Van RISc naar RICH: risicotaxatie voor witteboordencriminelen

madoff (flickr oso_remote)

Als een verdachte voor de rechter komt, kan een risicotaxatie worden uitgevoerd om het recidiverisico in te schatten. In Nederland wordt daarvoor het instrument ‘RISc’ gebruikt. De RISc meet verschillende factoren, waaronder de sociaaleconomische situatie van de verdachte. Het idee is dat mensen die slecht scoren op het gebied van wonen, werk, inkomen en opleiding een groter risico hebben om weer criminaliteit te plegen. De reclassering spreekt niet voor niets over de ‘3 W’s’ – wonen, werk, wijf (correcter: wederhelft) als het gaat om resocialisering.

Bernie Madoff

Maar wat als we zo’n risicotaxatie-instrument bij een dader als Bernie Madoff zouden afnemen? Madoff zit een straf uit van 150 jaar in een Amerikaanse gevangenis wegens beleggingsfraude. Madoff stond aan het hoofd van een legitieme beleggingsfirma, maar lichtte via een Ponzischema ook gedurende bijna drie decennia zijn klanten op voor het duizelingwekkende totaalbedrag van 65 miljard dollar.

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Problemen met (etnisch) profileren

Nieuwe post op CrimEUR – het blog van de afdeling Criminologie, Erasmus Universiteit Rotterdam. Lees hier:

Luxe auto of lage status: tegen (etnisch) profileren

De discussie over etnisch profileren is al snel beladen omdat de thema’s etniciteit en racisme in ons land, evenals elders overigens, op zichzelf hete hangijzers zijn. Is het überhaupt een goed idee dat politie, justitie en rechters profileren op bepaalde kenmerken, of dat nu gaat om huidskleur of recidiverisico?

Against socioeconomic marginality as a risk factor – 2

risk (flickr birdmanphotos)

In the previous blog post I discussed several arguments put forward by legal scholar Sonja Starr against including socioeconomic factors such as unemployment, low or lack of education, and homelessness in risk assessment instruments used for informing sentencing decisions.

Here is another argument, put forward by legal scholar Michael Tonry in an article titled ‘Legal and Ethical Issues in the Prediction of Recidivism’:

Tonry reiterates widely supported normative and ethical rules such as ‘don’t treat people differently based on the basis of social class’, that are ‘largely incompatible’ with sorting people into risk categories. Tonry describes how, in the US, in the 1970s federal parole guidelines initially allowed variables such as employment, education, residential status and family characteristics, but that these factors were gradually abandoned because ‘they are heavily correlated with race’. The 1991 parole guidelines do not include education, employment or family characteristics.

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Against socioeconomic marginality as a risk factor

risk bus juliefaith flickr

I have written several times about risk assessment of individual defendants and offenders and the role of socioeconomic factors such as employment, educational level, income, financial situation and housing. Risk assessment is used by criminal justice agencies – in the US, UK, Canada and the Netherlands, as well as other countries – to inform decisions about bail, pre-trial detention, sentencing, probation, parole, treatment, and/or supervision.

For example, a widely used tool called LSI-R and a tool that is used in New York State called COMPAS take into account the following individual factors (mentioned in Starr 2014, see below):

  • Performance at work
  • Housing stability
  • Neighbourhood crime rates
  • Dependence on social assistance
  • High school grades
  • Chances of finding work above minimum wage


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Are algorithms class-blind?

Toledo 65 algorithm (jmjesus Flickr)

In an earlier post I wrote about a supposedly new solution to the injustice of the American bail system: risk assessment. To briefly summarize: many people in the US are in jail because they can’t afford to post bail, and risk assessment would avoid class bias because such assessment would be based on factors that are known to predict re-offending and not appearing in court. I criticized this idea, because risk assessment introduces a new class bias when factors include employment, housing, community support, and owning a car and a cell phone. Replacing judges’ biased discretion with a biased risk assessment tool does not solve the problem.

A recent article in the New York Times pointed out this problem, discussing how bail decisions use ‘little science’ and that ‘hidden biases against the poor and minorities can easily creep into the decision-making.’ For this and other reasons ‘many law enforcement groups and defense lawyers have supported the use of scientifically validated’ risk assessment tools. The news: ‘Now comes help in a distinctly modern form: an algorithm.’


There is new risk assessment tool based on an algorithm. Interestingly, this new tool, already tested and rolled out in 21 jurisdictions, challenges the widespread belief that class and criminal behaviour are tightly related:

The Arnold assessment has been met with some skepticism because it does not take into account characteristics that judges and prosecutors normally consider relevant: the defendant’s employment status, community ties or history of drug and alcohol abuse.

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