Nice try, I like the gesture. Count me extremely skeptical though. You can get decent to really good tabular learning right off the shelf and it‘s so good and

Nice try, I like the gesture. Count me extremely skeptical though. You can get decent to really good tabular learning right off the shelf and it‘s so good and ubiquitous by now that interestingly enough, a few things start to happen that imho might make this effort close to impossible: - The differences between different algorithms get ever smaller, leaving more and more weight of any machine driven decision to the actual input data itself, including proper labeling, preparation, feature engineering, QA and debiasing. You can‘t properly separate the data from the algorithm itself anymore these days. - Even IF you added all this data together with the algorithm in a register (which brings its own privacy and practicality concerns), with the massive breadth and width of modern data sources, most data scientists don‘t even understand model decisions by themselves anymore. You‘ll need to dig deep into the explainability toolbox to be able to at least partially explain them. - There are dozens of documented cases where algorithms just mirror and regurgitate human biases through data. Algorithms by themselves are (mostly) innocuous, but can get to make racist, sexist and populist decisions anyway, purely based on what they have trained on. So if you can‘t even trust the people who build the algorithms to understand their reasoning, what benefit could a publication possibly bring to any outsider?