State v. Loomis
The leading state supreme court decision defining constitutional limits on the use of a proprietary algorithmic risk assessment at criminal sentencing.
Executive Summary
Eric Loomis challenged a Wisconsin sentence after the presentence investigation report included scores from COMPAS, a proprietary risk and needs assessment. He argued that reliance on a model whose internal weighting he could not inspect violated due process and that its use of gender denied him an individualized sentence. The Wisconsin Supreme Court affirmed. It concluded that COMPAS could be considered when accompanied by specified cautions and used only to supplement—not replace—the sentencing court's independent judgment. The court emphasized that a COMPAS score may not determine whether a defendant is incarcerated, set the severity or length of a sentence, or become the determinative answer to whether community supervision is appropriate.
Loomis is binding in Wisconsin and influential elsewhere. Its importance in 2026 lies as much in its warnings as in its result: prediction is probabilistic, group-based data does not identify what one person will do, proprietary design limits scrutiny, performance may vary across populations, and a model can drift as conditions change.
Key Holdings at a Glance
Facts and Sentencing Record
Loomis was charged in connection with a drive-by shooting. He denied participating in the shooting but admitted driving the vehicle involved. He pleaded guilty to attempting to flee or elude a traffic officer and operating a motor vehicle without the owner's consent. Other charges were dismissed and read in for sentencing.
The presentence investigation report included a COMPAS assessment that classified Loomis as high risk for pretrial misconduct, general recidivism, and violent recidivism. The circuit court imposed consecutive sentences totaling six years of initial confinement followed by five years of extended supervision.
What COMPAS Is—and Is Not
COMPAS stands for Correctional Offender Management Profiling for Alternative Sanctions. The assessment uses information about a person and comparison data to produce classifications intended to assist correctional management, supervision, treatment, and risk-related decisions.
| Concept | Function | Legal Caution |
|---|---|---|
| Risk assessment | Estimates the likelihood of a defined outcome for members of a comparison group | It is not a factual finding that a particular person will reoffend |
| Needs assessment | Identifies factors potentially relevant to services, treatment, or supervision | A need classification should not be converted automatically into punishment |
| Risk classification | Places a score into a category such as low, medium, or high | Cut points and labels can obscure uncertainty and error rates |
| Professional judgment | Applies law and case-specific facts to an individual decision | The accountable official—not the software—must make and explain the decision |
Procedural History and Result
Loomis sought postconviction relief and requested resentencing, arguing that the circuit court had relied on inaccurate or impermissible information. The circuit court denied relief. The Wisconsin Court of Appeals certified the case to the Wisconsin Supreme Court because use of algorithmic risk assessment at sentencing presented an issue of statewide importance.
The Due Process Framework
A defendant has a due process right not to be sentenced on materially inaccurate information. The defendant generally must show both that information was inaccurate and that the sentencing court actually relied on it.
Loomis focused on the proprietary nature of COMPAS, the inability to inspect its internal weighting, the use of group data, and gender-based scoring. The supreme court evaluated those objections against the limited role the score played in the complete sentencing record.
Proprietary Algorithm and Ability to Respond
COMPAS was proprietary, and Loomis could not inspect how the developer weighted variables or calculated risk. The court nevertheless found no due process violation because he could review and challenge the factual inputs and the resulting scores, and because the sentencing court did not use the output to determine incarceration or sentence length.
That holding is narrow. It does not create a general rule that trade-secret status defeats disclosure, that source code is never relevant, or that a vendor's output is presumptively reliable. A different system, decision, record, error, or degree of reliance may present materially different due process, discovery, evidentiary, statutory, or state constitutional questions.
Individualized Sentencing and Independent Reasons
Wisconsin sentencing requires consideration of the individual defendant and offense. The supreme court concluded that Loomis received an individualized sentence because the judge discussed case-specific facts independent of COMPAS.
A defensible decision record should identify which legally permissible factors mattered, how the evidence supports each factor, what role—if any—the model played, and why the same legal conclusion does not rest on the score alone. Merely stating that a human made the final decision is insufficient if the record shows practical automation bias.
Gender and Potential Bias
Loomis argued that the assessment's use of gender violated due process. The court rejected that claim on the record before it, reasoning that gender-specific data was used to improve statistical accuracy and that the sentencing judge did not actually rely on gender in selecting the sentence.
The court did not decide an equal protection challenge because one was not presented. The opinion therefore should not be cited for the broad proposition that using sex or another protected characteristic in an automated decision is always lawful.
Uses Loomis Identified as Permissible
Subject to the opinion's cautions, the court described a limited role for risk and needs information:
- Identifying low-risk individuals who otherwise might be incarcerated and could be considered for alternatives.
- Helping assess whether risk may be managed safely in the community, so long as the score is not determinative.
- Informing the level and conditions of probation or supervision.
- Identifying treatment, intervention, and service needs.
Each use still requires lawful authority, a tool validated for the actual purpose and population, accurate data, trained reviewers, an opportunity to correct material errors, and a documented exercise of independent judgment.
Uses Loomis Prohibited
| Prohibited Role | Why It Is Improper | Required Alternative |
|---|---|---|
| Deciding whether to incarcerate | The tool was not developed to determine punishment and cannot replace individualized sentencing | Apply governing law to verified case-specific facts |
| Determining sentence severity | A group prediction cannot supply the legal measure of blame or punishment | Explain the lawful sentencing factors independently |
| Determining sentence length | The score does not establish the proper duration of confinement | Base duration on statute, precedent, and individualized reasons |
| Determinatively denying community supervision | Risk output is supplementary information, not the final answer | Conduct a documented, human review of feasibility and conditions |
Required Advisement and Cautions
The court directed that any presentence investigation report containing a COMPAS assessment include a written advisement explaining the tool's limitations. The warning should remain current as research and the system change.
