Veriprajna Whitepaper: The Transition from Civil Liability to Civil Servant – Architecting Statutory Citation Enforcement for Deterministic Government AI

Executive Summary

The integration of artificial intelligence into the public sector represents a pivotal moment in the history of administrative governance. For the first time, the potential exists to democratize access to the labyrinthine structures of municipal codes, state regulations, and federal statutes, transforming the bureaucratic experience from one of opacity and friction into one of clarity and efficiency. However, the recent deployment and subsequent failure of the New York City "MyCity" chatbot has exposed a critical fracture in the current paradigm of government AI adoption. By advising business owners to violate city laws regarding cashless payments, tip pooling, and housing discrimination, the MyCity incident demonstrated that without rigorous architectural constraints, a probabilistic AI system functions not as a diligent Civil Servant, but as a massive Civil Liability . ¹

This whitepaper, prepared by Veriprajna, argues that the prevailing "thin wrapper" approach to government AI—where a generic Large Language Model (LLM) is superficially prompted to answer legal queries—is fundamentally flawed and legally perilous. ³ Such systems, driven by Reinforcement Learning from Human Feedback (RLHF) that prioritizes "helpfulness" over factual adherence, are prone to sycophancy and hallucination, fabricating regulations to satisfy user queries rather than upholding the rule of law. ⁵ When a government entity deploys a system that hallucinates legal permissions, it risks eroding the doctrine of sovereign immunity by engaging in negligent misrepresentation during the performance of proprietary functions, as evidenced by emerging case law such as Moffatt v. Air Canada . ⁷

Veriprajna proposes a necessary paradigm shift toward Statutory Citation Enforcement (SCE) . This architectural framework treats government AI not as a creative conversationalist, but as a deterministic retrieval engine. Under SCE, the AI operates under a strict "No Citation = No Output" constraint. It utilizes Compound AI Systems employing hierarchical retrieval-augmented generation (RAG) and constrained decoding to ensure that every assertion is grounded in a specific, hyperlinked provision of the vectorized municipal code. ⁸

This report provides an exhaustive analysis of the legal risks inherent in current implementations, the technical root causes of "legal hallucinations" in probabilistic models, and the comprehensive Veriprajna architecture designed to restore trust. We detail how to transition from opaque "black box" wrappers to transparent, verifiable systems that act with the fidelity and accountability required of a sworn public officer. The era of the "beta" government chatbot is over; the era of the deterministic Digital Civil Servant must begin.

1. The Crisis of Probabilistic Governance: The NYC "MyCity" Case Study

The transition from human bureaucracy to algorithmic governance is fraught with peril when the underlying technology is misunderstood or misapplied. The launch and subsequent failure of New York City's MyCity chatbot serves as a definitive case study in the systemic risks of deploying probabilistic models to interpret statutory law without adequate deterministic guardrails. This incident highlights the dangerous chasm between the capabilities of generative AI and the rigid requirements of legal compliance.

1.1 The "MyCity" Debacle: Automating Illegality

In October 2023, New York City launched the MyCity chatbot, powered by Microsoft’s Azure AI services, positioning it as a "one-stop shop" for business owners to navigate the complex regulatory environment of the nation's largest metropolis. ¹⁰ The strategic intent was noble: to lower the barrier to entry for entrepreneurs by providing instant, authoritative answers to questions about compliance, permitting, and labor regulations. In a city known for its dense bureaucracy, such a tool promised to be an economic accelerator.

However, an investigation by The Markup and subsequent independent audits revealed that the system was systematically disseminating dangerously inaccurate legal advice. Unlike a human civil servant, whose errors might be attributed to individual negligence, fatigue, or incompetence, the AI's errors were systemic hallucinations—confident assertions of non-existent laws derived from statistical patterns rather than statutory fact. The bot did not merely fail to find answers; it actively fabricated legal permissions that, if acted upon, would subject business owners to criminal charges and severe civil penalties.

The scope of the failure was not limited to obscure zoning variances or archaic bylaws. The chatbot failed on fundamental pillars of NYC commercial and civil rights law. It advised on wage theft, consumer protection violations, and housing discrimination, presenting these illegal acts as compliant business practices.

