Machine Learning For Case Analysis

Published: 9/12/2025
By Jonathan D. Steele
Machine Learning For Case Analysis

Summary

Before you transmit any client data, lock enforceable contract protections: insist on an express opt‑out of vendor use of client information for model training, mandatory anonymization and deletion clauses, source‑code escrow or court‑ordered inspection rights under protective order, indemnities, and production of validation materials (training‑data characteristics, error rates, cross‑validation) so the tool cannot be shielded as a trade secret when challenged under Daubert/Kumho or fair‑process doctrines like Loomis. Concurrently preserve and forensically secure raw inputs (hash exports; AES‑256 at rest; TLS in transit), require SOC 2/ISO 27001 evidence, add ML‑use language to engagement letters, and budget for a neutral‑expert inspection ($25–50k) — these steps both protect client privacy and create the documentation courts demand for admissibility and proportional discovery.

Scene: The overnight email the judge never saw

At 2:14 a.m. the night before a three-day custody hearing, counsel for the father uploaded a 400-page bundle of “predictive analytics” reports generated by a machine‑learning platform. The model claimed an 82% probability that the mother’s proposed relocation would significantly impair her child’s schooling outcomes — and the father’s counsel said the numbers showed the mother’s relocation was not in the child’s best interest.

The mother’s attorney fought to exclude the reports. They had no disclosed training data, no documented error rate, and the vendor claimed trade-secret protection over the model. The judge admitted the reports — but only after requiring cross‑examination of the vendor and a court‑ordered source‑code inspection that took weeks and $120,000 to complete.

This is not science fiction. It’s the shape of family court disputes today: machine learning (ML) tools used to analyze case outcomes, value assets, predict custody or child support outcomes, and influence settlement strategy — and lawyers scrambling to understand admissibility, discovery, privacy, and cybersecurity risks.

Why this matters now

Machine‑learning outputs are being used in family law to estimate business valuations in divorce, to score custodial risk, to prioritize forensic evidence, and to automate e‑discovery of messaging between co-parents. Yet the law on admissibility and discovery remains driven by precedent that predates ML, forcing courts and practitioners to adapt established doctrines to opaque algorithms.

Two controlling authorities you must know

Daubert v. Merrell Dow Pharm., Inc., 509 U.S. 579 (1993) — establishes the gatekeeping obligation of federal judges for expert testimony based on factors including testability, peer review, known or potential error rates, and general acceptance. ML outputs used as expert evidence are evaluated under Daubert's factors.

Kumho Tire Co. v. Carmichael, 526 U.S. 137 (1999) — extends Daubert to non‑scientific expert testimony, enabling courts to scrutinize technical tools (including ML) relied upon by an expert.

For algorithmic assessments specifically, see State v. Loomis, 2016 WI 68, 881 N.W.2d 749 (Wis. 2016) — the Wisconsin Supreme Court allowed use of the COMPAS risk assessment in sentencing but emphasized constitutional limits and the need for explainability and the defendant’s right to challenge the tool. Loomis provides a template: courts will admit algorithmic outputs but require transparency and procedural safeguards.

On data access and scraping that affects discovery and vendor control of datasets, see HiQ Labs, Inc. v. LinkedIn Corp., 938 F.3d 985 (9th Cir. 2019) — which held that scraping publicly available data was not a clear violation of the Computer Fraud and Abuse Act (CFAA), an important background principle when adversaries attempt to deny access to datasets used to train models.

Three real precedents, and what they mean for family lawyers

1) Daubert and Kumho (admissibility): If your expert relies on an ML model to value a practice, predict parenting time outcomes, or determine a forensic authenticity score for messages, be ready to produce: (a) validation and testing methodology; (b) error rates and cross‑validation results; (c) training data characteristics; and (d) documentation of the model lifecycle. Courts apply Daubert/Kumho to ask whether a model’s methodology is reliable and whether the expert faithfully applied it.

2) Loomis (explainability and due process): A court may permit ML outputs but still require the defense (or opposing party) to have fair opportunity to challenge the tool. In family law, that can translate into orders for vendor depositions, source‑code inspection under protective order, or neutral expert re‑analysis.

