We’ve all seen the video, and know the case that caused the furor.  And what’s more, those of us with the guts to say so are applauding the courage of Fernando Mateo, son of a Black man and a Hispanic woman:

Fernando Mateo gets it right.

[youtube=http://www.youtube.com/watch?feature=player_detailpage&v=nuOw7cBuieQ]

He spoke the truth about the cab driver, shot seven times during a robbery, and backed up his demand for profiling with this reasoned analysis and statistical truth:  that most robberies are committed by Blacks and Hispanics.  He was vilified, accused of being worse than satan (“He’s a racist!  Burn him!”), and forced to defend the truth he gave voice to.

And I sat back and wondered, How did we get to this point?

Remember when profiling was first introduced into the average person’s lexicon?  It was around the time of infamous serial killer Ted Bundy.  It was applauded as an amazing breakthrough by the law enforcement community, a tool of scientific fact.  My dad was a Federal Parole Officer, and I remember him saying how remarkably accurate they were, how he would get chills reading over them once they had the individual in custody, right down to the types of homes they came from, education, profession, relationships, and even sexual dysfunction.

All of this from just analyzing the details and nature of the crime and plugging them in to mathematical formulas.

It was a tool that was met with the awe and respect we used to reserve for science and math.  And make no mistake; profiling is as scientific as it gets:

• Preliminary grounding: The profiling process starts with a specification of the applicable problem domain and the identification of the goals of analysis.
• Data collection: The target dataset or database for analysis is formed by selecting the relevant data in the light of existing domain knowledge and data understanding.
• Data preparation: The data are preprocessed for removing noise and reducing complexity by eliminating attributes.
• Data mining: The data are analyzed with the algorithm or heuristics developed to suit the data, model and goals.  (See Data Mining for breakdown)
• Interpretation: The mined patterns are evaluated on their relevance and validity by specialists and/or professionals in the application domain (e.g. excluding spurious correlations).
• Application: The constructed profiles are applied, e.g. to categories of persons to test and fine-tune the algorithms.
• Institutional decision: The institution decides what actions or policies to apply to groups or individuals whose data match a relevant profile.

Wow…how demonic profiling is.  Who knew that math was clearly so racist?  Maybe 2+2 isn’t 4 after all, right?  I guess, if “4” is the truth and you’re afraid of the “4.”