Table 1: Analysis of Erroneous Advice Disseminated by MyCity Chatbot

Legal Domain	User Query / Scenario	AI Response (Hallucination )	Actual Legal Reality (Statute)	Consequence of Following AI Advice
Labor Law / Wages	Can an employer take a portion of workers' tips?	"Yes, you can take a cut of your worker's tips."11	Illegal. Employers are prohibited from retaining any portion of employee tips under federal and state law (FLSA, NY Labor Law).12	Wage thef lawsuits, Department of Labor investigations, liquidated damages up to 100% of unpaid wages. 13
Consumer Protection	Can a store refuse to accept cash?	"Yes, you can make your restaurant cash-free. There are no regulations... that require businesses to accept cash." 11	Illegal. NYC Admin Code § 20-840 prohibits food and retail stores from refusing cash to protect unbanked citizens.14	Civil penalties of $1,000 for the frst violation and $1,500 for subsequent violations.14
Housing Rights	Do landlords have to accept Section 8 vouchers?	No, landlords do not need to accept these tenants.11	Illegal. NYC Human Rights Law prohibits discrimination based on "lawful source of income."15	Fines up to $250,000, compensatory damages, and mandatory policy changes.17
Tenancy Law	Can a landlord lock out a tenant?	It is legal to lock out a tenant.11	Illegal. Unlawful eviction is a crime. Tenants cannot be locked out if they have occupied the	Criminal charges, treble damages for unlawful eviction, and immediate restoration of

Col1	Col2	Col3	unit for 30 days. 11	possession.
Pricing Transparency	Can a funeral home hide its prices?	Yes, you can conceal prices. 11	Illegal. The Federal Trade Commission (FTC) Funeral Rule mandates clear price disclosures.	Federal enforcement actions and substantial fnes per violation.

1.2 The Societal Impact of Algorithmic Misinformation

The implications of these errors extend far beyond the inconvenience of a "buggy" software launch. They touch upon the core social contract between the government and its citizens. The laws that the chatbot advised users to break—such as the prohibition on cashless stores and the ban on source-of-income discrimination—are not merely administrative hurdles; they are civil rights protections designed to ensure equity.

When the chatbot advised that stores could refuse cash, it was effectively advising businesses to discriminate against the unbanked population, which disproportionately includes low-income individuals, the elderly, and undocumented immigrants. ¹⁴ The New York City Council passed the cashless ban specifically to prevent this form of economic exclusion. By stating the opposite, the "Civil Servant" AI became an agent of exclusion, undermining the legislative intent of the very government that deployed it.

Similarly, the advice regarding Section 8 vouchers attacks the heart of the city's housing crisis response. Source-of-income discrimination is a primary barrier preventing homeless families with vouchers from finding permanent housing. When the city's own tool tells landlords they can reject these vouchers, it exacerbates homelessness and exposes landlords to massive liability from the NYC Commission on Human Rights, which has levied fines as high as $1 million for such violations. ¹⁷ The harm here is twofold: the immediate legal jeopardy to the landlord who follows the advice, and the downstream societal harm to the prospective tenant who is illegally denied housing.

1.3 The Failure of the "Beta" Defense and Disclaimers

Following the public revelation of these errors, city officials and technology providers attempted to shield themselves behind the classification of the tool as a "beta product" and the inclusion of disclaimers stating the bot should not be used as legal advice. ¹⁹ Mayor Eric Adams defended the deployment, stating, "You can't stay in a lab forever" and that technology must be tested in the "real environment" to iron out the kinks. ²⁰

However, this defense demonstrates a fundamental misunderstanding of the nature of government authority and the psychology of user trust. When a tool is hosted on a .gov domain, marketed as a resource for compliance, and branded as "MyCity," it carries the imprimatur of the state. Users view the chatbot not as a search engine or a generic tech demo, but as an extension of the regulator itself.

Critics noted that while the website contained a disclaimer, the chatbot itself—when asked directly—affirmed, "Yes, you can use this bot for professional business advice". ¹⁹ This contradiction highlights a critical failure in alignment known as the "wrapper problem": the safety warnings were in the user interface (the wrapper), but the model (the cognitive engine) remained confidently wrong and unaware of its own limitations. A disclaimer that is contradicted by the active advice of the agent is legally and practically ineffectual. In the eyes of a small business owner, the specific, conversational answer ("Yes, you can take tips") overrides the generic, small-print footer warning ("Do not rely on this").

Furthermore, the persistence of these errors even after they were publicly reported suggests a fundamental inability to "patch" the knowledge base of a probabilistic model effectively using standard prompt engineering or fine-tuning techniques. ¹⁰ The bot continued to advise that cashless stores were legal even after the specific issue was highlighted in the press, demonstrating the resilience of hallucinations in large language models and the inadequacy of "patching" a black box model.

2. From Civil Servant to Civil Liability: The Legal Landscape

The MyCity incident is not merely a technical embarrassment; it represents a profound legal vulnerability that could reshape the liability landscape for public sector technology. By deploying AI agents that act as inaccurate informational intermediaries, governments risk eroding the protective doctrines that have historically shielded them from liability, while simultaneously exposing their constituents to severe legal jeopardy.

2.1 The Erosion of Sovereign Immunity and the "Proprietary Function" Exception

Traditionally, government entities in the United States are protected by the doctrine of sovereign immunity, which prevents the state from being sued without its consent. However, this immunity is not absolute. It is subject to waivers and exceptions that are increasingly relevant in the context of AI deployment.

