The phrase “predictive sentencing” makes people uneasy, and probably for good reason.
Any system that claims to predict how a judge should sentence risks becoming a machine that replaces judgment with math and discretion with automation. That is not justice. That is control.
As a result, the Fit-Bench Framework is doing something different.
Fit-Bench does not predict outcomes.
It predicts risk.
And that’s what makes it so important to implement.
The Problem Fit-Bench Is Actually Solving
Most sentencing failures are not caused by bad law. Instead, they are caused by predictable breakdowns in discretionary decision-making:
- guideline drift after role transitions
- overconfidence following promotion
- resistance to appellate correction
- narrative substitution for proportional analysis
- emotional or institutional pressure leaking into legal reasoning
These failures repeat across jurisdictions because the system does not monitor how discretion degrades over time.
FitBench exists to make those failure modes visible before they cause harm.
Curious how it works? Read a Fit Bench Case Study on Judge Michael Schipper here.
Predictive Sentencing Without Predicting Sentences
FitBench never asks:
What sentence should this judge impose?
Instead, it asks:
Under what conditions does sentencing error become likely?
This is the same approach used in aviation, medicine, and emergency management.
No one tells a surgeon how to operate, but systems monitor when surgical error becomes more likely.
Judicial sentencing is a safety-critical function; FitBench treats it like one.
What Fit-Bench Actually Identifies
Fit-Bench does not predict sentences, punishment levels, or case outcomes.
It identifies process risk by detecting repeatable performance patterns across eight observable capacity categories.
Each category captures a different dimension of judicial reliability. Individually, a single indicator may reflect an isolated mistake. When indicators recur across categories, cases, and time, Fit-Bench surfaces pattern convergence, the point at which risk becomes structurally meaningful.
Fit-Bench’s value is not in any one datapoint. It is in how the eight categories interlock to reveal early warning signals that would otherwise be dismissed as anecdotal.
How the Eight Categories Enable Pattern Recognition
Cognitive Tracking captures whether a judge can reliably follow facts, parties, timelines, and procedural posture across hearings. When confusion, inconsistency, or factual drift appears repeatedly, it signals strain in baseline decision-tracking capacity.
Legal Accuracy captures repeated misapplication of controlling law, sentencing rules, evidentiary standards, or constitutional limits. This category distinguishes unpopular outcomes from rule-based error by focusing on whether legal constraints are being applied as designed.
Impulse Control captures volatility, inappropriate escalation, and disproportionate responses to disagreement. These indicators matter because regulated courtroom conduct is a prerequisite for neutral, reviewable decision-making.
Neutrality and Bias Risk captures the introduction of impermissible factors into reasoning, including personal ideology, protected traits, or viewpoint-based punishment. This category also identifies retaliation risk when lawful advocacy or protected speech begins to influence judicial logic.
Procedural Control captures breakdowns in notice, transparency, scheduling, and consistent rule enforcement. Procedural instability often precedes more visible legal error and undermines meaningful appellate review.
Health and Safety Risk Flags capture observable performance indicators that, in other safety-sensitive roles, would prompt confidential evaluation. Fit-Bench does not require medical disclosure. It relies on observable reliability and safety signals only.
Resistance to Correction captures whether errors persist after appellate remand, administrative guidance, or prior reversal. This category distinguishes isolated mistakes from entrenched unreliability by focusing on whether correction changes behavior.
Fiscal and Economic Competence captures repeated failure to account for the public cost of judicial decisions, particularly avoidable incarceration, excessive departures, and high rates of appeal or resentencing that impose measurable taxpayer harm.
What Fit-Bench Flags
When these categories begin to align, Fit-Bench flags danger zones. For example:
- During role transitions, strain may appear simultaneously in cognitive tracking, procedural control, and impulse regulation as discretion expands.
- When the same reasoning reappears after remand, resistance to correction converges with legal accuracy failures.
- When guidelines are acknowledged but dismissed as inadequate, legal accuracy, procedural control, and fiscal competence indicators move together.
- When post-plea conduct or protected speech enters sentencing logic, neutrality and impulse control indicators escalate in tandem.
None of these patterns predict a specific outcome.
They predict where reliability is breaking down.
Why This Framework Matters
Michigan currently relies on rumor, informal concern, or late-stage crisis to assess judicial fitness. Fit-Bench replaces guesswork with a structured, observable screen that allows oversight bodies to identify declining capacity before harm becomes systemic.
Fit-Bench does not accuse. It measures.
It does not diagnose. It detects patterns.
By using eight independent but reinforcing categories, Fit-Bench makes early intervention possible without violating privacy, politicizing outcomes, or waiting for failure to become irreversible.
Why This Does Not Threaten Judicial Independence
FitBench does not constrain discretion.
It does not:
- set ranges
- recommend sentences
- override judicial authority
- score defendants
But it does:
- surface patterns
- warn oversight bodies
- guide appellate triage
- inform training and intervention
Judges still decide sentences.
FitBench simply asks whether the conditions for lawful decision-making are intact.
That preserves independence while reducing harm.
Why Oversight Bodies Quietly Like This
Oversight systems fail when they act too late.
They succeed when they:
- identify risk early
- intervene quietly
- prevent escalation
- avoid scandal
FitBench gives oversight bodies language they already understand:
- corrective resistance
- performance drift
- reliability failure
- institutional risk
It allows intervention before families are destroyed and taxpayers fund endless remands.
Predictive Sentencing as Prevention, Not Control
The goal of predictive sentencing is not to automate justice.
It is to prevent predictable judicial failure.
FitBench does not replace judges.
It protects the system from its own blind spots.
That is how you modernize sentencing without surrendering humanity.
SOURCES
- Michigan Sentencing Guidelines Manual
- People v Lockridge
- Appellate remand patterns in Michigan Court of Appeals unpublished opinions
- Judicial performance oversight models in safety-critical professions
