Comprehensive Study Notes: Policing and Technology in the Modern Era

Discussion on identified problems in modern policing
Understanding how modern technology serves as solutions
Mention of recommended readings: "Reply All" podcast and "defund podcast reply all".

Definition: CompStat can stand for either Comparative Statistics or Computational Statistics.
Emergence in the early 1990s in New York City:
- A response to skyrocketing crime rates in New York, addressing widespread perceptions of police ineffectiveness.
- Development prompted by Jack Maple and police decision-making teams, aiming to be proactive instead of reactive.
- Initial use primarily analog, evolving with the introduction of Geographic Information Systems (GIS).

Initial method of crime reporting involved collecting data and visualizing it using maps and pushpins.
- This included information on crime by time of day and neighborhood patterns.
Regular CompStat meetings held at NYPD headquarters where each borough's police chief would report their numbers.
Policing actions driven by data visibility to manage crime more effectively.

Introduction of performance management as a system for police accountability.
Leads to a significant drop in crime rates at first, but reveals a flaw:
- Police chiefs begin manipulating statistics (e.g., downgrading or not reporting crimes).
Shift in focus from crime rates to police activity, including arrests and citations.
Close connection to shift there policing methods and practices:
- Introduction of Broken Windows theory, suggesting that keeping minor crimes in check will prevent larger crimes.

Introduction of stop-and-frisk as a policing practice targeting predominantly Black and Hispanic individuals.
CompStat's influence extends beyond NYC to other cities, including Toronto, promoting similar policing models.
Effects: Disparities in crime statistics, leading to accusations and realities of racial profiling.

Transition from pushpins and acetate maps to more advanced GIS systems in the 21st century.
Data collection and visualization becomes increasingly computerized.
Development of new metrics and performance assessments in policing correlating with crime data.

Emphasis on organizations adapting to data collection demands; impact on police practices and accountability.
Potential for human error in data entry, affecting the reliability of crime data.
- Noteworthy saying: "Garbage in, garbage out."
Calls into question the objectivity and accuracy of data-driven approaches in policing.
Technological implementations can amplify systemic biases rather than ameliorate them.

Increase in risk-based profiling generating a higher frequency of police engagement based on algorithmic data points.
Links to racial and criminal profiling, separating human bias from algorithmic decision-making.
Arguments presented by scholars like Sarah Brain suggesting the limitations of tech in transforming policing.
- Tools must align with professional routines to be effective; data analytics are unlikely to revolutionize policing without restructuring.

Quantification of Individual Risk:
- Data-driven assessments increasing susceptibility to profiling.
- Algorithms identifying high-risk individuals based on available data (e.g., gang databases).
Shift from Reactive to Proactive Policing:
- Focus on intervention through data analysis versus traditional methods of policing.
From Query-based to Alert-based Systems:
- Systems notifying officers of potential threats based on vast datasets.
Dragnet Surveillance and Integrating Large Systems:
- Use of Automated License Plate Readers (ALPRs) and extensive data storage without direct suspicion leading to mass surveillance policies.
Integrated Data Systems:
- Emergency of comprehensive databases integrating criminal records, which could also pertain to non-criminal backgrounds (e.g., employment or social ties).

Rising concerns of privacy and data rights amidst growing surveillance practices.
Impacts on marginalized communities, and potential erasure of human oversight in data processing.
Future of policing as a data-driven enterprise, raising questions about accountability and bias in automated systems.

Ongoing need to critique the relationship between emerging technologies in policing and community trust.
Discussion on the path from historical injustices through to modern implementations of technology and their implications for society.
Urgency of understanding the integration of technology in policing practices to prevent further entrenchment of existing biases in the criminal justice system.