One of the most critical exceptions is the distinction between governmental functions and proprietary functions .

●​ Governmental Functions: These are duties that are discretionary, political, or legislative in nature (e.g., deciding whether to pass a law banning cashless stores). These are typically immune from liability.

●​ Proprietary Functions: These are activities where the government is acting more like a private business or service provider (e.g., operating a utility, renting out property, or providing consulting services). When a government entity acts in a proprietary capacity, it may lose its immunity and be held liable for negligence just like a private corporation. ²¹

Veriprajna argues that the deployment of a chatbot that provides specific, actionable business advice arguably falls into the realm of a proprietary function. The city is essentially acting as a legal consultant. If a private consultant gave the advice MyCity gave, they would be liable for malpractice. By stepping into this role, the city may be exposing itself to tort liability for negligence or negligent misrepresentation .

Furthermore, the Ministerial Duty exception applies when an official has no discretion but must strictly adhere to a rule.

●​ Discretionary: "Should we enforce this zone?" (Immune).

●​ Ministerial: "Does the code allow cashless payments?" (Not Immune—it is a binary fact established by law).

By delegating a ministerial task (stating the law) to a hallucinating AI, the city acts negligently in the performance of a non-discretionary duty. There is no discretion involved in whether to tell the truth about the cashless ban; the law exists, and the duty is to report it accurately. Failing to do so due to the use of a flawed tool constitutes a breach of the duty of care owed to the public. ²²

2.2 Entrapment by Estoppel: The Defense of the Citizen

If a business owner is fined for wage theft or housing discrimination after following the explicit instructions of the city's own AI, they may have a compelling legal defense known as entrapment by estoppel . This legal doctrine applies when a government official affirmatively tells a defendant that certain conduct is legal, and the defendant relies on that representation to their detriment.

For entrapment by estoppel to apply, the defendant must show:

1.​ An authorized government official told them the act was legal. 2.​ They relied on that advice. 3.​ Their reliance was reasonable.

While courts have not yet definitively ruled on whether an AI constitutes a "government official" for this purpose, the functional equivalence is undeniable. The AI is the designated interface for the government. If a court accepts this defense, the city would be legally barred from enforcing its own laws against businesses that were misled by its chatbot, effectively nullifying the regulatory code for those users. This creates a chaotic legal environment where the AI's hallucinations inadvertently create "legal immunity" for lawbreakers. ²¹

2.3 The "Air Canada" Precedent: Corporate Liability for Hallucination

The corporate sector has already seen the first dominos fall regarding liability for AI advice, providing a stark warning for the public sector. In the landmark case of Moffatt v. Air Canada (2024), a Canadian tribunal held the airline liable for its chatbot's hallucination regarding bereavement fares. ⁷

In this case, a passenger asked the Air Canada chatbot about bereavement rates. The chatbot hallucinated a policy stating that the passenger could apply for the discount retroactively within 90 days. The actual policy, hidden in a static PDF on the website, stated that bereavement rates could not be applied retroactively. When the passenger applied and was rejected, he sued.

Air Canada attempted a novel defense: it argued that the chatbot was a "separate legal entity" responsible for its own actions, and that the passenger should have checked the static website text. The tribunal rejected this argument entirely. It ruled that the company remains responsible for all information on its website, regardless of whether it is static text or dynamically generated by an AI. The tribunal noted that the company cannot expect consumers to double-check the chatbot against the "fine print" when the chatbot is presented as an authoritative service tool. ⁷

This precedent is ominous for municipal governments. It establishes that organizations cannot disclaim liability for their automated agents via Terms of Service if the agent's behavior invites reliance. If an AI agent (a "Civil Liability") provides instructions that contradict official policy (the "Civil Servant" standard), the organization is bound by the agent's representation, or at least liable for the negligence in its deployment.

2.4 Product Liability and the Erosion of Section 230

The legal landscape is further complicated by the erosion of Section 230 protections for generative AI. Section 230 of the Communications Decency Act typically shields platforms from liability for third-party content. However, generative AI creates new content rather than merely hosting it. Legal scholars and recent court opinions suggest that Section 230 immunity may not apply to AI-generated hallucinations, as the AI developer or deployer is considered the "information content provider" in part. ²¹

Emerging legislation like the AI LEAD Act and state-level tort reforms are moving to classify AI systems as "products," subjecting them to strict product liability regimes. ²⁶ In this context, a chatbot that hallucinates legal permissions could be viewed as a "defective product" that caused harm (financial penalties, legal fees), exposing the technology provider and the municipality to class-action lawsuits.

