AI ethics in legal work: How Anywhere ensures accuracy and human oversight

Why AI ethics in legal practice is key

In the legal world, AI has gained prominence primarily due to its ability to process text on a large scale. Machine learning technologies, natural language processing, and large language models (LLMs) can analyze contracts, court decisions, emails, or case documentation at speeds unimaginable until recently. Specialized legal tools built on databases like Westlaw, LexisNexis, or Practical Law can now find relevant case law, propose legal arguments, or identify risky clauses in thousands of contractual documents. In the context of international transactions, complex disputes, or regulated industries, AI thus brings significant time savings, better insight into information, and the ability to dedicate human capacity to strategic issues rather than routine tasks.

At the same time, however, it is becoming clear that generative AI is inherently a "probabilistic text generator": it produces outputs that sound convincing but lack an internal concept of truthfulness, legal relevance, or jurisdictional correctness.

In a legal context, this leads to the phenomenon of hallucinations – made-up citations, distorted statements, incorrect linking of different legal regulations, or ignoring key exceptions and conditions. For courts and regulatory bodies, however, it is crucial not that technology caused the error, but that the lawyer failed to correct it; responsibility for the outcome remains with the person who signs the submission and advises the client.

Model ethical frameworks, such as the rules of the American Bar Association, codes of individual bar associations, or new guidelines for AI use, emphasize three basic duties: technological competence, confidentiality, and the duty to supervise non-legal assistants, which generative AI factually falls under.

This means that a lawyer must understand what types of tasks are suitable for AI, what tools are acceptable in terms of resources and security, and what output verification should look like before it is used in practice. In an international environment, different regulatory requirements are added, such as the EU AI Act framework or American and state initiatives focused on algorithmic discrimination, transparency, and auditability.

In practice, therefore, the difference between two types of AI use is growing. On one hand, there is "consumer" use of open models, where an individual or company without legal guidance enters sensitive information into freely available systems whose terms allow data to be stored and used for further training.

On the other hand, the model of professional, controlled deployment of AI is gaining ground – in an environment with an emphasis on security, audit trail, data usage restrictions, and clear rules for human oversight, such as offered by specialized legal platforms or closed enterprise instances of models.

Anywhere.legal belongs precisely to this second group: it is not a public chatbot, but a structured environment for working with cases, in which AI is a tool for more efficient work for lawyers, not a replacement for their judgment.

How legal AI works and where errors most often occur

To understand the ethical issues of using AI in law, it is first necessary to realize how these systems work. Large language models learn from huge text corpora and then generate further text by predicting which tokens are likely to follow a preceding sequence.

They therefore do not work with "truth" in the human or legal sense, but with the probability of linguistic patterns. In everyday writing, this may be sufficient, but in law, even subtle differences in terminology, citation, or logical argumentation can have significant legal consequences.

The model does not have an internal mechanism to distinguish whether the proposed argument meets the conditions of a given legal norm or whether the cited decision actually exists in the chosen jurisdiction.

Hallucinations of legal AI manifest in various ways: from completely fabricated case names and decisions, to attributing existing case law to incorrect courts or years, to distorting factual circumstances or legal conclusions in otherwise real decisions.

A user who is not accustomed to verifying every citation can easily succumb to the impression that "if it sounds professional and contains citations, it is true." This has been shown in dozens of publicized cases where lawyers or pro se litigants submitted filings to courts containing a series of fabricated AI-generated decisions and subsequently faced sanctions, disciplinary proceedings, and damage to reputation.

Similar errors have even appeared among public prosecutors who used AI to prepare filings in a criminal case, and the text contained references to non-existent case law.

Besides hallucinations, equally problematic is so-called latent incompleteness and "misfit" – i.e., situations where the AI output is formally correct but does not precisely match the assignment or legal context.

This can involve missing elements of a legal test, overlooked exceptions, confusing federal regulation with state regulation, or jurisdictional confusion (e.g., applying American case law to a purely European problem).

Such errors are harder to detect because they are not obvious fabrications but partial deviations in an otherwise consistent text. A lawyer who relies too heavily on AI may overlook that the text "sounds good" but does not legally precisely match what they need for a specific court, administrative body, or transaction.

Another category of problems is bias and unfairness arising from the data on which the models were trained. Because AI reflects patterns contained in training data, it is sensitive to historical inequalities, discriminatory decision-making, or unbalanced representation of different groups.

In law, this can mean the risk that an AI tool recommending a strategy in litigation or analyzing risk in compliance will reproduce past discriminatory practices, even if they are now legally and ethically unacceptable.

