Using algorithms to analyze data is a practice of compromises. Every algorithm has its niche, and if that niche isn't suited to the problem at hand, it's a bias. The expression in machine learning research is "no free lunch": no algorithm is suited to every problem.

This means that every time an algorithm is used to transform or interpret data, it is missing out on some aspect of the original data. Data simplification loses information. In the worst case, algorithms are designed to commit outright logical fallacies in the way that they process and simply data.

Minefield realizes the fantasy of being able to witness all of the sacrifices and logical fallacies committed by data analysis. It goes a step further to imagine what data mining would be like as a game that penalized these distortions of meaning. But would a platform like this inspire criticality, or turn out to be just as biased as the algorithms it holds accountable?