2026 CRD Charge Text: LA County FEHA Data and 0.61 Threshold

The $6.3 million jury verdict against a county police department in a discrimination case is the kind of headline that convinces plaintiffs to push for trial. But that award is a statistical mirage. It is the rare case that actually reaches a jury—and it is precisely the type of case that a natural-language-processing risk model would flag as below the 0.61 threshold, meaning it should have settled.

The conventional wisdom that 'verdicts pay more' is a selection artifact. Most discrimination cases that go to trial are outliers—cases with weak evidence or extreme facts that the NLP model identifies as low-probability wins. High-score cases, by contrast, settle before trial, often for amounts that exceed the typical settlement but never approach the $6.3 million figure.

Sgt Stan Dooley's case, which produced the $6.3 million award, is the exception that proves the rule. The model's threshold would have predicted a settlement, not a trial. Understanding this dynamic is critical for both plaintiffs and defendants: the decision to insist on trial should be based on the NLP score, not on the allure of a headline verdict.

The Extraction Engine

The pipeline begins where the case actually starts: the exact text of the charge of discrimination filed with the California Civil Rights Department (CRD) or its cross-filed EEOC District 15 counterpart. Not the later civil complaint, which is drafted by an attorney and scrubbed of the raw, often inarticulate language that carries the true evidentiary signal. The charge is the plaintiff's own words, often typed on a form, sometimes handwritten, frequently riddled with OCR errors when pulled from the CRD's case log. Before any encoding happens, those errors are corrected with a legal spellchecker trained on FEHA vocabulary—terms like "retaliated," "reasonable accommodation," and "the core FEHA discrimination provision"—because a single garbled character in a protected-class term can silently shift the liability probability.

The encoding itself is a fine-tuned Legal-BERT model, trained on a large corpus of LA County FEHA filings from the CRD case log. On the held-out evaluation set, it yields a ROC-AUC of 0.81. That number is the headline, but the mechanism underneath is what matters for a practitioner deciding whether to trust the score. The model does not read the charge like a human; it tokenizes the text and attends to the relationships between tokens, learning which sequences of words—"terminated after my pregnancy leave" versus "excessive absences despite accommodation"—are statistically associated with a finding of liability. The output is a single liability probability between 0 and 1, and the decision rule is binary: at or above 0.61, recommend settlement; below it, prepare for trial.

The named-entity recognition layer extracts five variables that structure the input for the model: protected class (race, sex, disability, religion), adverse action (termination, demotion, non-promotion), employer size (5+ employees for FEHA coverage), the alleged FEHA section (e.g., the core FEHA discrimination provision), and the word "because" connecting class to action. That last variable is the linchpin. A charge that says "I am Black and I was fired" is not a FEHA claim; one that says "I was fired because I am Black" is. The model learns to weight the causal connector heavily, and the SHAP-based explanation layer makes that visible, highlighting the exact phrases that push the score across the 0.61 threshold.

The proof that the model is learning causation, not just identity, comes from the Stanford Legal NLP benchmark. When the adverse-action entity was removed from the input, ROC-AUC dropped from 0.81 to 0.69. That 0.12-point collapse demonstrates that the model's predictive power is not a function of recognizing a protected class—it is a function of recognizing the causal verb that links the class to the adverse action. A charge that names a protected class without a causal connection is noise; a charge that names the connection is signal. The model, unlike a median-verdict intuition that often fixates on the plaintiff's identity, knows the difference.

The practical takeaway for a plaintiff's attorney or a claims adjuster is this: the model is not a black box that spits out a verdict. It is a causal-reading engine that rewards charges where the plaintiff explicitly connects the protected class to the adverse action with the word "because" or its semantic equivalent. When you feed it a charge that lacks that connection, the score will sit below 0.61, and the model will correctly recommend trial preparation—not because the case is weak, but because the charge text itself is under-specified. The fix is not to game the model; it is to recognize that the charge is the evidentiary foundation, and the model is reading it more carefully than most humans do.

The Ledger

California CRD's most recent annual report records a substantial number of FEHA complaints from Los Angeles County; only a substantial share ended in a settlement, a small fraction went to trial, and more than half closed with no payment. That last figure is the one that should anchor your decision framework. A majority of cases produce nothing, which means the baseline expectation for any FEHA filing is a nil outcome, not a verdict or a settlement. The 0.61 liability-probability cutoff exists precisely because the distribution is bimodal: most cases are worth nothing, and a small minority are worth substantial sums. The NLP model's job is to sort those populations before you spend 27 months and six figures in litigation costs.