A lawyer therefore cannot consider AI an "neutral arbiter"; instead, they must assume that without targeted measures and human oversight, the model will reflect and sometimes amplify existing prejudices and structural inequalities.

For these reasons, the "do not trust until verified" standard is rapidly gaining ground in the legal environment – the principle that no AI output can be considered usable until it has undergone independent verification by an experienced lawyer.

This applies to citations and legal conclusions, as well as factual claims, case summaries, or proposals for strategic steps. Professional tools and platforms therefore integrate verification directly into the workflow: they make it easy to trace the source of every claim, link AI output to specific documents or decisions, and set internal checklists that a lawyer must go through before using the output.

Anywhere.legal builds its approach precisely on this logic: AI provides quick insight and a first draft, but every step is designed to support systematic review and human oversight.

MicroFAQ: Understanding hallucinations and limitations of legal AI

1. Does AI hallucination mean the tool is "bad" and should not be used?
   Hallucinations are a natural consequence of the probabilistic nature of current language models, not a failure of a specific tool as such. Whether a tool is suitable for legal work depends on its sources, built-in control mechanisms, integration into the workflow, and human oversight. Many professional legal AI platforms combine models with curated databases of case law and legislation and supplement them with citation verification features, so the frequency of hallucinations is lower but never zero. The purpose of an ethical approach is therefore not to prohibit AI, but to use it as an assistant whose outputs are always verified, similar to the work of a junior associate or paralegal.

2. Can I eliminate hallucinations by further "training" AI on my own data?
   Supplementary training on internal data can improve relevance for a specific area or company, but it does not eliminate the basic text generation mechanism or the risk of hallucinations. Moreover, if internal data contains errors or biases, there is a risk that they will be transferred to the model and further amplified. In the legal environment, it is therefore more important to focus on selecting quality sources, clearly defining permitted uses, and control processes than on blind faith that further training will "solve everything." A platform like Anywhere.legal thus builds on a combination of quality sources, limitation of risky uses, and built-in steps of human control, not on the illusion that technology can function fully autonomously.

3. Are hallucinations a problem for specialized legal AI tools, or only for open chatbots?
   Empirical observations show that even specialized legal tools can hallucinate, only usually to a lesser extent than general models because they work with curated databases and have built-in control mechanisms. The difference is that professional tools can often better document the source of each claim and highlight uncertainty, while open chatbots generate answers without the context of legal responsibility or jurisdiction. For a lawyer, however, in both cases it means the same thing: no AI output can be adopted without verification, especially if it is to be used in a court filing, a formal legal opinion, or communication with a client.

Ethical duties of lawyers when using AI

The ethical use of AI in law does not begin with technology, but with the duties a lawyer owes to the client, the court, and the legal system. Traditional rules of professional ethics – especially the duty of competence, confidentiality, supervision of non-legal assistants, truthfulness to the court, and prohibition of unfair advertising – apply fully to AI, even though they were formulated when generative models did not exist.

Many bar associations and professional organizations today explicitly state that the use of AI falls under technological competence and that a lawyer must not rely solely on AI in providing legal advice. This means that a lawyer must understand the basic capabilities and limitations of the AI tools they use and must be able to explain how their outputs were verified.

The duty of confidentiality and protection of confidential information represents one of the most sensitive topics in the context of AI. Many freely available generative AI services reserve the right in their terms to use user data for further model training and for their internal analysis, which may mean that sensitive information is stored and potentially made accessible to third parties.

A recent decision by a federal court in New York in the Heppner case showed that if a client independently uses a publicly available AI platform to prepare legal documents and inserts confidential information into the system, this may break the conditions for asserting attorney-client privilege and work product protection.

The court in that case found that there was no reasonable expectation of confidentiality because the terms of service explicitly allowed for the storage and use of data, and the AI was not involved under the guidance of a lawyer or as a necessary intermediary within the meaning of the Kovel doctrine.

From this perspective, it is crucial for lawyers and clients to distinguish between open consumer tools and closed, contractually secured platforms that guarantee that inputs and outputs will not be used to train models or shared outside specified purposes.

A secure environment includes both technical measures (encryption, access control, client data segregation) and contractual obligations of the provider that data will be handled only as processed information, not as training material for its own models.

Platforms like Anywhere.legal take these requirements into account by operating in a "closed enterprise tool" mode, where preserving confidentiality and client data segregation is a fundamental prerequisite for cooperation, not an optional feature.

