Events Calendar

22 Apr
ISP AI Forum: "Using Legal Text Analytics to Quantify Suspicion and Remedy Bias in Drug Interdiction Cases"
Event Type

Forums

Topic

Research, Technology

Target Audience

Undergraduate Students, Staff, Faculty, Graduate Students

Tags

ai

Website

https://pitt.co1.qualtrics.com/jfe/fo...

University Unit
Intelligent Systems Program
Hashtag

#isp

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ISP AI Forum: "Using Legal Text Analytics to Quantify Suspicion and Remedy Bias in Drug Interdiction Cases"

This is a past event.

Abstract: Traffic stops are a very common occurrence in the United States. However, these normally innocuous occasions can carry serious consequences, such as felony arrests. Highway drug interdiction is the process by which law enforcement officers attempt to detect the trafficking of drugs during a routine traffic stop. Often, the legal question in a routine traffic stop is whether a police officer has reasonable suspicion, based on observed facts, that drug trafficking is afoot. Under this legal test, however, any fact observed by an officer is potentially relevant. This test leaves open the possibility of bias effecting an officer’s decision to seize the individual. Additionally, the breadth of the suspicion analysis itself may be unintentionally permitting biased decisions. I will discuss how AI/ML can be used to determine the importance of specific facts officers rely on in making these legal determinations, how legal predictions could be made using this information, and how bias can be addressed and possibly eliminated in the process.

Bio: Morgan Gray is a PhD student in the Intelligent Systems Program. His research focuses on natural language processing and machine learning, with a focus in text analytics in the legal domain. He is specifically interested in criminal law and procedure.

RSVP for the Zoom meeting information: https://pitt.co1.qualtrics.com/jfe/form/SV_4THGjnpJlBm5wtU 

Friday, April 22 at 12:30 p.m. to 1:00 p.m.

Virtual Event

ISP AI Forum: "Using Legal Text Analytics to Quantify Suspicion and Remedy Bias in Drug Interdiction Cases"

Abstract: Traffic stops are a very common occurrence in the United States. However, these normally innocuous occasions can carry serious consequences, such as felony arrests. Highway drug interdiction is the process by which law enforcement officers attempt to detect the trafficking of drugs during a routine traffic stop. Often, the legal question in a routine traffic stop is whether a police officer has reasonable suspicion, based on observed facts, that drug trafficking is afoot. Under this legal test, however, any fact observed by an officer is potentially relevant. This test leaves open the possibility of bias effecting an officer’s decision to seize the individual. Additionally, the breadth of the suspicion analysis itself may be unintentionally permitting biased decisions. I will discuss how AI/ML can be used to determine the importance of specific facts officers rely on in making these legal determinations, how legal predictions could be made using this information, and how bias can be addressed and possibly eliminated in the process.

Bio: Morgan Gray is a PhD student in the Intelligent Systems Program. His research focuses on natural language processing and machine learning, with a focus in text analytics in the legal domain. He is specifically interested in criminal law and procedure.

RSVP for the Zoom meeting information: https://pitt.co1.qualtrics.com/jfe/form/SV_4THGjnpJlBm5wtU 

Friday, April 22 at 12:30 p.m. to 1:00 p.m.

Virtual Event

Event Type

Forums

Tags

ai

University Unit
Intelligent Systems Program
Hashtag

#isp

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