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Ohio, U.S. Builds Sentencing Data Platform

The Ohio Criminal Sentencing Commission is working with the University of Cincinnati to build the Ohio Sentencing Data Platform (OSDP). The OSDP is designed to help judges implement the Uniform Sentencing Entry and Method of Conviction entries and empower courts with accessible and reliable information. The OSDP will achieve goals that include: using data to inform decision-making; improving transparency; and, making data accessible for the public, practitioners, and research.

The collection of sentencing data in a comprehensive and searchable database will inform decision-making and give judges the tools and information needed to impose sentences in accordance with the purposes and principles of felony sentencing.

Courts, Counties, and policymakers statewide can use this data to make sensible, cost-effective decisions, promote smart, effective use of resources, and ensure measured proportional responses. Further, reliance on data creates an opportunity to monitor and evaluate the results of those changes, to determine if the desired effects are achieved, and assess unintended consequences.

The OSDP will establish standardised data formats for compiling and tracking felony sentencing in all 88 Ohio counties. Built with $800,000 in funding from the court, the database will allow users to compare sentences across the state and see the broader demographics of those who are sentenced to identify race- or income-based inconsistencies, for example.

Those of us who have been entrusted with the duty to lead and to participate in the criminal justice system have an obligation to make sure there is public trust in that system and that the system delivers. Diverse justice for all. And data collection will make that happen.

– Maureen O’Connor , Ohio Supreme Court Chief Justice

So far, 34 of the state’s 244 common pleas judges have opted into the program, which requires them to fill out detailed forms on their sentences. More judges are signing up every week. The platform is the first step to providing accessible and searchable information for judges making sentencing decisions and increasing transparency and accessibility for the public, journalists and researchers.

Giving justice-system practitioners, including judges, attorneys, and court staff the best information available for use during the sentencing process without administrative or fiscal burden, allows them to perform their public-service duties in the most impactful way.

Until recently, Ohio didn’t have a central index on sentencing, so it was difficult to find the number of people sentenced for a specific felony in a given year, the sentences imposed for each felony offender, how many of those were imposed as a result of a plea bargain or how many offenders were placed on community supervision.

The data-driven OSDP project is designed to “tell the story” of sentencing in Ohio by providing understanding and analysis of the criminal justice system by providing statewide, reliable and accessible information on sentencing outcomes.

As reported by OpenGov Asia, the justice system, banks, and private companies use algorithms to make decisions that have profound impacts on people’s lives. Unfortunately, those algorithms are sometimes biased — disproportionately impacting people of colour as well as individuals in lower-income classes when they apply for loans or jobs, or even when courts decide what bail should be set while a person awaits trial.

U.S. researchers have developed a new Artificial Intelligence (AI) programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives. Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system.

SPPL shows that exact probabilistic inference is practical, not just theoretically possible, for a broad class of probabilistic programmes. The researchers have been applying SPPL to probabilistic programmes learned from real-world databases, to quantify the probability of rare events, generate synthetic proxy data given constraints, and automatically screen data for probable anomalies.

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