firefighters and not EMTs and their demographic attributes? 
If nbV and nbP determine iProf, why are they part of the variables used in predicting ambulance engagement time? 
Among other questions, why is the engagement time binarized? 

Lastly, the figures critical to assessing the results are not visible. It's hard to make out the writing/captions, therefore, hard to appreciate the authors' contributions. 

suggested_changes: The authors should provide more support for their modeling choices and experimental setups. 
They should also make the write-up flow clearer and more cohesive to ensure better reading and comprehension of the key ideas presented.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This study elaborates on a complete pipeline for the development of a private and fair machine learning model to predict ambulance engagement time. 
strengths_and_weaknesses: In general I think this paper is quite meaningful in real-world reliable machine learning by considering fairness and DP-privacy. The analysis sounds quite interesting.

However, this reviewer feels like current submission lacking clarity. Further the paper organization could be improved. Several suggestions are in *suggested changes*.
suggested_changes: I would suggest author 

- fixing several typos such as 
>  This results in long response times and leaves a locality unprotected. 
- revising experimental section in Sec 2.
- zooming Fig1 and Fig2, since they are too small to read.
review_title: null

Decision: Invite to revise