Human Judgment, Explanation, and Accountability
Loomis requires more than a nominal human in the loop. The accountable decision-maker must understand the output's limited meaning, verify material data, consider contrary evidence, exercise genuine discretion, and give reasons independent of the model.
| Control | Operational Requirement |
|---|---|
| Authority | Define who may use the tool and for which decisions |
| Competence | Train users on intended use, error, uncertainty, and prohibited inferences |
| Contestability | Provide a practical method to inspect and correct material inputs |
| Explanation | Record lawful, case-specific reasons that stand without the score |
| Override | Permit and document informed disagreement with the output |
| Audit | Preserve version, inputs, output, user actions, and downstream reliance |
Algorithmic Decision Systems in 2026
Public agencies now encounter predictive models, machine-learning classifiers, generative AI summaries, biometric scores, automated alerts, and vendor decision-support platforms. These systems differ technically from COMPAS, but Loomis supplies durable questions about purpose, transparency, validation, bias, data quality, and the boundary between advice and decision.
Generative systems add distinct risks: fabricated facts, unstable answers, prompt sensitivity, hidden model changes, and persuasive explanations that may not reveal the actual basis of an output. A COMPAS-style warning does not by itself make a generative output fit for a liberty-affecting decision.
Practical Guidance for Courts and Public Agencies
Algorithmic Assessment Review Checklist
| Question | Evidence to Preserve |
|---|---|
| What exact decision and legal authority govern? | Policy, statute, rule, order, and approved-use statement |
| Was the system designed and validated for this use? | Technical documentation, validation studies, dates, and populations |
| Which version produced the output? | Model identifier, software version, configuration, and threshold |
| Are the inputs accurate and legally obtained? | Source records, corrections, provenance, and access history |
| How does performance vary across groups? | Subgroup error, calibration, false-positive, and false-negative results |
| Can the affected person meaningfully respond? | Notice, understandable explanation, correction process, and timing |
| Did a qualified person exercise independent judgment? | Reviewer identity, analysis, override, and case-specific reasons |
| Can the complete event be reconstructed? | Inputs, output, logs, user actions, exports, and retention controls |
Litigation and Discovery Checklist
- Identify the model, vendor, version, configuration, and stated intended use.
- Determine exactly how the output entered the decision and who saw it.
- Obtain the input data, source records, corrections, and score report.
- Compare the deployed use with validation studies and contractual representations.
- Examine subgroup performance, calibration, error rates, and known limitations.
- Preserve audit logs, prompts, outputs, model updates, user notes, and overrides.
- Review warnings given to the decision-maker and affected person.
- Separate risk prediction, needs identification, factual findings, and legal judgment.
- Identify independent reasons and test whether the result stands without the output.
- Evaluate due process, equal protection, evidence, discovery, statutory, contractual, and state-law claims separately.
Frequently Asked Questions
What did State v. Loomis hold?
It held that the limited consideration of COMPAS in Loomis's sentencing did not violate due process because the score was not determinative, specified warnings applied, and the judge gave independent reasons for the sentence.
Did the court approve algorithmic sentencing?
No. It allowed one risk assessment to play a restricted supplementary role. The court prohibited using the score to decide incarceration or determine the severity or length of a sentence.
Did Loomis have access to the source code?
No. The model was proprietary. The court found no due process violation on this record because Loomis could challenge the inputs and scores and because the assessment's role was circumscribed.
Does Loomis mean proprietary algorithms never require disclosure?
No. The opinion does not establish that rule. Disclosure obligations may depend on the tool, legal claim, jurisdiction, consequence, contested issue, and degree of actual reliance.
What warning must accompany COMPAS?
The presentence report must explain proprietary limitations, group-versus-individual prediction, bias concerns, local-validation limitations, the need for monitoring and renorming, and that COMPAS was not developed to determine sentences.
Is Loomis binding nationwide?
No. It is a Wisconsin Supreme Court decision. It is binding on Wisconsin courts and persuasive elsewhere, subject to federal law and each jurisdiction's constitution, statutes, rules, and precedent.
Does the decision apply directly to generative AI?
No. Loomis addressed COMPAS at sentencing. Its concerns are instructive, but generative AI presents additional reliability, traceability, and fabrication risks requiring separate analysis.
Primary Authorities
Wisconsin Supreme Court opinion addressing COMPAS, due process, proprietary scoring, gender, individualized sentencing, required warnings, and prohibited uses.
Read the complete State v. Loomis opinion
Applicable sentencing law should be reviewed in its current form together with controlling decisions and local procedure.
Review Wisconsin Statutes § 973.01
Final Assessment
State v. Loomis is neither a blanket approval nor a categorical rejection of algorithmic tools. It is a warning-bound decision allowing a proprietary risk assessment to supplement an individualized sentencing record while sharply limiting what the score may do.
The practical lesson is governance. Before an algorithm affects a high-consequence public decision, officials should define its permitted purpose, validate it for the actual context, disclose material limits, provide a method to correct errors, measure disparate performance, preserve an audit trail, and require a qualified person to give independent reasons grounded in law and verified facts.