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The Arizona State Board for Private Postsecondary Education (“Board”) is responsible for enforcing the statutes and rules relating to private postsecondary education in Arizona. In this capacity, this Board is charged with licensing and regulating private postsecondary private institutions operating vocational programs and granting degrees within the State of Arizona.

Please Be Advised, Pursuant To:

A.R.S. 41-1030(B): "An agency shall not base a licensing decision in whole or in part on a licensing requirement or condition that is not specifically authorized by statute, rule or state tribal gaming compact.  A general grant of authority in statute does not constitute a basis for imposing a licensing requirement or condition unless a rule is made pursuant to that general grant of authority that specifically authorizes the requirement or condition."

A.R.S. 41-1030(D):"This section may be enforced in a private civil action and relief may be awarded against the state.  The court may award reasonable attorney fees, damages, and all fees associated with the license application to a party that prevails in an action against the state for a violation of this section"

A.R.S. 41-1030(E): "A state employee may not intentionally or knowingly violate this section.  A violation of this section is cause for disciplinary action or dismissal pursuant to the agency's adopted personnel policy."

https://nc-sara.org/agency/arizona-state-board-private-postsecondary-education

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Leaving Your Home in Texas Shows 63% Chance of Being Involved in a Mass Shooting AI Modeling Index Shows

An ML-Powered Risk Assessment System for Predicting Prospective Mass Shooting

by 

Ahmed Abdelmoamen Ahmed

 * and

Nneoma Okoroafor

Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA

*

Author to whom correspondence should be addressed.

Computers 2023, 12(2), 42; https://doi.org/10.3390/computers12020042

Received: 10 January 2023 / Revised: 12 February 2023 / Accepted: 14 February 2023 / Published: 17 February 2023

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

Abstract

The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics.

 https://www.mdpi.com/2073-431X/12/2/42

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