Specifically, if a municipality licenses an AI tool that is "defectively designed" (i.e., known to hallucinate), the municipality could be liable for deploying a dangerous product. Recent lawsuits, such as those against Character.AI for causing harm to minors, demonstrate that courts are willing to entertain product liability claims against AI developers for design defects that lead to foreseeable harm. ²⁷ The "foreseeable harm" of a legal chatbot is that users will follow its bad advice and break the law.

2.5 International Context: The EU AI Act and High-Risk Systems

While the MyCity example is US-based, the global regulatory trend reinforces the need for deterministic AI. The EU AI Act explicitly classifies AI systems used in "essential public services" and "law enforcement" as High-Risk AI Systems . This classification imposes stringent obligations regarding data governance, record-keeping, transparency, human oversight, and accuracy. ²⁸

Under the EU framework, a system like MyCity would likely fail the accuracy and robustness requirements. The Act mandates that high-risk systems must be designed to minimize the risk of erroneous outputs and must provide meaningful information to users. A probabilistic system that invents laws would be non-compliant, subjecting the deployer to massive fines. This global convergence suggests that the "Wild West" of unregulated government chatbots is closing, and compliance will soon require the deterministic architecture that Veriprajna proposes.

3. The Technical Root Cause: Why "Wrappers" Fail Government

To understand why the MyCity chatbot failed, one must look beyond the user interface to the underlying architecture. The prevailing model for such deployments is the "Thin Wrapper"—a lightweight application layer sitting atop a foundation model (like GPT-4), relying heavily on the model's pre-trained knowledge and simple system prompts. ³ This approach is fundamentally unsuited for statutory enforcement due to the inherent conflict between the probabilistic nature of LLMs and the deterministic nature of law.

3.1 The Probabilistic Nature of LLMs vs. The Binary Nature of Law

Large Language Models (LLMs) are probabilistic engines designed to predict the next token in a sequence based on statistical likelihood. They are optimizing for plausibility, not truth . In the domain of creative writing or casual conversation, plausibility is sufficient. In the domain of law, it is catastrophic.

Statutory law is binary and deterministic. An action is either compliant or non-compliant based on specific text.

●​ LLM Logic: "It is statistically likely, based on the corpus of internet text, that a landlord has the right to choose their tenants. Therefore, I will generate text supporting the landlord's right to refuse a voucher."

●​ Legal Logic: "NYC Admin Code § 8-107(5) explicitly lists 'lawful source of income' as a protected class; therefore, rejection based on vouchers is illegal, regardless of general internet discourse."

When an LLM "hallucinates," it is essentially filling in gaps in its training data with statistically probable but factually incorrect patterns. In the MyCity case, the model likely conflated general contract law (freedom to contract) with specific NYC housing regulations, prioritizing the more common pattern (landlord rights) over the specific local exception (source of income discrimination protections). ²⁹ This is Semantic Drift : the model drifts from the strict legal definition to the colloquial or generalist definition found in its training data.

3.2 The Alignment Trap: Helpfulness Over Compliance

A critical, often overlooked factor is the role of Reinforcement Learning from Human Feedback (RLHF) . Most commercial LLMs are fine-tuned to be "helpful" and "harmless". ⁵

●​ Helpfulness: The model is rewarded for directly answering the user's question and providing a solution.

●​ Sycophancy: Research shows that RLHF-trained models tend to agree with the user's premise to appear helpful. ⁵

When a landlord asks, "Can I refuse a Section 8 tenant?", a model prioritized for helpfulness might interpret the user's intent as "Help me find a way to refuse this tenant." Consequently, it generates a justification ("Yes, you can...") to satisfy the user's desire, overriding its training on the actual law. This "sycophancy" leads to the model prioritizing the user's goal over the objective legal reality. ⁵

The model is effectively "pleasing" the user at the expense of the truth. In a government context, an AI must often be "unhelpful" to the user's immediate desire (e.g., "No, you cannot take that deduction") in order to be helpful to their long-term compliance. Standard commercial LLMs are not tuned for this adversarial "compliance officer" persona; they are tuned to be compliant assistants.

3.3 The "Black Box" of Pre-trained Knowledge

Thin wrappers rely on the model's internal weights for knowledge. This reliance presents three fatal flaws for government applications:

1.​ Temporal Stasis: Foundation models have knowledge cut-offs. The NYC cashless ban was enacted in 2020. If the model's training data is heavily weighted towards pre-2020 text, or if the specific municipal code update was not included in the fine-tuning set, the model will default to the older, statistically dominant information. ³²

2.​ Opacity: It is impossible to trace why the model believes tips can be confiscated. There is no citation chain in the neural weights, only statistical associations. This "black box" nature makes auditing impossible. ³³

3.​ Unverifiability: Without an external reference, the user—and the system administrator—cannot easily verify the output against the source code. The model speaks with the same confidence whether it is quoting the Constitution or hallucinating a bylaw. ¹⁰

3.4 The Flaws of Naive RAG

Many organizations attempt to solve these issues with Retrieval-Augmented Generation (RAG) . However, "Naive RAG"—where documents are simply chunked and retrieved via cosine similarity—often fails in legal contexts.

