Why AI Hallucinates Indiana's Safe Haven Newborn Abandonment Law

Indiana HB1099 is a useful test case because it is exactly the kind of statutory change an AI legal research tool can mishandle while sounding polished. A lawyer searching “indiana safe haven law newborn abandonment” does not need a comforting overview of safe haven policy. The lawyer needs the current Indiana rule, the amendment history, the effective statutory language, and enough confidence in the source trail to put the answer in front of a client, intake worker, supervisor, or court-facing file. HB1099 belongs in that verification path because it amended Indiana’s safe haven framework; it cannot be treated as a side note beneath a model-generated summary of older law. [1]

This article is a tool-reliability and legal-risk evaluation, not legal advice. The point is narrower than “AI sometimes gets law wrong.” The point is that safe haven newborn abandonment statutes are a poor place to accept an AI answer that has not been checked against the current primary source. As of July 28, 2026, the controlling Indiana question must be answered from the Indiana Code and amendment record, not from a fluent paragraph that resembles a statutory explanation.

The Indiana problem is not the policy summary; it is the statutory detail

A safe haven answer can be broadly correct and still be unusable. “Indiana allows a parent to surrender a newborn without prosecution if statutory conditions are met” may orient the reader, but it does not answer the legal question. The working answer turns on the current age language, the permitted surrender method, the authorized receiving party or device, anonymity and immunity provisions, required downstream steps, and whether a recent amendment changed any of those items.

Indiana’s current safe haven provisions are codified in Title 31, Article 34, Chapter 2.5 of the Indiana Code. The statutory text addresses the voluntary surrender of a child who meets the age condition and also includes provisions involving newborn safety devices; those are not interchangeable details a researcher can safely smooth over. [2]

That is where an AI answer becomes risky. A model may correctly identify Indiana as a safe haven state, correctly use the phrase “newborn,” and correctly mention immunity in general terms, while still misstating the current surrender route or omitting a device-related provision. In an intake setting, that is not a harmless drafting flaw. It can change what an intake worker says to a frightened parent, what a supervising attorney signs off on, or what a firm later has to explain to a carrier.

A hallucinated Indiana answer can look like legal research

The most dangerous output is not the obviously absurd one. It is the answer formatted like a lawyer’s quick-reference note: bullet points, a code citation, a confident age limit, a list of locations, and a short conclusion that the surrendering parent will not face liability. If one of those statutory points is wrong, the format may make the error more persuasive.

A hypothetical example shows the problem without pretending to quote a real tool result: an AI system tells an Indiana practitioner that surrender is available only through a hospital and gives no warning that the statutory scheme includes newborn safety device provisions. The answer might still sound knowledgeable. It might still mention Indiana. It might still cite a code chapter. But if the current statute supplies another route, the omission is not cosmetic; it narrows the legal option presented to the person who needs the answer.

The reverse error is just as serious. A tool can overstate a protection, describe a surrender option too broadly, or fail to capture conditions attached to immunity. For family-law lawyers, the malpractice concern is not limited to whether a final memo is wrong. It includes the earlier moments when staff triage a call, a partner asks for a quick state comparison, or a lawyer in one jurisdiction tries to understand whether a matter must be referred immediately to Indiana counsel.

Why safe haven statutes are hostile to unverified AI research

Safe haven laws invite AI error because they combine three features that large language models handle poorly when not anchored to current primary law: state-by-state variation, small statutory thresholds, and periodic amendment. The general policy is easy to summarize. The legally operative detail is not.

Across states, safe haven statutes vary on matters such as who may surrender an infant, the infant’s maximum age, where surrender may occur, whether anonymity is expressly protected, what immunity applies, and what child-welfare or medical steps follow. National summaries can help identify the research universe, but they also show why a jurisdiction-specific answer cannot be inferred from the existence of a safe haven law elsewhere. [3]

That structure makes “mostly accurate” a weak defense. A model can be right about the national concept and wrong about Indiana. It can be right about Indiana before an amendment and wrong after codification. It can retrieve a state agency page that has not caught up with a legislative change, or it can merge neighboring states’ rules into a plausible but false hybrid. None of those failure modes requires the tool to be useless. They require only that the user mistake a generated answer for authority.

