The intrusion of the predictive analytics or risk assessment tools into judicial settings is exemplified with the implementation of ‘COMPAS’ into the USA (Wisconsin, back in 2012, after years of development since the 1990s). Such algorithmic software uses machine-learning techniques that find patterns or correlations in vast quantities of ‘data’. Judges use them to assess an offender’s likelihood of recidivism while making parole, probation, bail, and sentencing decisions — who is likely to re-offend at some point in the future or to fail to appear at their court hearing.
Since the justice system already has traditional problems
that require solutions, the recidivism risk scale as the outcomes of powerful
high-tech solutions is being chosen ‘as a potent, pervasive, unstoppable
force’. In fact, such risk assessment algorithms are repurposed, (Završnik,
2019)[1] and intended to transform traditional bail and sentencing systems, and
minimize ‘human biases that lead to unequal application of laws’ (see Samuel Greengard,
2020). No doubt, such a quantified risk-scoring method to enhance the
efficiency and efficacy of the courts’ decision-making process makes a vivid
shift in the criminal justice paradigm in the name of predictive justice.