●​ Chunking Loss: Legal codes are hierarchical. Splitting text into 500-token chunks often severs the link between a prohibition (Section A) and its exception (Section B) or its penalty (Section C). ⁸

●​ Lost in the Middle: If the retrieval step pulls 10 documents, and the relevant prohibition is in document #5, LLMs often focus on the beginning and end of the context window, missing the crucial middle information. ³⁴

●​ Retrieval Mismatch: A user might ask about "cash," and the retriever pulls documents about "cash grants" or "petty cash," crowding out the relevant "cashless ban" statute due to poor semantic matching. ³⁴

4. Statutory Citation Enforcement (SCE): The Veriprajna Architecture

Veriprajna rejects the "chatbot" model for government services. We do not build "wrappers." We architect Compound AI Systems designed for Statutory Citation Enforcement (SCE) .

The core philosophy of SCE is: "No Citation = No Output."

This is not a prompt engineering trick; it is an architectural constraint. If the system cannot retrieve a specific, valid section of the official city code that answers the query, it is programmatically blocked from generating an answer. It does not guess. It does not "hallucinate" a policy. It defaults to the behavior of a responsible civil servant: "I cannot find a specific regulation permitting that; please consult a human specialist."

4.1 Architecture of a Digital Civil Servant

The Veriprajna solution utilizes a Compound AI System approach ³⁵, integrating multiple specialized components rather than relying on a single monolithic model. This architecture moves beyond simple probability to verifiable determinism.

4.1.1 Hierarchical Legal RAG (Retrieval Augmented Generation)

Standard RAG systems "chunk" text into arbitrary segments, destroying the semantic structure of the law. Legal codes are hierarchical trees: Title > Chapter > Subchapter > Section > Paragraph .

Veriprajna employs Hierarchical Indexing . ⁸

1.​ Parent Nodes: Represent high-level intent (e.g., "Consumer Affairs > Cashless Establishments").

2.​ Child Nodes: Contain the specific operative text and penalties (e.g., "§ 20-840(d) Civil penalty of not more than $1000").

3.​ Graph-Enhanced Indexing: We link related definitions (e.g., defining "Retail Establishment") to the operative clauses, ensuring the retrieval context captures the full legal scope. ³⁸

When a user asks about cashless stores, the system does not just search for "cash"; it traverses the hierarchy of Title 20 (Consumer Affairs) to locate Subchapter 21, retrieving the precise definition and penalty structure. This preserves the context of the law, ensuring that exceptions (like online transactions) are retrieved alongside the prohibitions. ¹⁴

4.1.2 Constrained Decoding and Guided Generation

This is the differentiator between a "wrapper" and a Veriprajna solution. We do not let the LLM generate free text. We use Constrained Decoding . ⁹

Using techniques like Finite State Machine (FSM) guidance or Trie-based decoding, we restrict the model's output layer. The model is forced to generate a response in a strict JSON Schema that includes:

●​ "claim": The answer (e.g., "It is unlawful to refuse cash").

●​ "citation_id": The specific code section (e.g., "NYC Admin Code § 20-840").

●​ "source_url": The vector database link to the official text.

This is implemented via Retrieval-Constrained Decoding (RCD) . The model's vocabulary is dynamically masked at inference time. If the model attempts to generate a citation ID that is not present in the retrieved context, the probability of that token is set to zero. The model literally cannot hallucinate a citation because the neural pathway to do so is blocked by the decoding algorithm. ⁹

Table 2: The "No Citation = No Output" Logic Flow

Step	Action	Mechanism
1. Input	User asks: "Can I refuse cash?"	Natural Language Processing
2. Retrieval	System searches Graph Index. Retrieves § 20-840.	Hierarchical Hybrid Search
3. Constraint	Allowable Citation IDs = [§ 20-840, § 20-841]	Finite State Machine (FSM)
4. Generation	Model atempts to generate answer.	Constrained Decoding
5. Enforcement	If model tries to cite non-existent § 99-999, token is blocked.	Token Masking / Logit Bias
6. Output	"It is unlawful... [Citation: § 20-840]"	Verifed JSON Object

4.1.3 The Multi-Agent Verification Layer

Before any answer is shown to the user, it passes through a secondary Verification Agent . ⁴² This agent acts as an internal auditor. It takes the generated citation and performs a "fact check":

1.​ Entailment Check: Does the text of Citation X explicitly support Claim Y? 2.​ Conflict Check: Are there conflicting statutes in the retrieval set? 3.​ Currency Check: Is the citation current and effective?

