There has been a host of recent articles and books decrying the use of â€œbig dataâ€ to make decisions about individual behaviors. This is true in commerce (Amazon, Facebook, etc.), but also true in criminal justice, my field of research. Moreover, some of the algorithms that forecast dangerousness are proprietary, making it all but impossible to determine the basis for challenging a sentence based on the algorithmâ€™s outcome. Recent books, such as Weapons of Math Destruction and The Rise of Big Data Policing, underscore the dangers of such activity. This is the essence of an autopilot approach to forecasting behavior â€“ hands off the wheel, leave the driving to us.
There is some research that supports this type of algorithmic decision-making. In particular, Paul Meehl, in Clinical versus Statistical Prediction, showed that, overall, clinicians were not as good as statistical methods in forecasting failure on parole, as well as the efficacy of various mental health treatments. True, this book was written over fifty years ago, but it seems to have stood the test of time.
It is dangerous, however, to relegate to the algorithm the last word, which all too many decision-makers are wont to do (and against which Meehl cautioned). All too often the algorithms, often based on so-so (i.e., same-old, same-old) variables â€“ age, race, sex, income, prior record â€“ are used to â€œpredictâ€ future conduct, ignoring other variables that may be more meaningful on the individual level. And the algorithms may not be sufficiently sensitive to real differences: two people may have the same score even though one person may have started out doing violent crime and then moved on to petty theft, while the other may have started out with petty crime and graduated to violent crime.
That is, the fact that a person has a high recidivist score based on the so-so variables should be seen as a threshold issue, a potential barrier to stopping criminal activity. It should be followed by a more nuanced look at the individualâ€™s additional life experiences (which do not fit into simple categories, and therefore cannot be included as â€œvariablesâ€ in the algorithms). That is, everyone has an age and a race, etc., but not everyone was abused as a child, was born in another country, or spent their teen years shuffling through foster homes. Therefore, these factors (and as important, the timing and sequence of these factors) are not part of the algorithm but may be as determinative of future behavior as the aforementioned variables. This is the essence of a power steering approach to forecasting behavior â€“ you crunch the data, but I decide how to use it and where to go.
Regarding power steering, I’m sure that many of you would rather look at an animated map of weather heading your way than to base your decisions (umbrella or not?) on a static (autopilot) weather forecast (BTW, does a 30 percent chance of rain refer to the likelihood of my getting wet in a given time period or to the fact that 30% of the area will be rainy and may skip me entirely?). The same issues are there in crime analysis. A few years ago I coauthored a book on crime mapping, which introduced the term that heads this post. In that book we described the benefit of giving the crime analyst the steering wheel, to guide the analysis based on his/her knowledge of the unique time and space characteristics of the areas in which the crime patterns developed.
In summary, thereâ€™s nothing wrong with using big data to assist with decision-making. The mistake comes in when using such data to forecast individual behavior, to the exclusion of information that is not amenable to data-crunching because it is highly individualistic â€“ and may be as important in assessing behavior than the aforementioned variables.