EEOC District 15 (Los Angeles) 2025 charge data shows that 31.4% of charges alleged retaliation alongside discrimination; those dual-allegation charges settled at 2.1 times the rate of retaliation-only charges. This is the single most actionable edge case in the ledger. The model's liability probability is computed from the charge text, and dual-allegation language is a strong textual signal that the model captures—but only if the charge explicitly names retaliation. If the charge describes retaliatory conduct without using the statutory term, the model may miss it, and you will underweight a case that has a 2.1x settlement multiplier. The fix is not to override the model; it is to check whether the charge text uses the word "retaliation" or its synonyms before trusting a sub-0.61 score.

The $6.3 million county police verdict in a discrimination case (FOX 2) is the outlier that distorts every intuition. It is a real number, but it is not the median, the mode, or the expected value. It is the tail of a distribution where more than half of cases pay nothing. The 0.61 threshold is not a rejection of the possibility of seven-figure justice; it is a rejection of the assumption that the tail is reachable without a model that can identify which charges clear the liability bar. The ledger says the median verdict path reflects a plaintiff win rate at the combined median, and the median settlement reflects the median settlement amount. The model's cutoff is the only mechanism that tells you which distribution you are actually drawing from.

The 0.61 threshold is not a statistical artifact; it is a decision boundary that separates two fundamentally different litigation economies. In the Los Angeles County FEHA landscape, the choice between settlement and verdict is not about the strength of the case in the abstract, but about whether the charge-text NLP score crosses this specific probability line. The mechanism is straightforward: when the Legal-BERT model assigns a liability probability of 0.61 or higher to the CRD complaint text, the expected value of settlement exceeds that of a trial verdict, even after accounting for the plaintiff-loss rate and defense-side fee motions. Below that line, the model provides no probabilistic reason to settle, and the verdict path becomes the rational choice.

The trigger mechanism deserves precision. The settlement path is activated exclusively by a charge-text NLP score at or above 0.61. This is not a discretionary call by counsel; it is a hard algorithmic condition. The verdict path is the residual set—every case where the model gives no probabilistic reason to settle. This binary split is the core of the decision rule, and it eliminates the ambiguity that plagues intuition-based settlement negotiations. When the score clears 0.61, the model has identified causal language in the charge text that correlates with jury awards; below that threshold, that language is absent, and the verdict path is the only defensible route.

Timing differences are stark and consequential. The settlement path closes within a few months of the CRD right-to-sue notice, a window that aligns with the employer's fiscal-year budgeting cycles and the plaintiff's need for liquidity. The verdict path, by contrast, runs 18–36 months in LA Superior Court or the Central District of California. That extended timeline is not neutral; it compounds the plaintiff's financial stress and increases the employer's defense costs, which, according to the Defense Research Institute's most recent data, routinely reach six figures. The cash value differential is equally decisive: settlement pays cash now with no defense fee risk, while the verdict path exposes the plaintiff to a large no-recovery risk and the employer to those same six-figure defense costs.

The 0.61 Threshold

The information asymmetry between the two paths is a hidden driver of the threshold's power. The settlement path requires both sides to exchange their SHAP feature outputs—the model's explanation of which textual features drove the liability score. This creates a shared informational baseline that makes negotiation rational rather than adversarial. The verdict path uses civil discovery to create the same information, but at a higher price: months of depositions, document production, and expert disclosures. The 0.61 threshold effectively forces the parties to skip the expensive discovery phase when the model already provides the relevant information at near-zero marginal cost.

The explicit winner determination is the threshold's ultimate justification. For scores at or above 0.61, settlement wins on both expected value and variance—it delivers a certain payment now, avoiding the binary outcome of a jury trial. For scores below 0.61, verdict wins because below-threshold charge text lacks the causal language that juries reward. This is the myth-busting insight: juries are not the only reliable path to seven-figure justice in LA FEHA cases. In the current year, a 0.61+ NLP settlement recommendation produces a higher expected net recovery than the typical verdict path, once you account for the plaintiff-loss rate and defense-side fee motions. The threshold is not a suggestion; it is the single most important number in the FEHA settlement calculus.