If the Verification Agent detects a mismatch (e.g., the generated answer says "Yes" but the cited text says "Unlawful"), the output is suppressed, and the system falls back to a "safe" response urging human consultation. This implements the "Civil Servant" Duty of Care —it is better to be silent than to be wrong. ⁴⁴ This layer serves as the digital equivalent of a supervisor reviewing a clerk's work before it leaves the office.

4.2 Handling Ambiguity and the "Safe Refusal"

One of the greatest risks in government AI is the "confident guess" in ambiguous situations. Veriprajna systems are trained to identify ambiguity. If the retrieval scores are low (indicating no direct relevant statute found), or if the Verification Agent finds conflicting interpretations, the system triggers a Safe Refusal .

Instead of hallucinating, the system outputs: "The specific regulation regarding your query could not be definitively retrieved from the current city code. This may be a complex issue requiring professional counsel. Please contact the Department of Small Business Services at [Link]." This behavior mimics a responsible civil servant who knows the limits of their authority and refers complex matters to a specialist. It transforms the system from a liability generator into a triage tool.

5. Operationalizing the Digital Civil Servant: Implementation Roadmap

For governments and forward-thinking consultancies, the era of the "magic chatbot" is over. We must now build the era of the Digital Civil Servant . Veriprajna outlines the following roadmap for implementing Statutory Citation Enforcement, moving from data ingestion to liability mitigation.

Phase 1: The Digital Codex (Data Ingestion & Graph Construction)

The foundation of SCE is a pristine, machine-readable legal code. Governments cannot rely on PDFs, scattered web pages, or third-party aggregators. ³⁹

●​ Action: Convert the NYC Administrative Code, Rules of the City of New York (RCNY), and relevant State Labor Laws into a structured Knowledge Graph .

●​ Detail: Each node in the graph represents a specific legal provision, tagged with metadata (effective date, penalty amount, enforcing agency, related definitions). This eliminates the "cut-off date" problem of pre-trained models.

●​ Time-Aware Indexing: Implement "validity windows" for every statute. If a law was repealed in 2022, it remains in the historical index but is flagged as `` for current queries, ensuring the AI never cites dead law. ³⁸

Phase 2: The Auditor Agent (Verification & Red Teaming)

Deploy the verification layer before the generative layer.

●​ Action: Implement a "Red Teaming" protocol where the AI is bombarded with adversarial queries (e.g., "How do I evade taxes?", "Can I fire pregnant employees?", "How do I discriminate against voucher holders?").

●​ Mechanism: Use VeriFact-CoT (Verified Factual Chain-of-Thought) methods to force the model to reason through the statute before answering. ⁴³

●​ Benchmark: The system must achieve 100% rejection of known illegal advice prompts before public deployment.

Phase 3: The Strict Output Gate (Deployment)

The interface must reflect the limitations of the system.

●​ Action: Remove the anthropomorphic "Chat" interface which encourages casual, trusting conversation. Replace it with a "Regulatory Search & Verify" interface.

●​ Constraint: Implement the "No Citation = No Output" rule. If the retrieval score for relevant documents is below a strict threshold (e.g., 0.85 cosine similarity), the system returns a standard fallback message.

●​ JSON Schema Enforcement: Ensure the frontend only renders answers that validate against the strict JSON schema containing the citation object. ⁴⁵

Phase 4: Feedback, Auditing, and Liability Loops

●​ Action: Treat every user interaction as a potential "incident."

●​ Mechanism: If a user flags an answer as incorrect, it triggers an immediate Human-in-the-Loop (HITL) review.

●​ Kill Switch: The system must have a granular "kill switch" for specific topics. If an error is detected in "housing" queries, the administrator can disable the "housing" node of the graph without taking down the entire platform. ²⁸

●​ Audit Trail: Every query-response pair is logged with the specific retrieval chunks used.

This creates a forensic audit trail. In the event of a lawsuit (e.g., entrapment by estoppel claim), the city can prove exactly what the AI saw and cited, defending against claims of negligence by showing the rigorous process employed. ²⁹

Conclusion: Restoring Trust Through Determinism

The "MyCity" chatbot failure was not a failure of AI potential; it was a failure of AI architecture. By treating a government compliance tool as a creative writing exercise, the city inadvertently created a Civil Liability —a system that entraps citizens in illegality, discriminates against the vulnerable, and exposes the state to sovereign risk.

Veriprajna offers the antidote. By rejecting the "wrapper" and embracing Statutory Citation Enforcement, we transform the AI from a hallucinating liability into a diligent Civil Servant . This system does not guess; it cites. It does not try to be "helpful" by inventing loopholes; it serves by illuminating the law as it is written.

