Ai In Criminal Law Investigations
📌 What Is AI in Criminal Investigation?
Artificial Intelligence (AI) refers to the use of computational systems that can analyze data, make predictions, and assist in decision-making. In criminal investigations, AI is used to:
Analyze CCTV footage using facial recognition
Predict crime hotspots through predictive policing
Assist in digital forensics (e.g., identifying patterns in cybercrime)
Automate document review and legal research
Identify suspects through biometric and behavioral analysis
Aid law enforcement with speech recognition and natural language processing
🧩 Key Areas Where AI Is Used in Criminal Justice:
Facial Recognition Technology (FRT)
Predictive Policing
Automated Surveillance & Data Mining
Crime Pattern Analysis
Forensic Analysis & Evidence Review
Voice & Speech Recognition
⚖️ Legal Issues Involving AI in Criminal Investigations:
Right to Privacy (Article 21 of Indian Constitution, 4th Amendment in the US)
Right against Self-Incrimination (Article 20(3), 5th Amendment in the US)
Due Process and Fair Trial
Bias in AI Algorithms (Racial, socio-economic)
Admissibility of AI-Generated Evidence
📚 Case Laws on AI in Criminal Law Investigations
Below are detailed explanations of key court cases (both Indian and foreign) where AI or automated tools were directly or indirectly involved:
1. K.S. Puttaswamy v. Union of India (2017) – Supreme Court of India
Context: Landmark case on the right to privacy.
Issue: The case didn’t involve AI directly, but the Court laid down that informational privacy is part of Article 21.
Relevance to AI:
Forms the constitutional basis for challenging AI surveillance, facial recognition, and mass data collection in investigations.
AI tools used in criminal law must pass the three-fold test: Legality, Necessity, Proportionality.
Impact:
Any AI-based surveillance or predictive tool used by police must be justified under constitutional principles.
2. Bridges v. South Wales Police (2020) – UK Court of Appeal
Facts: Ed Bridges, a UK citizen, challenged the use of Facial Recognition Technology (FRT) by South Wales Police, claiming it violated his privacy.
Decision: The Court of Appeal held that the deployment of FRT was unlawful, as it lacked clear legal guidance, had inadequate safeguards against misuse, and breached the Data Protection Act.
Significance:
Landmark case on AI surveillance and privacy rights.
Showed that AI systems must not operate in legal vacuums.
Stressed need for algorithmic transparency and human oversight.
3. State v. Anvar P.V. (2014) – Supreme Court of India
Context: Though the case dealt with electronic evidence under Section 65B of the Indian Evidence Act, it laid the foundation for admissibility of digital/AI-generated data.
Key Holding:
Only properly authenticated electronic records are admissible.
Chain of custody and certification under Section 65B is mandatory.
Relevance to AI:
AI-generated crime analysis, facial matches, or voice identification must be authenticated and certified to be admissible.
AI cannot be a black box; the evidence it produces must meet legal standards.
4. Loomis v. Wisconsin (2016) – Wisconsin Supreme Court (USA)
Facts: The trial court used a risk assessment AI software (COMPAS) to determine sentencing, labeling Loomis as high-risk.
Issue: Loomis challenged the lack of transparency and potential racial bias in the AI algorithm.
Court Holding: The Court upheld the use of COMPAS but warned that sentencing cannot be based solely on such tools.
Significance:
Opened debates on bias in AI algorithms used in criminal justice.
Court highlighted the need for human judgment and review of AI outputs.
Encouraged scrutiny of AI fairness and explainability.
5. People v. Goldsmith (2014) – California Court of Appeal (USA)
Facts: The case involved automated traffic enforcement where AI-driven cameras captured images of red-light violators.
Issue: Whether AI-generated images and data logs were admissible without a live witness.
Court’s View:
Held that automated systems can produce admissible evidence if proper authentication is done.
The machine-generated evidence was not considered hearsay.
Significance:
Established that AI-generated evidence is admissible if legally verified.
Paved way for broader use of surveillance AI tools in traffic and criminal enforcement.
6. Justice K.S. Puttaswamy (Retd.) v. Union of India (Aadhaar Case), 2018 – Supreme Court of India
Context: Though focused on Aadhaar, the case has deep implications for biometric data and AI usage.
Relevance:
The court allowed limited use of biometric data (fingerprint, iris) under lawful purposes, with strict regulation.
Significance for AI:
Reinforces that AI tools relying on biometric data must comply with privacy and purpose limitation.
Encourages data minimization and legal authorization before AI tools are deployed in investigations.
7. Suresh Kumar Koushal v. Naz Foundation (Revisited in Navtej Singh Johar, 2018)
Context: Not AI-specific but relevant to surveillance and personal data.
Significance:
Reiterates individual autonomy and dignity.
AI surveillance tools used to profile individuals must pass strict constitutional scrutiny.
🔑 Key Legal Principles Emerging from These Cases:
| Legal Principle | Explanation |
|---|---|
| Privacy Protection | AI tools used for surveillance must respect individual privacy under constitutional law. |
| Transparency & Explainability | AI algorithms used in sentencing or investigation must be explainable and auditable. |
| Admissibility of AI Evidence | AI outputs (e.g., FRT, risk assessments) must meet legal evidence standards like authenticity and chain of custody. |
| Bias & Fairness | Courts are increasingly cautious about racial/gender bias in AI predictive tools. |
| Human Oversight | AI must assist, not replace, human judgment in investigations and legal proceedings. |
✅ Conclusion
AI is a powerful tool in modern criminal law investigations, offering speed and precision in analyzing large volumes of data. However, courts across the world — including India — have emphasized that AI must operate within constitutional and legal boundaries, ensuring privacy, fairness, and transparency.
RELATED Blog
0 comments