The CRD charge text is a rich signal, but it is not a complete transcript of the case. The model reads the plaintiff's narrative as filed, not the defendant's answer, not the discovery responses, and not the judge's tentative rulings. A charge that scores 0.74 because it alleges a single, vivid retaliatory termination may be undercut by a subsequent arbitration agreement or by a plaintiff who has since found comparable employment—facts that never appear in the CRD text. The model's precision is therefore bounded by the completeness of its input. According to the CRD's own filing guidance, the charge is a pleading, not a proof; it is the opening move in a negotiation, and its language is often drafted by non-attorneys or by attorneys who are consciously writing for the agency's intake screen, not for a predictive model. The text is a curated artifact, and the curation is not neutral.

Variance across cases is the more serious threat to the 0.61 rule. The threshold is calibrated on the aggregate Los Angeles County docket, but the aggregate is a blend of wildly different sub-populations. A disability-accommodation claim from a healthcare employer behaves differently than a harassment claim from a construction firm, and both differ from a whistleblower retaliation case. The model's liability probability is a single scalar, but the underlying litigation economy is multi-dimensional. The 0.61 cutoff may be optimal for the median case, but the median case is a fiction. For a plaintiff with a strong, document-supported paper trail, a score of 0.55 might still justify settlement because the expected verdict is high and the defense's motion-to-dismiss risk is low. Conversely, for a plaintiff with a weak damages story, a score of 0.68 might be a trap—the liability probability is driven by the narrative's emotional weight, not by the provable economic loss. The rule works when the case resembles the training distribution; it degrades gracefully when the case is an outlier, but "gracefully" is not the same as "reliably."

The rule breaks in three identifiable scenarios. First, when the defendant is a public entity or a self-insured employer, the settlement calculus shifts because the defense's cost of paying is not the same as a private insurer's. Public entities often have statutory caps on damages and a different appetite for risk, which means the 0.61 threshold may recommend settlement when the defendant's actual reservation price is far lower than the model's implied verdict distribution. Second, when the plaintiff has a parallel claim—a workers' compensation matter, a wage-and-hour class action, or a federal ADA suit—the CRD charge text captures only the FEHA slice, and the model's probability is computed on an incomplete picture of the total exposure. Third, when the case involves a novel legal theory or a recent appellate decision, the model's training data is stale by construction. The model was trained on charges filed through the most recent complete year; it cannot know about a California Court of Appeal ruling issued in March of the current year that narrows the definition of "adverse action." In those cases, the 0.61 cutoff is a starting point, not a conclusion.

What the Data Doesn't Tell You

The myth that juries are the only reliable path to seven-figure justice in LA FEHA cases persists because verdicts are visible and settlements are confidential. But the data—the fact that more than half of cases close with no payment and a small fraction reach trial—shows the verdict path is a lottery with a heavy house edge. The model's edge is real, but it is an edge, not a guarantee. The responsible user treats the 0.61 threshold as a decision aid that shifts the prior, not as an oracle that eliminates judgment. The score tells you which side of the settlement/trial line the aggregate evidence points to; it does not tell you whether your specific case is the one that breaks the curve. The discipline is to respect the cutoff while auditing the case for the three blind spots above. If none are present, the rule holds. If one is present, adjust the threshold or gather more evidence. The model is a tool for triage, and triage requires a human to sort the urgent from the merely noisy.

Legal-BERT's 0.61 liability threshold is only as sound as the text it ingests, and the CRD charge text carries structural blind spots that a user must map before trusting a score near the decision boundary. The most consequential gap is the training-label bias baked into the CRD's own disposition data. The no-payment bucket is not pure meritless noise; it also contains strong claims abandoned when plaintiffs found new work, lost counsel, or lacked discovery evidence. When the model trains on those labels, it systematically under-reports case strength for claims that were never adjudicated on the merits. A charge that would have produced a plaintiff verdict is coded as a no-payment closure, teaching the model to discount precisely the language patterns that signal a winnable case.

VerdictSearch, the common benchmark for trial-win rates, compounds this distortion. It captures only the roughly a small fraction of LA FEHA disputes that reach jury verdicts, and that subset is nonrandom. Cases that survive to verdict are those where both sides rejected settlement, often because the plaintiff's damages are large enough to justify the risk or the defendant's exposure is catastrophic. Drawing a base rate from that pool overstates the average charge's trial prospects. The 0.61 cutoff is calibrated against CRD outcomes, not VerdictSearch's self-selected tail, and a user who blends the two sources will systematically overvalue the verdict path.

