Stanford AI researchers, along with colleagues from Harvard University, have published a new paper showing that many risk models may not be all they are cracked up to be because they have too much data. Oftentimes risk models measure things indirectly using proxies and the use of inapt proxies leads to a research phenomenon known as label bias.