AI in Criminal Justice: Predictive Policing or Prejudiced Policing?

2 Introduction

1 Thesis Statement: While AI promises to make criminal justice systems more efficient through tools like predictive policing, it also risks reinforcing systemic biases, raising critical questions about fairness, accountability, and civil rights.

2 Context: AI is increasingly used in areas such as risk assessment, surveillance, and crime prediction but not without controversy.

3 What Is Predictive Policing?

Definition: Predictive policing uses data-driven algorithms to forecast where crimes are likely to occur or identify individuals at risk of offending.

Types:

1 Location-based: Predicts hotspots of criminal activity.

2 Person-based: Identifies individuals deemed high-risk for criminal behaviour.

3 Tools Used: COMPAS, PredPol, HunchLab, and other proprietary platforms.

4 The Promises of AI in Criminal Justice

Resource Allocation:

1 Helps police departments deploy officers more efficiently based on data patterns.

Crime Prevention:

2 Can identify trends and intervene before crimes occur.

Decision Support:

3 Judges and parole boards use AI risk assessments to inform bail or sentencing decisions.

5 The Risks and Realities of Bias

Reinforcement of Historical Bias:

1 Algorithms trained on historical arrest data reflect systemic inequalities (e.g., over-policing in minority neighborhoods).

Disproportionate Impact:

2 Communities of color are more likely to be targeted by person-based predictions.

Lack of Transparency:

3 Many predictive systems are proprietary “black boxes” with limited oversight or explainability.

Case Study: COMPAS:

4 Found to falsely label Black defendants as high risk at nearly twice the rate of white defendants (ProPublica, 2016).

6 Ethical and Legal Challenges

Due Process Concerns:

1 Automated decisions may deprive individuals of the opportunity to challenge or understand outcomes.

Surveillance and Privacy:

2 AI-driven surveillance (e.g., facial recognition) can infringe on civil liberties.

Accountability Gaps:

3 Who is responsible when AI makes a faulty or biased recommendation the developer, the police, or the judge?

7 Can AI Be Used Responsibly in Criminal Justice?

Bias Auditing and Impact Assessments:

1 Regular testing to identify and mitigate discriminatory outcomes.

Open Algorithms and Transparency:

2 Making models interpretable and subject to public scrutiny.

Community Involvement:

3 Involving local communities in designing and evaluating AI tools.

Supplement, Not Replace:

4 AI should support not replace human judgment, especially in life-altering decisions.

Conclusion

Final Thought: AI in criminal justice walks a fine line between innovation and injustice. Predictive tools can offer efficiency and foresight, but without robust ethical safeguards, they risk becoming mechanisms of prejudiced policing that perpetuate rather than solve deep-rooted societal inequalities.