This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
The Pasco County Sheriff’s Office implemented an 'Intelligence-Led Policing' program that used an algorithm to identify and target residents, including minors, for aggressive surveillance. The system utilized sensitive school and child welfare data to label individuals as potential future criminals, leading to systematic harassment, unwarranted home visits, and retaliatory arrests. The program has been widely criticized by experts as discriminatory and a violation of civil rights.
The Intelligence-Led Policing model rolled out by the Pasco County Sheriff’s Office was allegedly developed based on flawed science and biased data that also contained sensitive information and irrelevant attributes about students, which critics said to be discriminatory.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.
Also classified as
The program obtained and utilized confidential student records, including grades, attendance, and child welfare histories, without parental consent.
The system was used to conduct systematic, automated, and coordinated surveillance of over 1,000 residents and 20,000 students.
The ILP algorithm systematically targeted individuals, including minors and people with developmental disabilities, based on biased scoring rubrics, leading to unequal treatment and unfair outcomes.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The incident was caused by the deployment of the ILP algorithm, which was intentionally designed and used by the Sheriff's Office to target and harass specific residents.
Added categories peak at 3: Substantial (Psychological Harm, Indirect)
A local law enforcement agency's deployment of a predictive policing algorithm systematically violated the civil rights of over 1,000 residents and exposed 20,000 students to unauthorized surveillance. The program's severe societal impacts and constitutional violations prompted federal oversight, illustrating the national security risks of unregulated domestic algorithmic surveillance.
The algorithm calculates risk scores and generates lists of targets to assist police analysts and deputies, who then make the final decisions on enforcement actions.
Unacceptable Risk: The system represents a form of social and criminal scoring by a government authority that targets vulnerable groups based on age and disability, which is prohibited under fundamental rights.
The algorithm was explicitly designed to analyze criminal and school records to predict future criminal behavior and identify targets for proactive policing.
The investigative reporting by the Tampa Bay Times is highly detailed, referencing official manuals, body-camera footage, court filings, and interviews with both victims and former deputies. The Sheriff's Office also provided extensive public rebuttals, ensuring both perspectives are documented.
The Sheriff's Office argues the program is standard 'Intelligence-Led Policing' focused on recidivism reduction rather than 'Predictive Policing' or 'pre-crime' targeting.
Technology
Flawed Scoring Algorithm
Algorithm assigns points for arrests and suspect status, creating bias.
Lack of Algorithmic Transparency
The exact weighting of scores and enhancement criteria remains hidden.
Pre-criminal Predictive Modeling
Unscientifically attempts to predict future crimes before they occur.
Data Inputs
Biased Arrest Records
Uses arrest histories regardless of whether charges were dropped.
School Data Sharing
Mines school grades, attendance, and child-welfare histories.
Unverified Suspect Information
Includes subjective police reports and witness designations as inputs.
Human Factors
Aggressive Deputy Enforcement
Deputies ordered to harass targets and make their lives miserable.
Confirmation Bias of Analysts
Analysts manually select targets, reinforcing algorithmic bias.
Overreliance on Scores
Officers trust generated scores to justify warrantless home visits.
Process and Methods
Lack of Appeal Mechanism
No way for residents to check their status or appeal being listed.
Misapplied Code Enforcement
Using minor code violations as a weapon to force cooperation.
Siloed Information Sharing
Sharing student data between schools and police without consent.
Management
Prioritizing Metrics over Rights
Focusing on property crime reduction stats at the expense of rights.
Retaliation Against Critics
Internal management pushed out deputies who questioned the program.
Defiant Leadership
Leadership refused to acknowledge flaws or apologize for harassment.
Regulatory Environment
No Predictive Policing Laws
Absence of state or federal laws banning or regulating pre-crime tools.
Weak Privacy Enforcement
Student privacy laws failed to prevent sharing of sensitive records.
Inadequate Independent Oversight
Sheriffs operate with vast autonomy and little external accountability.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