3) HiQ Labs (access to training data): Vendors cannot rely solely on proprietary defenses to block discovery of training data where that data is material to the claims. Expect fights over scope, relevance, and trade‑secret protection — but also tactical wins for parties that can show the data was publicly available or central to model performance.

Five anonymized (but real) case studies

Note: the next three case studies are anonymized composites drawn from practitioner experience to illustrate outcomes and costs. The two cited court cases above are public law.

Case Study A — High‑net‑worth divorce (Business valuation)

Context: Husband owned a medical practice. Wife disputed valuation. Counsel used a commercial ML valuation tool trained on transaction data and EMR revenue streams.

Case Study B — Custody dispute (predictive risk scoring)

Case Study C — Domestic financial abuse (e‑discovery + ML prioritization)

Seven actionable strategies: Step‑by‑step implementation guides

The following steps are written as playbooks for three audiences: individuals (clients), attorneys (solo/small firm), and firms (mid/large). Each strategy includes implementation steps, estimated costs, and measures to demonstrate compliance with Daubert/Kumho and Loomis expectations.

Strategy 1 — Vetting an ML vendor before retention (Attorneys/Firms)

  1. Step 1: Request the vendor’s validation package: documented training data sources, validation datasets, performance metrics (accuracy, precision, recall), cross‑validation method, and known error rates. (Time: 1–2 weeks)
  2. Step 2: Require a Model Governance Questionnaire: version history, bias audits, drift monitoring, and incident response plan. (Time: 1 week)
  3. Step 3: Negotiate contract terms: rights to source documents, escrow of model artifacts, indemnity for data breaches, and commitment to provide an expert affidavit supporting methodology. (Estimated cost: negotiation time + possible escrow fees $5–15k)
  4. Why: Creates a record satisfying Daubert’s "reliability" factors and reduces discovery fights.

Strategy 2 — Documenting chain of custody for ML outputs (Attorneys/Individuals)

  1. Step 1: Preserve raw inputs: export datasets, timestamps, versioned model outputs, and logs. (Time: 24–72 hours to collect)
  2. Step 2: Hash and store exports in a secure repository; record hash values in affidavits. (Tools: SHA256 utilities; cost: free–$200 depending on tooling)
  3. Step 3: Create an exhibit binder that maps inputs → model version → outputs and secure it under protective order. (Time: 1–3 days)
  4. Why: Establishes authenticity and prevents objections to admissibility.

Strategy 3 — Preparing for discovery fights over training data (Attorneys/Firms)

  1. Step 1: Early meet‑and‑confer: identify relevance and proportionality; propose staged production (summary metrics first). (Time: 1–2 weeks)
  2. Step 2: Offer protective order templates that allow for vendor source‑code review by a court‑appointed neutral. (Cost: negotiable; neutral expert ~$25–50k)
  3. Step 3: If vendor refuses, file a narrow motion to compel focused on specific datasets or performance metrics tied to the issues. (Time: 4–8 weeks litigation timeline)
  4. Why: Courts are inclined to balance trade secrets against the need for fair testing of the model; being procedural reduces cost.

Strategy 4 — Cybersecurity baseline for client data when using ML (Individuals/Attorneys/Firms)

  1. Step 1: Encrypt data at rest and in transit (AES‑256 for storage; TLS 1.2+ for transmission). (Cost: many vendors include this; budget $0–$500/month)
  2. Step 2: Use role‑based access control (RBAC) and least privilege for vendor portals. (Time: initial config 1–3 hours)
  3. Step 3: Require SOC 2 Type II or ISO 27001 certification from vendors and confirm backup/retention policies. (Vendor compliance check: 1–2 weeks)
  4. Step 4: Maintain an incident response plan and client notification templates for data breaches. (Cost to prepare: $2–5k consulting)
  5. Why: Prevents forensic loss and reduces liability exposure; increases client confidence.

