Oct 7, 2025

Introduction to Policing and Modern Technology

  • Overview of identified problems in policing that technology can address.

  • Recommendation of podcasts such as "Reply All" and "Defund Podcast" for further understanding.

COMSTAT Overview

Definition and Origin

  • CompStat can stand for either Comparative Statistics or Computational Statistics.

  • Developed in the early 1990s in New York City as an innovative means to produce information about crime.

  • Initially analog; emerged just as Geographic Information System (GIS) technologies were starting.

Background

  • Crime rates were skyrocketing in New York, leading to perceptions that the police were ineffective.

  • Jack Maple and police decision-makers aimed to improve police effectiveness through data collection instead of reactive measures.

Data Collection Methodology

  • Police began collecting and mapping information about criminal activities using colored pushpins on city maps to visualize crime locations and patterns.

  • Identification of crime patterns by type, time of day, and neighborhood.

  • CompStat aimed to make crime visible to make it actionable, encouraging police chiefs to report data regularly.

Operational Effects

  • Individual boroughs were required to produce their own CompStat reports.

  • High-stakes public meetings where police chiefs faced scrutiny for poor crime stats, threatening demotion or termination.

  • Resulted in significant decreases in crime rates but led to manipulations of crime data by police chiefs, such as underreporting or downgrading crimes.

Shift in Metrics

  • Metrics transitioned from measuring actual crime rates to measuring police activity, including arrests and citations.

  • Coincided with a rise in aggressive policing policies, such as broken windows policing—going after minor offenses to prevent major ones.

  • Increase in stop and frisk practices targeting primarily Black and Hispanic individuals.

  • The system's pressure created a feedback loop that perpetuated flawed crime data.

Technological Influence

Evolution to Contemporary Methods

  • Transition from physical map systems to GIS and computer analytics.

  • Policing modernization driven by pressures to utilize new data technologies.

Socio-Technical Imaginaries in Policing

  • Continual identification of problems in police work and reliance on data as remediation.

  • The assumption that data can solve significant policing challenges, while neglecting inherent biases.

  • Example of the societal belief that math and objective data can eradicate human errors in policing.

The Role of Humans in Data Shaping

  • Importance of recognizing human influence in the collection and interpretation of policing data.

  • Emphasizes the interaction between technology and the organizational structure where it is deployed.

Key Shifts in Policing Technology

Quantification of Individual Risk

  • Algorithmic risk assessment in policing, such as the PREDPOL system.

  • Risk factors used to quantify individuals' likelihood of committing crimes, intertwining risk and social needs.

  • Concerns over data neutrality and systemic biases leading to disproportionate targeting of certain populations (e.g., people of color).

Transition from Reactive to Proactive Policing

  • Emphasis on proactive alert-based systems over traditional query-based policing.

  • The introduction of feedback loops whereby police action on individuals based on algorithmic assessments results in reinforcing existing biases.

Dragnet Surveillance and Data Integration

  • Use of systems like automated license plate readers (ALPRs) for extensive data surveillance without suspicion.

  • ALPRs log massive amounts of data unilaterally, leading to potential overreach and privacy violations.

  • Concerns regarding mass data collection initiatives lacking accountability.

Concerns Regarding Data Privacy and Surveillance

  • Proposed data collection regulations by the Canadian government (e.g., C-2 State Borders Act) raising alarm over privacy rights.

  • Integration of various data sources, creating comprehensive surveillance profiles without adequate safeguards.

Conclusion and Implications

  • The increasing reliance on data technologies in policing expands existing racial biases under a guise of objectivity.

  • As technological interventions grow, policing practices face significant ethical and operational challenges.

  • The discussion on the necessity for comprehensive reform to ensure equitable policing amid technological advancements.