India AI market forecast to reach $ 7.8 billion by 2025 – OpenGov Asia

The Ohio Criminal Sentencing Commission is working with the University of Cincinnati to create the Ohio Sentencing Data Platform (OSDP). The OSDP is designed to help judges implement uniform sentence entries and sentencing method and to provide courts with accessible and reliable information. The OFSP will achieve the following objectives: use data to inform decision making; improve transparency; and make the data accessible to the public, practitioners and research.

Collecting sentencing data into a comprehensive and searchable database will inform decision-making and provide judges with the tools and information necessary to impose sentences in accordance with the goals and principles of sentencing..

Courts, counties, and policymakers statewide can use this data to make smart and cost-effective decisions, promote smart and efficient use of resources, and ensure measured proportional responses. In addition, the use of data creates an opportunity to monitor and evaluate the results of these changes, determine whether desired effects are being achieved, and assess unintended consequences.

The OSDP will establish standardized data formats for compiling and tracking felony convictions in all 88 counties in Ohio. Built with $ 800,000 in court funding, the database will allow users to compare sentences across the state and view the broader demographics of those convicted to identify inconsistencies based on race or income. , for example.

Those of us who have been given the duty to lead and participate in the criminal justice system have an obligation to ensure that the public has confidence in this system and that the system works. Diversified justice for all. And data collection will make that happen.

– Maureen O’Connor, Chief Justice of the Ohio Supreme Court

So far, 34 of the state’s 244 common plea judges have opted for the program, requiring them to fill out detailed forms about their sentences. More and more judges are registering every week. The platform is the first step in providing accessible and searchable information to judges making sentencing decisions and increasing transparency and accessibility for the public, journalists and researchers.

Giving practitioners of the justice system, including judges, lawyers and court staff, the best available information to use during the sentencing process without administrative or tax burdens, enables them to exercise their public service functions. the most efficient way.

Until recently, Ohio did not have a central sentencing index, so it was difficult to find the number of people convicted of a specific crime in any given year, the sentences imposed for each offender, how many were imposed as a result of plea bargaining or how many offenders were placed under 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 State-Wide Information , reliable and accessible on the results of sentences.

As OpenGov Asia reports, the judiciary, banks and private companies use algorithms to make decisions that have a profound impact on people’s lives. Unfortunately, these algorithms are sometimes flawed – disproportionately affecting people of color as well as people from lower income classes when applying for loans or jobs, or even when courts decide what bond should be set while a no one is awaiting trial.

US researchers have developed a new artificial intelligence (AI) programming language that can assess the fairness of algorithms more accurately and faster than available alternatives. Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system.

SPPL shows that exact probabilistic inference is practical, and not only theoretically possible, for a large class of probabilistic programs. The researchers applied SPPL to probabilistic programs drawn from real-world databases, to quantify the probability of rare events, generate synthetic proxy data based on constraints, and automatically filter the data for probable anomalies.

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