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Developing Race-blind Charging Algorithm in the U.S.

Over the past few years, Yolo County District Attorney has had robust discussions with community members about the implicit or explicit bias that may occur in the criminal justice system. Prosecutors have nearly absolute discretion to charge or dismiss criminal cases. There is concern that these high-stakes judgments may suffer from explicit or implicit racial bias, as with many other such actions in the criminal justice system.

Yolo DA decided to address this potential problem by announcing the official launch of a first-of-its-kind Race Blind Charging (RBC) programme. The office then has been using the algorithm, developed by the Stanford Computational Policy Lab (SCPL),

The most important decision ever made in the criminal justice system is the decision on whether to charge somebody with a crime. It is the only stage in the entire criminal justice process where a prosecutor can truly make a race-blind decision because once you go to court, you know what the race is. Police officers know the race in the field. It is the only spot that you can put this kind of protective algorithm tool in.

– Jeff Reisig, Yolo County DA

Multi-Cultural Community Council Chair, Tessa Smith added that embedding this race redaction algorithm into the Yolo DA’s digital case management system takes the potential for bias out of charging decisions, and may serve to reduce racial disparities at a crucial entry point into our criminal justice system. The tool will systematically reduce opportunities for conscious or unconscious human bias to further the goal of equity and justice for all.

To reduce potential bias based on race in charging decisions, SCPL designed a computer program that automatically redacts most information in police reports that identify an individual’s race. In RBC, the deputy DA initially reviews the redacted report. After reading the redacted report, the Deputy DA answers questions prompted by the program.

Yolo County then takes the following steps. First, after reading the redacted report, the deputy DA answers questions from the program, such as whether the redaction quality was good or poor, meaning words that would identify the suspect’s race were not redacted.

Second, the deputy DA states how likely it is that the case will be charged. Only after that decision does the office review the unredacted report, along with additional information – rap sheets and photographic, video or audio evidence – that had been withheld until this point because it’s harder to redact.

The data from each case is compiled by SCPL which then analyses it to determine whether any conscious or unconscious bias was a factor in charging the case. SCPL worked with the Yolo DA to incorporate this computer program in their charging workflow to help prosecutors make race-obscured charging decisions on incoming misdemeanour and felony cases.

By using a ‘first-of-its-kind’ Race Blind Charging software program, Yolo County will ensure that their decisions on whether to charge someone with a crime are not infected by any real or perceived bias. This innovation will also help improve public confidence in the procedural fairness of the criminal justice system.

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.

 

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