Supreme Court Rulings On Automated Decision-Making In Criminal Investigations
⚖️ What Is Automated Decision-Making (ADM) in Criminal Investigations?
Automated Decision-Making refers to technology-based systems (like AI, algorithms, predictive policing, facial recognition, etc.) used by investigative agencies to:
Predict crime patterns
Identify suspects
Analyze digital evidence
Make risk assessments (e.g., bail, parole, etc.)
Such systems raise legal questions around:
Due process
Transparency
Bias and discrimination
Right to fair trial
Accountability in law enforcement
🏛️ Supreme Court of India – Key Rulings on ADM in Criminal Justice
Though no case directly calls out "automated decision-making" in title, the principles and rulings in these cases shape how ADM tools must be used within constitutional and legal boundaries.
1. Justice K.S. Puttaswamy (Retd.) v. Union of India
Citation: (2017) 10 SCC 1
Bench: 9-judge Constitution Bench
Issue:
Whether the right to privacy is a fundamental right and how surveillance or data collection impacts constitutional freedoms.
Relevance to ADM:
The judgment addressed state surveillance, data profiling, and predictive technologies, which are forms of ADM.
The Court held that privacy is intrinsic to personal liberty under Article 21.
Warned against algorithmic profiling without legal safeguards.
Key Observations:
Any use of automated systems that affect individual rights must pass the three-fold test:
Legality (backed by law)
Necessity (needed for a legitimate aim)
Proportionality (least restrictive method)
Significance:
Puttaswamy laid the constitutional foundation for challenging opaque, biased, or unjust use of automated tools in criminal investigations.
2. Anuradha Bhasin v. Union of India (2020)
Citation: (2020) 3 SCC 637
Facts:
Petition challenged internet shutdown and communication restrictions in Jammu & Kashmir.
Relevance to ADM:
Addressed automated tools for communication surveillance, shutdown decisions based on algorithmic threat assessments.
Key Observations:
Government must publish reasons and cannot arbitrarily use automated risk assessments to shut down civil liberties.
Emphasized transparency and judicial review of decisions based on ADM tools.
Significance:
Reinforces that digital decisions affecting rights must be reviewable, even if derived from automated intelligence or predictive models.
3. Manohar Lal Sharma v. Union of India (Pegasus Case)
Citation: (2021) 10 SCC 1
Facts:
Petitioners alleged illegal surveillance using the Pegasus spyware, an automated cyber-intelligence tool.
Relevance to ADM:
Pegasus is an example of automated investigative technology that collects data without direct human input.
Petitioners argued violation of privacy, free speech, and fair investigation.
Key Observations:
Supreme Court created a technical committee to examine unauthorized surveillance.
Stated that the state cannot deploy surveillance tech without clear legal backing.
The "mere invocation of national security" cannot justify such actions.
Significance:
First case where the Court actively reviewed the use of automated surveillance tools.
Set a precedent for accountability in use of AI or cyber-intelligence tools.
4. Selvi v. State of Karnataka (2010)
Citation: (2010) 7 SCC 263
Facts:
Issue related to the involuntary use of narco-analysis, polygraph tests, and brain-mapping during criminal investigations.
Relevance to ADM:
While not AI per se, these are automated scientific techniques used to extract information without consent.
Key Observations:
Such tools, if used without consent, violate Article 20(3) (right against self-incrimination) and Article 21 (right to dignity).
Evidence derived from these techniques is not admissible unless conducted voluntarily and with safeguards.
Significance:
Reinforced that scientific/automated investigation tools must respect constitutional rights.
Provides a legal template for scrutinizing AI-based tools used for profiling or interrogation.
5. Shaheen Welfare Association v. Union of India (1996)
Citation: (1996) 2 SCC 616
Facts:
Concerned the long detention of undertrial prisoners under TADA (Terrorist and Disruptive Activities Act).
Relevance to ADM:
TADA cases often involved automated suspicion models, where software flagged suspects based on associations or behavioral patterns.
Court examined mass suspicion mechanisms.
Key Observations:
Courts must balance national security with personal liberty.
Arrest or surveillance decisions must be based on concrete evidence, not algorithmic suspicion alone.
Significance:
Reinforces judicial skepticism towards blanket or AI-generated suspicions without proper evidence.
6. People's Union for Civil Liberties (PUCL) v. Union of India (Telephone Tapping Case)
Citation: (1997) 1 SCC 301
Facts:
Challenged the state's powers to intercept telephone conversations.
Relevance to ADM:
Modern surveillance uses automated keyword recognition, voice recognition, and algorithmic filtering — an early concern raised in this case.
Key Observations:
State must follow procedural safeguards before surveillance.
Unchecked or secretive use of automated monitoring tools violates Articles 19 and 21.
Significance:
Precursor judgment establishing accountability in digital surveillance, a foundation for judging AI-assisted monitoring tools.
7. Supreme Court on Facial Recognition in Protest Cases (2020–2022)
Though not in a titled judgment, during Delhi Riots and Anti-CAA Protests, Supreme Court heard petitions questioning the use of Facial Recognition Systems (FRS) by police.
Relevance:
These tools make automated identifications and arrests based on visual data.
Judicial Concerns:
Judges questioned accuracy, bias, and lack of legal framework for such tech.
The government was asked to submit technical details and procedural safeguards.
Significance:
While no conclusive ruling, SC’s engagement shows active judicial monitoring of ADM tools in policing and crime control.
📌 Summary of Legal Principles Emerging from These Cases
| Principle | Explanation |
|---|---|
| Due Process | Automated tools must not bypass legal safeguards like notice, hearing, or judicial review. |
| Transparency | Algorithms used in investigation must be explainable and not “black boxes.” |
| Legality & Proportionality | ADM must be backed by law and proportionate to the aim (Puttaswamy). |
| Right Against Self-Incrimination | Automated tools like brain mapping or AI interrogation cannot violate Art. 20(3) (Selvi case). |
| Protection Against Mass Surveillance | Predictive policing or mass monitoring through ADM must not override civil liberties (PUCL, Pegasus). |
| Judicial Review of ADM | Courts retain power to scrutinize AI-based decisions affecting liberty (Anuradha Bhasin). |
🔚 Conclusion
The Supreme Court of India has consistently emphasized constitutional safeguards, transparency, and accountability in the use of automated tools in criminal investigations. While AI and automation can make law enforcement more efficient, judicial interpretation ensures that technology does not become a tool for unchecked surveillance or arbitrary decisions.
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