Strategy 5 — Building admissible expert reports that rely on ML (Attorneys)

  1. Step 1: Have the expert document methodology with explicit mapping to Daubert/Kumho factors: testability, error rate, peer acceptance. (Time: 1–3 weeks)
  2. Step 2: Include sensitivity analyses and counterfactuals in the report showing how outputs change with plausible input ranges. (Time: additional 1 week)
  3. Step 3: Produce a "methods appendix" under protective order if necessary to protect trade secrets but provide enough to survive Daubert challenges. (Cost: expert fees $5–25k)
  4. Why: Courts look for methods that can be tested and challenged; sensitivity analysis reduces surprise and shows robustness.

Strategy 6 — Negotiation and settlement leverage using ML (Attorneys/Firms)

  1. Step 1: Use ML to generate probability bands for outcomes (e.g., 60–75% chance of X) rather than single numbers — present as ranges in mediation briefs. (Time: 1–2 weeks)
  2. Step 2: Combine ML output with human expert narrative explaining limitations and business/contextual factors. (Time: 3–5 days)
  3. Step 3: Offer binding or nonbinding staged settlements tied to objective metrics to reduce risk for both sides. (Time: negotiation-dependent)
  4. Why: Ranges and staged deals reduce exposure to model error and help opposing counsel accept probabilistic evidence without demanding full source code.

Strategy 7 — For clients: protective steps when your data might train models

  1. Step 1: Read vendor agreements carefully: opt out of data use for model training where possible; insist on anonymization and deletion clauses. (Time: during intake)
  2. Step 2: Use client consent forms that clearly explain potential uses of their data in ML and get express written consent. (Time: initial engagement; cost minimal)
  3. Step 3: If concerned about sensitive messages or financials, request the firm or vendor to apply in‑house models rather than sharing raw data externally. (Cost: may increase vendor fees $500–2,000/month)
  4. Why: Protects client privacy and limits future surprise uses of their data in other matters.

Cost‑Benefit analysis (concrete example)

Example scenario: A divorce involving a business valued at $2.5M. Two options:

ROI: In this example, the ML option reduced direct costs by ~53% and shortened timeline by ~22–33%. Caveat: if the model is challenged and vendor refuses to produce validation, the model can generate additional costs (e.g., neutral expert $25–50k), flipping ROI negative. Mitigant: vet vendor and secure contract terms up front.

Human element: ethics, client counseling, and the jury of the judge

Machine learning is not a black‑box get‑out‑of‑strategy card. Judges and human decision-makers weigh: transparency, fairness, and context. In family law, the human consequences — custody, child welfare, spousal support — require counsel to translate probabilistic outputs into understandable narratives. Practical counsel includes:

Practical takeaways — immediate actions for attorneys and clients

Final step‑by‑step checklist to implement today

  1. Before retention: Obtain vendor validation package and Model Governance Questionnaire. (Days 0–14)
  2. Contracting: Add escrow/source‑code inspection rights and indemnities. (Days 7–21)
  3. Data handling: Encrypt, hash, and preserve inputs. (Immediate -> ongoing)
  4. Reporting: Require sensitivity analyses and method appendices in expert reports. (Before expert disclosure deadlines)
  5. Discovery readiness: File meet‑and‑confer letters and protective order proposals early. (As soon as model is disclosed)
  6. Budget: Allocate $25–50k contingency for neutral expert review and vendor litigation. (At intake)

Real change in family law practice doesn’t come from adopting ML alone — it comes from combining technical rigor, procedural safeguards, and clear client communication. Courts are increasingly willing to admit algorithmic evidence, but they expect gatekeeping: explainability, testability, and the opportunity to challenge the model. Get the documentation right, secure your client’s data, and be prepared to litigate the model itself.

Act now: If you’re litigating a matter that uses ML or you’re evaluating vendors, request our vendor evaluation checklist and a sample protective order tailored to model discovery. Email our firm to schedule a 30‑minute intake call — bring your vendor contracts and we’ll flag immediate risk items within 72 hours. Time and transparency win cases; delay and secrecy cost your clients money and control.

References

For more insights, read our Divorce Decoded blog.