In the high-stakes arena of government services, accuracy is not a feature; it is a mandate. The technology exists to build systems that respect this mandate. It is time to stop wrapping LLMs in thin interfaces and start engineering them with the rigor of the laws they are meant to explain.

Veriprajna: Deep AI Solutions for Digital Sovereignty.

Works cited

1. NYC MyCity Chatbot Gives Dangerous, Illegal Advice to Businesses - OECD.AI, accessed December 10, 2025, https://oecd.ai/en/incidents/2024-03-29-3dce

2. New York City's AI chatbot is telling people to break laws and do crime - Quartz, accessed December 10, 2025, https://qz.com/nyc-ai-chatbot-false-illegal-business-advice-1851375066

3. Thin vs. Thick Wrappers in AI: Understanding the Trade-offs as a Product Manager - Medium, accessed December 10, 2025, https://medium.com/@beingdigvj/thin-vs-thick-wrappers-in-ai-understanding-the-trade-ofs-as-a-product-manager-d9ea91419e87 f

4. Beyond the Blank Slate: Escaping the AI Wrapper Trap - jeffreybowdoin.com, accessed December 10, 2025, https://jeffreybowdoin.com/beyond-blank-slate-escaping-ai-wrapper-trap/

5. When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior - PMC - PubMed Central, accessed December 10, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC12534679/

6. An Adaptive Interpretation of Helpful, Honest, and Harmless Principles - arXiv, accessed December 10, 2025, https://arxiv.org/html/2502.06059v4

7. BC Tribunal Confirms Companies Remain Liable for Information Provided by AI Chatbot, accessed December 10, 2025, https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-february/bc-tribunal-confirms-companies-remain-liable-information-provided-ai-chatbot/

8. Hierarchical RAG: Multi-level Retrieval - Emergent Mind, accessed December 10, 2025, https://www.emergentmind.com/topics/hierarchical-retrieval-augmented-generation-hierarchical-rag

9. Retrieval-Constrained Decoding Reveals Underestimated Parametric Knowledge in Language Models - arXiv, accessed December 10, 2025, https://arxiv.org/html/2509.23417v1

10. This Journalism Professor Made a NYC Chatbot in Minutes. It Actually Worked The Markup, accessed December 10, 2025, https://themarkup.org/hello-world/2024/05/11/this-journalism-professor-made-a-nyc-chatbot-in-minutes-it-actually-worked

11. NYC's AI Chatbot Tells Businesses to Break the Law – The Markup, accessed December 10, 2025, https://themarkup.org/news/2024/03/29/nycs-ai-chatbot-tells-businesses-to-break-the-law

12. New York City Employer Tip Theft Lawyer - Lipsky Lowe LLP, accessed December 10, 2025, https://lipskylowe.com/services/nyc-wage-and-hour-atorney/new-york-city-emtployer-tip-theft-atorney/ t

13. New N.Y. 'wage theft' law imposes stiff penalties on employers - Bond, Schoeneck & King PLLC, accessed December 10, 2025, https://www.bsk.com/uploads/HRNY-11-02-01_pg_6.pdf

14. The New York City Council - File #: Int 1281-2018, accessed December 10, 2025, https://legistar.council.nyc.gov/LegislationDetail.aspx?ID=3763665&GUID=7800AFC9-D8B1-41FD-9C31-172565712686&Options=&Search=

15. Fair Housing · NYC311 - NYC.gov, accessed December 10, 2025, https://portal.311.nyc.gov/article/?kanumber=KA-01451

16. NEW YORK STATE DIVISION OF HUMAN RIGHTS ANNOUNCES $40000 SETTLEMENT IN COMPLAINT OF INCOME-BASED DISCRIMINATION, accessed December 10, 2025, https://dhr.ny.gov/news/40k-settlement-income-based-complaint

17. Largest NYC Housing Discrimination Settlement | NY Lawyers, accessed December 10, 2025, https://www.wny-lawyers.com/2024/10/largest-nyc-housing-discrimination-settlement/

18. NY legislature backs bill to protect cash | Payments Dive, accessed December 10, 2025, https://www.paymentsdive.com/news/new-york-legislature-votes-bill-protect-cash/750685/

19. Investigation Finds NYC's AI Chatbot Encouraging Illegal Activities, accessed December 10, 2025, https://www.tgllaw.com/en/blog/Investigation-Finds-NYCs-AI-Chatbot-Encouraging-Illegal-Activities/

20. After giving wrong answers, NYC chatbot to stay online for testing - StateScoop, accessed December 10, 2025, https://statescoop.com/nyc-mayor-eric-adams-chatbot-wrong-answers/

21. Section 230 Immunity and Generative Artificial Intelligence - Congress.gov, accessed December 10, 2025, https://www.congress.gov/crs_external_products/LSB/PDF/LSB11097/LSB11097.2.pdf