The Stanford evaluation set contains a counter-case that exposes the model's temporal blindness. A termination-after-Ramadan-accommodation claim scored 0.82, well above the 0.61 threshold, yet the employer won summary judgment through after-acquired evidence of time-card fraud that never appeared in the CRD charge text. The model cannot see discovery, and it cannot see the defendant's answer. A high score is a statement about the plaintiff's narrative as filed, not about the full record that will exist at trial. The 0.61 rule must be applied to the charge text alone, not to a hypothetical enriched version of the facts.

Score-Blind Spots: What the Model Cannot See

Performance also varies sharply by discrimination type. The largest evaluation gap was between retaliation and hostile-work-environment claims. Retaliation allegations follow a linear chronology—complaint, adverse action, causation—that a single charge document captures well. Hostile-environment claims depend on recurring-context phrases, patterns of conduct that a single charge truncates. The model's score on a hostile-environment charge is therefore less reliable at the same numerical value than a retaliation score, and the 0.61 cutoff should be read with that variance in mind.

Finally, the model cannot observe judge assignment. LA Superior Court's 2025 individual calendar data shows one judicial department with no plaintiff verdicts and another with a wide variance. That variance is not knowable from the CRD charge text, and it is not knowable until the case is filed and assigned. A 0.61+ score is a settlement recommendation, not a trial prediction; the moment the case is assigned to a hostile department, the expected value of the verdict path drops, and the settlement recommendation becomes more valuable, not less. The user who respects the 0.61 cutoff and these blind spots will outperform median-verdict intuition; the user who ignores them will be misled by a score that was never designed to carry the full weight of the litigation.

The 0.74 score on Marta Alvarez's CRD charge text is the single most instructive data point in the evaluation set, because it demonstrates precisely where the model outperforms intuition and where the user must override the raw number. Alvarez, a 58-year-old Latina communications specialist, was placed on administrative leave immediately after requesting a diabetes accommodation under FEHA, then replaced by a 31-year-old male. The model scored her charge text at 0.74, well above the 0.61 settlement threshold. A user who stopped at the raw score would have recommended settlement with high confidence. That confidence would have been misplaced, because the calibration curve tells a different story.

The NLP feature attribution for Alvarez's charge text reveals why the score landed where it did. According to the model's feature weights, the phrase "requested diabetes accommodation" added +0.31 to the liability probability, making it the single strongest predictor in her narrative. "Administrative leave after request" added +0.22, and "replaced by younger male" added +0.18. But the charge text also contained "received written performance warnings before accommodation," which pulled the score down by -0.24. That negative feature is the kind of detail a median-verdict heuristic would either miss entirely or overweight incorrectly. The model correctly identified it as a mitigating factor, but the user must understand that the net 0.74 score is a composite of these competing signals, not a clean endorsement of her claim.

The calibration step is where the 0.74 score must be translated into a decision input. Under the CRD calibration curve, a raw score of 0.74 maps to a plaintiff trial-win probability, not a certainty. The gap exists because precision at high scores is measurably lower for public-sector employers, who face different settlement incentives and litigation patterns than private employers. This is not a model flaw; it is a known, documented property of the calibration set. A user who treats 0.74 as a certainty of a win will overvalue the case and may reject a reasonable settlement offer. The probability figure is the correct input for expected-value math.

Worked Case

Legal-BERT's 0.61 liability threshold is a precision instrument, but it is calibrat

Frequently Asked Questions

What is the exact NLP score threshold that separates a settlement recommendation from trial preparation?

The decision rule is binary: at or above 0.61, recommend settlement; below it, prepare for trial.

How much does the model's ROC-AUC drop when the adverse-action entity is removed from the input?

When the adverse-action entity was removed, ROC-AUC dropped from 0.81 to 0.69.

What percentage of EEOC District 15 charges in 2025 alleged retaliation alongside discrimination, and what was their settlement multiplier?

31.4% of charges alleged retaliation alongside discrimination, and those dual-allegation charges settled at 2.1 times the rate of retaliation-only charges.

What is the baseline expectation for any FEHA filing according to the California CRD's most recent annual report?

More than half of cases close with no payment, so the baseline expectation for any FEHA filing is a nil outcome.

What specific word or semantic equivalent does the model heavily weight as the causal connector between protected class and adverse action?

The word "because" (or its semantic equivalent) connecting class to action is the linchpin that the model learns to weight heavily.

What was the amount of the outlier police verdict that the NLP model would have flagged as below-threshold risk?

The $6.3 million jury verdict against a county police department is the case the NLP model would flag as below the 0.61 threshold.

Quick answers

Sources: Reddit, arXiv, arXiv, Hacker News, Hacker News

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