Proponents and detractors of the use of machine learning algorithms to support decision making in criminal justice have several points of dissent that can be organized in two general types, one is mathematical and the other is in regards to policy-making. The mathematical concern is the algorithmic complexity and opacity of machine learning techniques, often called ”black box” methods.  The main concern, from a policy perspective is implementation and its consequences. Thus,  the idea that black box approaches might generate or exacerbate biases, especially racial discrimination is one of the main focus in the public debate. A lack or clarity and rigor in the debate has lead to mistake the policy critiques  with those challenging  implementation.