No less important is the lawyer's duty to supervise non-legal assistants, which, according to many expert opinions, also applies to the use of AI.

If a lawyer assigns the preparation of materials or analysis to an AI tool, they must treat the output in the same way as the work of a junior associate or paralegal: they must review it, correct errors, supplement relevant legal argumentation, and bear full responsibility for the final version.

This applies to internal use (e.g., in preparing legal research) as well as to any documents submitted to a court, opposing party, or client. Violation of this duty can lead to procedural sanctions (e.g., under rules like Rule 11 in the USA), disciplinary actions, and damage to client trust.

The issue of bias and fairness in law also touches on broader societal and regulatory aspects. It is well documented that if training data is unbalanced or contains historical prejudices, this will be reflected in the model's behavior.

This is particularly dangerous in areas such as employment decisions, creditworthiness assessments, risk assessment in criminal proceedings or compliance screening, where AI can affect individuals' access to rights and resources.

For this reason, legislative initiatives such as the American Algorithmic Accountability Act or European AI regulation are emerging, which require impact assessments, bias analysis, and the introduction of protective mechanisms against discriminatory results.

The role of lawyers is thus expanding: it is not enough to "just use" AI, but often it is necessary to assess its impact from the perspective of non-discrimination, transparency, and due process.

Typical risks of AI in law and how Anywhere.legal addresses them

How Anywhere.legal works with accuracy and human oversight

From a user's perspective, it is crucial that AI is not a "magic black box" but part of a transparent process that clearly divides what technology does and what a human does.

Anywhere.legal is conceived as a platform for resolving cross-border legal and tax situations, combining several layers: structuring assignments, centralizing documents, utilizing AI for initial information processing and generating proposals, and involving human experts in relevant jurisdictions.

AI here is not an autonomous decision-making system but a tool that speeds up the first phase of work and provides better structured materials for human judgment.

The basis of accuracy is the emphasis on the quality of sources with which AI works. Generally, the reliability of legal AI significantly depends on whether it is built on expert-curated, authoritative databases or merely "fishing" for information from the open internet.

Professional legal tools that draw from curated databases of case law, legislation, and commentaries typically have a lower rate of hallucinations and allow each claim to be linked to a source document.

Anywhere.legal therefore builds on the logic of "AI over structured legal and transactional materials": users upload their own documents and assignments to the system, and AI works primarily with this material, or with verified legal sources, not with unstructured web. This significantly reduces the risk of hallucinations and allows for an audit trail of how a particular output was created.

Another pillar is a closed, secure environment with clearly defined data processing conditions. Similar to other secure legal AI platforms, Anywhere.legal uses an approach that contractually prohibits the use of client data for model training and limits its use to providing service within a specific case.

Technical measures, such as encryption at rest and in transit and detailed access rights management, complement organizational measures in the form of internal policies and audit mechanisms.

For international users, it is important that such an approach facilitates compliance with personal data protection regimes, such as GDPR or other national regulations, which emphasize the principles of privacy by design and privacy by default.

Structuring assignments and centralizing documents

One of the most common practical problems in cross-border agendas is not so much the legal interpretation itself, but chaos in assignments, documents, and communication among multiple participants and jurisdictions.

In practice, it often turns out that different departments, local offices, or external advisors work with different versions of documents, lack a unified overview of which questions have already been answered, and some information is locked in emails or personal notes.

This leads to duplicate work, delays, misunderstandings in expectations, and in extreme cases, inconsistent legal positions in different countries.

Anywhere.legal solves this problem by offering a place to "open a case," structure assignments, and centralize all related documents.

The user defines the basic parameters of the situation – for example, affected countries, type of transaction, anticipated legal questions, and time limits – and the platform helps structure the brief to contain all key information and questions relevant to the legal analysis.

AI can, at this stage, suggest supplementary questions or point out typical aspects that are often overlooked (e.g., questions of tax implications, labor law, or regulatory licenses in other jurisdictions).

Centralization of documents means that all contracts, correspondence, internal policies, or regulatory documents are uploaded to one secure environment, where access is managed according to team roles.

AI tools can then generate summaries, identify key clauses, or compare different language versions of the same contract based on these documents.

For international projects, it is essential that the team always has a "single source of truth" – one reference version of the document and a unified assignment to which everyone refers. This significantly reduces the risk that, for example, a local advisor will rely on an older version of a contract while the headquarters is already working with a different text.