This is why the HB1099 example matters more than a generic hallucination story. A recent or targeted amendment tests whether the tool is actually doing current statutory research or merely producing a confident legal-sounding digest. If the output does not identify the controlling code section, the amendment path, and the date basis for the answer, the user still has the real research task ahead.

The proprietary-tool issue: lower hallucination rates do not make a statute answer reliable

The risk is not confined to public chatbots. Independent testing of leading AI legal research systems has reported hallucination rates above 17%, even for tools built and marketed for legal research rather than general conversation. [4]

That benchmark should be read carefully. It does not prove that any particular Indiana safe haven answer from any particular tool is wrong. It also does not prove that proprietary legal AI is equivalent to an unspecialized chatbot. What it does show is that legal branding, retrieval features, and professional formatting do not eliminate hallucination risk. For a statute-driven family-law question, the relevant unit of reliability is not the vendor’s average performance. It is whether this answer, on this date, in this jurisdiction, matches the current primary source.

Vendor assurances are least useful when they float above methodology. A risk manager evaluating these tools should ask what was tested, when it was tested, which jurisdictions were included, whether family-law statutes were part of the test set, how “hallucination” was defined, and whether the system’s answer can be traced to current primary law. A low error rate in one benchmark does not answer whether the tool accurately captured Indiana’s HB1099 amendment and the current safe haven code.

What competent verification looks like before relying on an Indiana safe haven answer

A lawyer can use AI to generate a first-pass issue list. The verification work, however, has to be human and source-based. For Indiana, that means checking the current Indiana Code chapter, reading the relevant HB1099 materials as amendment history, and confirming whether any later amendments or effective-date provisions alter the answer. [1][2]

• Open the current Indiana Code and identify the safe haven provisions governing voluntary surrender and newborn safety devices.
• Confirm the statutory age condition from the code text, not from a secondary summary or AI-generated paraphrase.
• Check the permitted surrender method and the statutory category of the receiving person, facility, or device.
• Read the provisions governing immunity, anonymity, reporting, custody transfer, and provider duties rather than assuming they follow the common safe haven pattern.
• Review HB1099 and any later legislative activity to determine whether the tool’s answer reflects current law or stale law.
• Document the date of verification, the primary sources checked, and the person who approved the answer for use.

That last step is not paperwork for its own sake. If a bad answer later becomes an issue, the firm will need to show more than “the tool said so.” It will need a record that someone checked the statute, understood the amendment, and limited the advice to what the verified law supported.

Where malpractice exposure enters

In this setting, malpractice exposure does not require a courtroom citation to a fake case. It can arise earlier and more quietly. An intake employee gives a parent the wrong location. A lawyer tells a referring professional that Indiana’s rule is narrower or broader than it is. A supervising attorney approves a multi-state chart that was never checked against current code. A risk team adopts an AI workflow that treats generated statutory answers as presumptively usable unless someone notices a problem.

The professional-responsibility overlay is familiar but important. The ABA has warned that lawyers using generative AI must understand the relevant risks and remain responsible for duties such as competence, confidentiality, communication, and supervision. [5]

For safe haven newborn abandonment law, supervision has to be more than telling staff to “double-check AI.” The workflow should specify what counts as a check: current statute, amendment history, effective date, jurisdiction-specific conditions, and file documentation. If a tool cannot provide source-linked support for those items, it may still be useful for framing the research question, but it has not produced a reliable legal answer.

AI can help frame the question, not supply the authority

AI can shorten the path to the right questions. It can remind a lawyer to check age limits, surrender locations, immunity, anonymity, device provisions, and amendment history. It can also produce a confident Indiana safe haven answer that is wrong on the one statutory point the lawyer needed.

For Indiana safe haven law newborn abandonment research, no AI output should be treated as statutory authority until a human has checked the current Indiana Code, reviewed the HB1099 amendment path, confirmed any later changes, and documented that verification. That is not hostility to AI. It is the minimum discipline required when a newborn, a surrendering parent, and a lawyer’s license may all depend on a small statutory detail.

References

1. House Bill 1099, Indiana General Assembly
2. Indiana Code Title 31, Article 34, Chapter 2.5, Indiana General Assembly
3. Infant Safe Haven Laws, Child Welfare Information Gateway
4. AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries, Stanford HAI, May 23, 2024
5. Formal Opinion 512: Generative Artificial Intelligence Tools, American Bar Association, July 29, 2024