GlassBox: GlassBox at MIT PolicyHackathon April-2019. Bringing transparency and auditability to recidivism risk-assessment and parole/pre-trial decision-making process.
Inspiration: Making AI more interpretable and transparent to the judges while adjudicating criminal cases. Currently available recidivism assessment tools (e.g. Compas) provides black-box recommendations to judges.
Built With: Scikit-learn Numpy Python v3.6 Bootstrap HTML 5 CSS 3 Description: Software transparency Recidivism risk-assessment software must provide statistically significant score followed by a list of contributing factors, allowing judges to tweak the inputs to influence the score
Decision-making process auditability Changes to original score must be justified by the judges, subject to appeal court’s audit
What it does: Displays the likelihood of recidivism (in %) Offers the choice to pull out Top-K(1<=K<=30) factors that contribute to the likelihood of recidivism score Allows toggling amongst all of the factors to see the score deviation What's next for Glass-Box: Training Glass-Box to surpass Compas accuracy Providing statistics and benchmarks to judges(individual trajectory, benchmark with state national level)