Bill Overview
Title: To direct the Attorney General to establish a pilot program to determine the effectiveness of body-worn camera continuous training programs.
Description: This bill authorizes the Department of Justice (DOJ) to establish a pilot program to determine the effectiveness of body-worn camera continuous training programs in improving policing, including community relations with law enforcement. It allows DOJ to award grants to states and local governments for carrying out these training programs.
Sponsors: Rep. Lawrence, Brenda L. [D-MI-14]
Target Audience
Population: People who will be impacted by changes in policing practices due to body-worn camera training
Estimated Size: 600000
- The bill focuses on the implementation and evaluation of continuous training programs related to body-worn cameras used by law enforcement.
- Law enforcement officers are directly targeted as they will participate in the training programs.
- State and local government agencies that manage law enforcement will be involved as administrators and coordinators of the programs, as these are the entities eligible for grants under the bill.
- The general public will experience an indirect impact through potentially improved policing and community relations as law enforcement officers are trained more effectively.
- The bill does not specify the number of officers or jurisdictions covered, which implies the pilot program will start on a smaller scale before potentially expanding.
Reasoning
- The pilot program will initially focus on specific regions, possibly targeting regions with the highest need for improved community-law enforcement relations.
- With a budget of $25 million in year 1, the program might be able to cover training for a pilot group of law enforcement officers in major urban areas or across several small to medium jurisdictions.
- The budget will not cover all officers nationwide, so direct impact will be limited in its initial phases, but indirect benefits could spread through improved community relations.
- The American public may not immediately see improvements, but there could be perception changes over time as trained officers interact with communities.
- The success of the pilot programs could lead to larger policy recommendations and implementations, extending impacts beyond initial areas targeted.
Simulated Interviews
Police Officer (Seattle, WA)
Age: 45 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 15/20
Statement of Opinion:
- Initially skeptical, but open to training if it improves community relations.
- Hopeful that training will bring more transparency and protect officers from false accusations.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 9 | 6 |
| Year 20 | 9 | 6 |
Local Government Official (Chicago, IL)
Age: 36 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 10/20
Statement of Opinion:
- Excited about the potential for this program to help our community.
- Worried about the budget constraints and ensuring long-term funding.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 7 | 5 |
| Year 3 | 7 | 5 |
| Year 5 | 8 | 5 |
| Year 10 | 9 | 5 |
| Year 20 | 8 | 5 |
Civil Rights Activist (Detroit, MI)
Age: 28 | Gender: female
Wellbeing Before Policy: 4
Duration of Impact: 3.0 years
Commonness: 12/20
Statement of Opinion:
- Skeptical about continuous footage being reviewed regularly.
- Concerned about whether officers will actually change behavior.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 4 | 4 |
| Year 2 | 5 | 4 |
| Year 3 | 6 | 4 |
| Year 5 | 6 | 4 |
| Year 10 | 7 | 4 |
| Year 20 | 7 | 4 |
Sheriff (Rural Kentucky)
Age: 50 | Gender: male
Wellbeing Before Policy: 7
Duration of Impact: 5.0 years
Commonness: 5/20
Statement of Opinion:
- Not confident about new training having much effect here.
- Worried bigger cities will take all funding but hopeful for future expansion.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 6 | 7 |
| Year 5 | 7 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 8 | 7 |
Community Organizer (Los Angeles, CA)
Age: 32 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 14/20
Statement of Opinion:
- Hopeful that this program can lead to real change.
- Supports anything that would improve officer training and accountability.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 7 | 5 |
| Year 5 | 8 | 5 |
| Year 10 | 8 | 5 |
| Year 20 | 8 | 5 |
Police Training Officer (Phoenix, AZ)
Age: 38 | Gender: male
Wellbeing Before Policy: 7
Duration of Impact: 8.0 years
Commonness: 10/20
Statement of Opinion:
- Expect training improvements to boost officer morale and efficiency.
- Cautious optimism about public perception changing.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 9 | 7 |
| Year 5 | 9 | 7 |
| Year 10 | 9 | 7 |
| Year 20 | 9 | 7 |
Tech Start-Up Employee (New York City, NY)
Age: 27 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 8/20
Statement of Opinion:
- Sees potential for technology to assist the training effectiveness.
- Wonders about what role data can play in preventing misconduct.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 6 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 6 | 6 |
Law Professor (San Francisco, CA)
Age: 43 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 10.0 years
Commonness: 6/20
Statement of Opinion:
- Believes pilot program could offer valuable insights for academia.
- Concerned about whether agencies will share data transparently.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 9 | 6 |
| Year 20 | 9 | 6 |
Retired Veteran (Houston, TX)
Age: 60 | Gender: male
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 18/20
Statement of Opinion:
- Optimistic about any positive changes in policing.
- Hopes training leads to better officer-public engagement.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 5 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 7 | 5 |
| Year 10 | 7 | 5 |
| Year 20 | 7 | 5 |
Public School Teacher (Baltimore, MD)
Age: 29 | Gender: other
Wellbeing Before Policy: 5
Duration of Impact: 15.0 years
Commonness: 14/20
Statement of Opinion:
- Concerned for students' safety during police interactions.
- Supports efforts to make officers more approachable to youth.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 7 | 5 |
| Year 5 | 7 | 5 |
| Year 10 | 8 | 5 |
| Year 20 | 8 | 5 |
Cost Estimates
Year 1: $25000000 (Low: $20000000, High: $30000000)
Year 2: $25000000 (Low: $20000000, High: $30000000)
Year 3: $25000000 (Low: $20000000, High: $30000000)
Year 5: $0 (Low: $0, High: $0)
Year 10: $0 (Low: $0, High: $0)
Year 100: $0 (Low: $0, High: $0)
Key Considerations
- The effectiveness of training programs will be crucial to measure for justifying further investments.
- Implementation variance due to varying local government capabilities.
- Potential scalability of successful elements of the pilot program.
- Constituent concerns about privacy and body-worn camera usage need addressing.