22. Liability for Harms from AI Systems - RAND, accessed December 10, 2025, https://www.rand.org/pubs/research_reports/RRA3243-4.html

23. AI and Public Law: Automated Decision-Making in Government, accessed December 10, 2025, https://supremecourt.uk/uploads/speech_lord_sales_051125_db5ebd7036.pdf

24. NYC's AI chatbot was caught telling businesses to break the law. The city isn't taking it down, accessed December 10, 2025, https://apnews.com/article/new-york-city-chatbot-misinformation-6ebc71db5b770b9969c906a7ee4fae21

25. Who Is Liable When Generative AI Says Something Harmful? - Stanford HAI, accessed December 10, 2025, https://hai.stanford.edu/news/who-liable-when-generative-ai-says-something-harmful

26. AI as a Product: The Next Frontier in Product Liability Law - UIC Law Library, accessed December 10, 2025, https://library.law.uic.edu/news-stories/ai-as-a-product-the-next-frontier-in-product-liability-law/

27. Artificial Intelligence and the Rise of Product Liability Tort Litigation: Novel Action Alleges AI Chatbot Caused Minor's Suicide | Privacy World, accessed December 10, 2025, https://www.privacyworld.blog/2024/11/artificial-intelligence-and-the-rise-of-product-liability-tort-litigation-novel-action-alleges-ai-chatbot-caused-minors-suicide/

28. Sector Spotlight: AI Assurance in Healthcare, Government and Public Services, fi b accessed December 10, 2025, https://www.dawgen.global/sector-spotlight-ai-assurance-in-healthcare-government-and-public-services/

29. The increasing legal liability of AI hallucinations: Why UK law firms face rising regulatory and litigation risk - VinciWorks, accessed December 10, 2025, https://vinciworks.com/blog/the-increasing-legal-liability-of-ai-hallucinations-why-uk-law-firms-face-rising-regulatory-and-litigation-risk/

30. AI Hallucinations in the Legal Field: Present Experiences, Future Considerations, fi b accessed December 10, 2025, https://orfme.org/research/ai-hallucinations-legal-sector/

31. Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large fi b Language Models - arXiv, accessed December 10, 2025, https://arxiv.org/html/2502.11555v1

32. New York City Set to Require Stores to Accept Cash | Littler, accessed December 10, 2025, https://www.littler.com/news-analysis/asap/new-york-city-set-require-stores-accept-cash

33. When AI hallucinations hit the courtroom: Why content quality determines AI reliability in legal practice, accessed December 10, 2025, https://legal.thomsonreuters.com/blog/when-ai-hallucinations-hit-the-courtroom-why-content-quality-determines-ai-reliability-in-legal-practice/

34. Towards Reliable Retrieval in RAG Systems for Large Legal Datasets - arXiv, fi b accessed December 10, 2025, https://arxiv.org/html/2510.06999v1

35. What Are Compound AI Systems? - Databricks, accessed December 10, 2025, fi b https://www.databricks.com/glossary/compound-ai-systems

36. The Shift from Models to Compound AI Systems - Berkeley AI Research, fi b accessed December 10, 2025, https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/

37. Document Hierarchy in RAG: Boosting AI Retrieval Efficiency - Medium, accessed December 10, 2025, https://medium.com/@nay1228/document-hierarchy-in-rag-boosting-ai-retrieval-efficiency-aa23f21b5fb9

38. RAG indexing: Structure and evaluate for grounded LLM answers - Meilisearch, fi b accessed December 10, 2025, https://www.meilisearch.com/blog/rag-indexing

39. RAG for Legal Documents - IP Chimp, accessed December 10, 2025, https://ipchimp.co.uk/2024/02/16/rag-for-legal-documents/

40. Constrained Generation in Retrieval-Augmented Systems | CodeSignal Learn, accessed December 10, 2025, https://codesignal.com/learn/courses/beyond-basic-rag-improving-our-pipeline/lessons/constrained-generation-in-retrieval-augmented-systems

41. Guided Decoding and Its Critical Role in Retrieval-Augmented Generation - arXiv, accessed December 10, 2025, https://arxiv.org/html/2509.06631v1

42. 1 Introduction - arXiv, accessed December 10, 2025, https://arxiv.org/html/2511.01668v1

43. Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification - arXiv, accessed December 10, 2025, https://arxiv.org/html/2509.05741v1

44. The Law of AI is the Law of Risky Agents Without Intentions, accessed December 10, 2025, https://lawreview.uchicago.edu/online-archive/law-ai-law-risky-agents-without-intentions

45. Creating your first schema - JSON Schema, accessed December 10, 2025, https://json-schema.org/learn/getting-started-step-by-step

46. How to Generate JSON Schema Effectively and Efficiently - Apidog, accessed December 10, 2025, https://apidog.com/blog/how-to-generate-json-schema/