AI Accident Reconstruction in Michigan PI: MRE 702 After 2024
AI-driven accident-reconstruction packages now process EDR downloads, dashcam video, drone scene scans, and physics-engine vehicle dynamics in hours rather than weeks. The question for Michigan personal-injury litigators in 2026 is no longer whether to use them, but how to get the resulting opinions past the gate that the Michigan Supreme Court tightened with its 2024 amendment to MRE 702.
The 2024 MRE 702 Amendment Changed the Standard
Effective May 1, 2024, the Michigan Supreme Court amended MRE 702 to track the 2023 amendment to Federal Rule 702. Two substantive changes matter for AI-reconstruction proofs:
- Burden made explicit. The proponent must establish to the court that it is “more likely than not” — a preponderance standard — that the testimony satisfies each admissibility requirement, including reliable application of principles and methods to the facts.
- Reliable application emphasized. The expert’s opinion must reflect a reliable application of the principles and methods to the case-specific facts, not merely a reliable methodology in the abstract.
Daubert Factors Applied to AI Engines
Michigan absorbed the Daubert framework from Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993) into MRE 702 long before the 2024 amendment, with the Michigan Supreme Court’s decision in Gilbert v. DaimlerChrysler Corp., 470 Mich. 749 (2004), articulating the state’s reliability gatekeeping role. The factors — testability, peer review, error rate, general acceptance, and standards controlling the technique — translate to AI reconstruction as follows.
| Daubert Factor | Traditional Reconstruction | AI-Assisted Reconstruction |
|---|---|---|
| Testability | Equations from physics texts; reproducible | Engine outputs must be reproducible from the same inputs; demand seed reporting |
| Peer review | SAE papers, ACTAR-certified methods | Published validation studies of the specific AI model |
| Error rate | Conservation-of-momentum within stated tolerance | Documented validation against ground-truth datasets (e.g., NHTSA crash-test data) |
| General acceptance | SAE, ACTAR, IPTM communities | Adoption by NHTSA, NTSB, or major insurer SIU panels |
| Standards | SAE J-series standards | NIST AI Risk Management Framework documentation; vendor SOC reports |
Authentication Under MRE 901
Before reliability comes authentication. MRE 901(a) requires the proponent to produce evidence sufficient to support a finding that the item is what its proponent claims. MRE 901(b)(9) addresses processes and systems: the proponent must describe the process used and show that the process produces an accurate result. For AI reconstruction, that translates to a chain-of-custody record covering the raw inputs (dashcam files, EDR downloads pursuant to the NHTSA 49 CFR Part 563 rules, LiDAR or photogrammetry scans), the model version and weights used, and the operator-entered parameters. Skipping any link invites a 901 challenge.
The “Black-Box” Objection and How to Defeat It
Defense counsel will object that the AI engine is a black box whose internal weights are not disclosed and therefore cannot satisfy MRE 702’s reliable-application requirement. The most effective plaintiff response combines three elements: (1) a validation report from the vendor or an independent lab against a known dataset; (2) the testifying expert’s own qualification to operate the engine and to verify outputs against first-principles physics; and (3) reproducibility — running the same inputs through the engine and obtaining the same outputs. Where any of those three are missing, the court is more likely to exclude under the new “more likely than not” standard.
AI-Enhanced Video Is Not the Same as AI Reconstruction
Trial courts in other jurisdictions have begun drawing a sharp line between AI-generated reconstructions (which are demonstrative scientific evidence) and AI-enhanced video (which is real-time imagery that an algorithm has upscaled, denoised, or interpolated). The latter category has run into authentication problems where the enhancement alters pixel data the jury treats as faithful. Michigan judges are likely to follow that distinction. Counsel offering AI-enhanced dashcam footage should disclose the enhancement, name the model, and ideally tender both the raw and enhanced versions.
Practical Workflow for Michigan Plaintiff’s Counsel
- Preserve the inputs. Send a litigation-hold letter for the EDR module, dashcam SD cards, and any cloud-stored telematics within days of intake. Spoliation under Brenner v. Kolk, 226 Mich. App. 149 (1997), still bites.
- Run two reconstructions. One traditional (physics-based, hand-calculated), one AI-assisted. Convergent results sell.
- Document model version. Include in the expert report the exact model build, training-data cutoff, and any post-training fine-tuning.
- Disclose seed and randomness controls. A reproducible run requires fixed seeds where the model has stochastic components.
- Tender a Daubert binder. Validation studies, peer-reviewed papers, and a published error-rate sheet head off the predictable challenge.
Tying the Reconstruction to Michigan Substantive Law
Reconstruction matters because it usually drives one or more elements: causation, comparative-fault apportionment under MCL 600.6304, the “serious impairment” question under MCL 500.3135 as construed in McCormick v. Carrier, 487 Mich. 180 (2010), and the rear-end presumption under MCL 257.402(a) discussed in our rear-end-presumption guide. Counsel should brief the trial judge on the substantive payoff before the Daubert hearing — judges are more willing to admit complex science when its relevance to a jury issue is concrete.
Self-Represented Plaintiffs Should Be Especially Cautious
Consumer-facing AI tools that promise an “instant accident-reconstruction report” are not a substitute for a qualified expert. A pro se claimant who relies on a chatbot-generated reconstruction may waive the ability to introduce a qualified human expert later, and the chatbot’s output is unlikely to survive an MRE 702 challenge. The State Bar of Michigan’s 2025 Age of AI report cautions practitioners about supervision under MRPC 5.3; the same caution applies in reverse to clients who try to substitute AI output for expert testimony.
Frequently Asked Questions
Can a Michigan court admit AI accident reconstruction in 2026?
Yes, provided the proponent satisfies MRE 702 as amended in 2024 — including the more-likely-than-not burden — and authenticates the underlying process under MRE 901(b)(9). Courts treat AI reconstructions as a subset of scientific expert opinion.
Is AI-enhanced dashcam footage admissible?
It depends on disclosure and the type of enhancement. Denoising and upscaling that change pixel content require authentication of the enhancement process. Best practice is to tender both raw and enhanced files.
Does the 2024 MRE 702 amendment change anything for plaintiffs?
Yes. The “more likely than not” language and the emphasis on reliable application raise the burden plaintiffs must meet at a Daubert hearing. Expect more rigorous pretrial challenges to expert proofs.
Can I use ChatGPT to reconstruct my own accident?
No. A general-purpose chatbot is not an accident-reconstruction tool, and its output cannot be authenticated to MRE 702 standards. Hire a qualified ACTAR-certified or SAE-credentialed reconstructionist.
What is ACTAR certification?
The Accreditation Commission for Traffic Accident Reconstruction credentials engineers and analysts who pass a written and practical exam. ACTAR certification is the single most common way to show qualification on the expert-witness stand in Michigan.
Does AI reconstruction beat the MCL 500.3135 threshold faster?
No — the serious-impairment question is decided by the McCormick framework, not by the reconstruction itself. A well-documented reconstruction can support a finding on causation and on the importance of the impaired body function, but it does not substitute for medical proofs.
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