Emerging Case Law Involving Ai And Machine Learning In Criminal Contexts
1. State v. Loomis (Wisconsin Supreme Court, 2016)
Facts:
Eric Loomis was sentenced after pleading guilty to operating a vehicle without the owner’s consent. The judge used COMPAS, a risk-assessment tool, to estimate Loomis’s likelihood of reoffending.
Role of AI/ML:
COMPAS is an algorithmic risk assessment system that calculates recidivism risk using questionnaires and statistical modeling. It is proprietary, so defendants cannot see exactly how scores are calculated.
Legal Issues:
Does using a proprietary algorithm violate due process?
Can judges rely on “black-box” AI for sentencing decisions?
Does reliance on group-based predictions undermine individualized sentencing?
Court Decision:
The court ruled that COMPAS can be considered as one factor in sentencing but cannot be the sole determinant. Judges must consider its limitations and supplement it with human judgment.
Significance:
Landmark for the use of AI in sentencing.
Highlighted transparency and fairness concerns.
Set precedent for “human-in-the-loop” requirements in algorithmic sentencing.
2. United States v. Ross Ulbricht (Silk Road Case, 2015)
Facts:
Ross Ulbricht operated the Silk Road darknet marketplace, facilitating illegal drug sales using Bitcoin. Investigators traced transactions on the blockchain to link him to the platform.
Role of AI/ML:
While blockchain tracing isn’t AI per se, law enforcement also applied pattern recognition algorithms to analyze transaction flows, detect anomalies, and connect pseudonymous addresses to real-world identities.
Legal Issues:
Admissibility of algorithmic analysis as evidence.
Reliance on AI-assisted investigative techniques to support criminal charges.
Court Decision:
Ulbricht was sentenced to life imprisonment without parole. Blockchain and algorithmic analyses played a critical role in linking him to illicit activity.
Significance:
Demonstrates AI-assisted investigative tools in prosecuting cybercrime.
Highlights how algorithmic analysis can complement traditional investigation.
3. Heather Morgan & Ilya Lichtenstein (Bitfinex Hack, 2022)
Facts:
Hackers stole $119 million in Bitcoin from the Bitfinex exchange in 2016. Funds were laundered using mixers and multiple wallets.
Role of AI/ML:
Law enforcement applied AI-powered blockchain analytics to detect patterns of fund movement and de-anonymize wallets. Machine learning models helped prioritize wallets for further investigation.
Legal Issues:
Reliability of AI-assisted blockchain tracing in criminal prosecution.
Attribution of pseudonymous cryptocurrency wallets to specific individuals.
Court Decision:
Morgan and Lichtenstein were arrested and charged with money laundering and wire fraud. Investigators successfully recovered most of the stolen funds using blockchain tracing.
Significance:
First large-scale recovery of stolen cryptocurrency using AI-assisted analysis.
Showcases AI as a tool for cybercrime investigations.
4. Loomis-Inspired Risk Assessment Cases in the US (Multiple States, 2017–2020)
Facts:
Several states (California, Pennsylvania, Florida) adopted COMPAS-like tools in pretrial and sentencing decisions. Defendants challenged the tools’ accuracy, bias, and transparency.
Role of AI/ML:
These algorithms analyze criminal records, demographic data, and behavioral surveys to predict recidivism risk.
Legal Issues:
Whether reliance on predictive algorithms violates equal protection and due process.
Potential racial and gender biases in training data affecting sentencing outcomes.
Court Decisions:
Courts allowed AI tools as advisory inputs but emphasized that human discretion remains essential.
Some appellate courts required disclosure of how algorithms function to defendants.
Significance:
Reinforced the principle of algorithmic transparency.
Highlighted the tension between efficiency and fairness in criminal justice.
5. UK AI Case – Lawyers Using Generative AI (2023)
Facts:
Lawyers submitted briefs citing fake case law generated by AI tools. The AI fabricated authorities and misled the court.
Role of AI/ML:
Generative AI was used to produce legal research and case citations.
Legal Issues:
Professional responsibility and ethical obligations of lawyers using AI.
Risk of perverting the course of justice if AI output is relied upon without verification.
Court Decision:
The court rebuked the lawyers, referring some to professional regulators, emphasizing that AI cannot replace human verification in legal practice.
Significance:
First major case demonstrating the criminal-law consequences of AI misuse in court submissions.
Highlights the need for verification and ethical standards in AI-assisted law.
6. AI in Predictive Policing – Delhi High Court, India (2023)
Facts:
Police used AI to auto-generate FIRs and prioritize cases based on predictive algorithms. A challenge arose regarding AI-generated decisions affecting criminal rights.
Role of AI/ML:
AI tools analyzed crime patterns, predicted hotspots, and suggested suspects for investigation.
Legal Issues:
Can AI-generated FIRs or investigative decisions replace human discretion?
Accountability, transparency, and the right to challenge algorithmic decisions.
Court Decision:
The court ruled that AI may assist but cannot replace human decision-making in criminal law. Decisions affecting rights must involve human oversight.
Significance:
Established a human-in-the-loop principle for AI in criminal justice.
Emphasizes accountability and protection of rights in AI-assisted policing.
7. EPV-R Risk Assessment Tool – Basque Country, Spain
Facts:
Used to predict risk of severe violence in domestic abuse cases. Judges and police used algorithmic scores to guide decisions on protective measures.
Role of AI/ML:
Machine-learning models processed historical cases and victim/perpetrator characteristics to predict risk levels.
Legal Issues:
Accuracy, bias, and transparency of predictive tools.
Potential over-reliance on algorithmic outputs reducing individualized assessment.
Court/Policy Findings:
While not a specific court case, studies emphasized that AI tools must be audited, transparent, and supplemented by human judgment.
Significance:
Shows international adoption of AI in criminal justice.
Highlights risk of bias and importance of human oversight in high-stakes decisions.
Key Takeaways from All Cases
Transparency and Explainability: Black-box AI tools are allowed, but courts require limitations and disclosures.
Human Oversight: Algorithms cannot replace human judgment in sentencing, investigation, or evidence verification.
Bias Risks: AI tools trained on historical data may propagate racial, gender, or socio-economic bias.
Evidence Reliability: AI-assisted evidence must be validated, explainable, and challengeable.
Global Trend: Courts worldwide are starting to address AI in criminal justice, from sentencing to predictive policing and forensic analysis.

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