Data Science Community Rocked by Pet Adoption Contest Cheating Scandal

A team of programmers scraped a pet adoption website to cheat in a $10,000 contest that was intended to help shelter pets get adopted. From a report: Kaggle, an online data science community that regularly hosts machine learning competitions with prizes often in the tens of thousands of dollars, has uncovered a cheating scandal involving a winning team. The Google subsidiary announced late last week that the winner of a competition involving a pet adoption site had been disqualified from the contest for fraudulently obtaining and obscuring test set data. The fact that a team cheated in a competition nominally intended to help shelter animals also raises questions about whether the people who participate in machine learning competitions like Kaggle are actually interested in making the world a better place, or whether they simply want to win prize money and climb virtual leaderboards.

The competition asked contestants to develop algorithms to predict the rate of pet adoption based on pet listings from PetFinder.my, a Malaysian pet adoption site. The goal, according to the competition, was to help discover what makes a shelter pet's online profile appealing for adopters. The winning team's entry would be "adapted into AI tools that will guide shelters and rescuers around the world on improving their pet profiles' appeal, reducing animal suffering and euthanization," the competition site said. The algorithm from BestPetting, the first place team, seemed to almost perfectly predict the rate of adoption for the test set against which the submissions were evaluated, winning with a nearly perfect score of 0.912 (out of 1.0). As a reward for their winning solution, the team of three was awarded the top prize of $10,000. Nine months after the close of the competition, however, one observant teenager found that the impressive results were too good to be true.