Predictive Policing And The Bias Trap: Analysing Systemic Challenges In Algorithmic Criminal Justice
Vijayaraghavan K, LLM, School of Criminal Law and Criminal Justice Administration, Tamil Nadu Dr. Ambedkar Law University, School of Excellence, Taramani, Chennai, Tamil Nadu, India
ABSTRACT
This abstract examines how place-based and person-based algorithmic tools embed systemic bias into public safety operations, converting flawed past data into automated, discriminatory futures. By analyzing the mechanics of machine learning models used in modern policing, this paper details the technical and institutional pipelines that codify racial and socioeconomic profiling into software infrastructure. Predictive policing technologies are data-driven innovations for optimized law enforcement. This method suffers lack in transparency and data reliability due to its increasing replicate and amplification as a result of historical human prejudices. At the core of algorithmic bias in law enforcement is the reliance on "dirty data." Machine learning models are trained on historical arrest, citation, and stop-and-frisk records rather than true, objective crime occurrences. Because these underlying datasets reflect decades of disproportionate policing in marginalized communities, racial minorities, and low-income neighborhoods, the algorithms inherit these skewed patterns. This reliance creates a destructive, self-fulfilling feedback loop. When software flags a specific zip code or demographic group as high-risk, police departments deploy additional patrol units to those areas. This heightened surveillance inevitably leads to more arrests and citations for minor infractions, which are then fed back into the system as new data points, validating and reinforcing the algorithm’s initial, biased prediction.
Keywords: Artificial intelligence, predictive policing, crime predication, algorithmic bias, criminal justice system.
