A Question on Fairness in the Amazon ML Challenge Evaluation
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I recently appeared for the Amazon ML Challenge on Unstop.
I was assigned a problem set involving Binary Search Trees (BST) and Fenwick Trees (Binary Indexed Trees). I successfully solved both coding questions with optimal solutions, completed all MCQs and the SOP, and still had 18 minutes remaining on the clock.
Despite this, I was not shortlisted for the next round.
At the same time, many participants reported receiving significantly easier problem sets involving basic arrays and strings.
This raises a genuine question:
How was the evaluation normalized across candidates who received vastly different levels of difficulty?
Was there difficulty-based scaling?
Was time remaining used as a tie-breaker?
Were different question sets weighted differently?
How can candidates be confident that they were evaluated on a level playing field?
I completely understand that large-scale assessments with 75,000+ participants require automated evaluation systems. However, transparency in the evaluation criteria is equally important, especially when candidates are given different difficulty levels.
This post is not about a rejection.
It is about understanding whether the selection process adequately accounts for variations in question difficulty and whether candidates are being compared fairly.
I would appreciate any clarification from the organizers regarding the evaluation methodology.
\#AmazonMLChallenge #Unstop #CompetitiveProgramming #DataStructures #Algorithms #FairEvaluation #HiringChallenges