Mains Practice Questions

Introduction

The use of AI-driven Facial Recognition Systems (FRS) in law enforcement offers efficiency in crime prevention but also raises concerns about false positives, privacy violations, and bias. The misidentification of Ravi highlights the ethical risks of AI in policing and the need for accountability, fairness, and transparency. Law enforcement must balance public safety with human rights while ensuring that AI serves justice without discrimination.

Body

A. How can law enforcement balance the benefits of AI-driven facial recognition with concerns over false positives, privacy violations, and algorithmic bias?

• Ensuring Accuracy and Human Oversight: AI should assist but not replace human judgment; officers must verify AI alerts before acting (e.g., manual review of flagged cases).
• Proportional Use of AI: AI should be used only where necessary, avoiding mass surveillance that may restrict civil liberties.
• Regular System Audits: The system should undergo frequent accuracy tests to reduce false positives and prevent misidentification of innocent individuals.
• Transparency and Public Awareness: Law enforcement must inform the public about AI surveillance policies to foster trust (e.g., open reports on AI performance).
• Bias Detection and Correction: AI must be tested for racial, gender, and socio-economic biases, with corrective measures implemented (e.g., diverse training datasets).
• Independent Review Committees: The AI system should be monitored by ethics boards and human rights commissions to ensure fair and unbiased implementation.
• Grievance Redressal Mechanism – Citizens must have a platform to challenge wrongful AI-based detentions.

B. What ethical principles should guide law enforcement in deploying AI tools, particularly in ensuring non-discrimination and protecting marginalized communities?

• Justice and Fairness: AI-based policing should be fair and impartial, ensuring that no community faces disproportionate targeting or wrongful detention.
• Accountability and Responsibility: Law enforcement agencies must own up to AI errors and establish accountability mechanisms to correct wrongful actions.
• Right to Privacy: AI should be used in a manner that respects individual rights, with strong data protection measures to prevent misuse.
• Human Dignity and Autonomy: No individual should be falsely labeled a criminal based on AI predictions alone. Ethical policing must ensure dignity and fairness in law enforcement.
• Public Trust and Consent: AI tools should be transparent and publicly accountable, allowing citizen engagement in decisions regarding their implementation.
• Bias-Free Implementation: Regular equity audits and bias testing must be conducted to ensure AI does not reinforce existing societal inequalities.

C. What legal, procedural, and technological safeguards should be implemented to ensure AI-driven policing remains transparent, fair, and accountable?

• Legal Safeguards:
  
  • Establish strict laws governing AI in policing, including clear regulations on data usage, retention, and deletion.
  • Require judicial oversight for AI-driven arrests and detentions to prevent misuse of power.
  • Implement data privacy laws that restrict the storage and use of facial recognition data beyond necessary limits.
• Procedural Safeguards:
  
  • Officers must receive training in AI ethics, bias detection, and human rights to ensure ethical AI use.
  • Manual verification of AI alerts should be mandatory before making any arrests or detentions.
  • Create a public grievance redressal system allowing citizens to challenge wrongful AI-driven policing decisions.
• Technological Safeguards:
  
  • Conduct regular AI system audits to identify and eliminate bias in algorithms.
  • Implement Explainable AI (XAI) so law enforcement can understand and justify AI decisions instead of relying on black-box predictions.
  • Use alternative AI models that incorporate multiple verification layers before tagging individuals as suspects.

Conclusion

AI-driven policing must ensure justice, fairness, and accountability while balancing public safety with individual rights. The system should help law enforcement but never replace ethical decision-making. By implementing legal protections, transparency measures, and bias control mechanisms, AI can serve as a tool for justice rather than oppression. A well-regulated AI system will enhance public trust while ensuring that law enforcement remains ethical, fair, and responsible.