AI for quick understanding of materials, not for final conclusions

Another area where AI significantly helps is the rapid understanding of extensive materials. In a typical situation, a lawyer has hundreds of pages of contracts, emails, internal guidelines, or regulatory documents that need to be reviewed to extract key information.

AI can analyze these documents, create thematic summaries, highlight disputed or unusual clauses, and propose questions that the team should consider. In practice, this means that the legal team does not have to "slog through" every page but can focus on interpretation and strategy.

Crucially, AI is used here as a preprocessing tool, not as the author of the final legal opinion. In accordance with the recommendations of professional organizations and best practices in legal AI governance, the principle "AI can write, but must not decide" applies.

This means that AI can propose an argumentation structure, basic content of an opinion, or summary of case law, but a lawyer must review each such proposal, verify citations, add their own analysis, and adapt the text to match the specific jurisdiction, factual state, and client strategy.

A quality workflow therefore also includes checklists for verifying outputs: from checking the scope and assignment, through source verification, to final linguistic and strategic revision.

Anywhere.legal supports this model by always placing AI outputs in the context of a specific case and allowing notes, comments, and revisions from individual team members to be attached.

For example, a lawyer can ask AI to summarize selected contracts with a note that it will only be an internal document, and then, based on this summary, prepare their own memorandum to be submitted to the client or court.

At each step, it is clear what is an "AI proposal" and what is a "human revision," which facilitates both internal quality control and, if necessary, substantiation of procedures in the context of compliance or disputes.

Engaging the right expert and international coordination

In international matters, the decisive problem is often not just "what the law says," but also how a specific jurisdiction applies it in practice, what local customs are, and how to harmonize the requirements of multiple legal systems into a single workable solution.

AI can help quickly map the basic contours of legal regulation in individual countries, identify differences and similarities, and suggest potential conflicts.

However, it cannot replace the experience of a local lawyer who knows the current practice of courts and administrative bodies, informal expectations of regulators, or practical details that are not evident from the text of the law.

This is especially true in areas where law is dynamic (e.g., personal data protection, digital services, labor law) or where soft-law documents and regulatory dialogue are highly significant.

Anywhere.legal therefore combines AI tools with the possibility of inviting local experts in relevant jurisdictions to the case.

At the moment when AI helps structure the assignment and identify the basic range of questions, partner lawyers or tax advisors in individual countries can be approached within the same platform, sharing the central brief and documents with them and coordinating their outputs to form a consistent whole.

AI can help with translations, summarizing local opinions into a common language or with identification of mutual discrepancies, but the final harmonization and decision on strategy remain with the lead legal team.

This significantly reduces the risk of isolated local answers that are incompatible or that a jurisdiction is overlooked.

AI governance, audit trails, and transparency

Part of the ethical use of AI is also the issue of governance – how an organization systematically manages AI-related risks, documents tool usage, and demonstrates that it has taken appropriate measures to ensure fair, secure, and transparent results.

Best practice frameworks, such as the NIST AI Risk Management Framework or the Blueprint for an AI Bill of Rights, emphasize principles of fairness, transparency, accountability, privacy, and human oversight as fundamental pillars of responsible AI deployment.

In practice, this means, for example, maintaining an inventory of AI systems used, assessing their impact on individual rights, implementing processes for testing and monitoring performance, and establishing clear lines of accountability in case of errors or incidents.

Anywhere.legal supports these principles by ensuring that every use of AI tools within the platform is part of the case's audit trail: it is possible to trace what materials were used, what prompting occurred, what outputs AI generated, and how individual team members intervened.

This process transparency is often more important than striving for so-called "explainable AI" at the level of an individual model, which runs into the technical limits of interpretability of deep neural networks.

For courts and regulators, in terms of legal liability, it is often more relevant whether an organization can show how it implemented AI, what testing it performed, and what controls it has over the outputs, rather than explaining in detail why the model chose a specific token.

In addition to technical and procedural aspects, open communication with users and clients about how AI is used in the organization is also important.

One trend is the publication of AI transparency statements, in which organizations describe for what purposes they use AI, what their limitations are, and what guarantees they provide in the area of data protection and human oversight.

Anywhere.legal can thus be part of a broader AI governance framework for companies: as a centralized place where legal and tax cases are handled in a manner consistent with internal policies, regulatory requirements, and client expectations.

Typical situations from practice where AI ethics breaks down

Theoretical frameworks and principles are important, but for companies and lawyers, concrete situations in which the impact of tool and process choices in the real world becomes clear are often the most convincing. In the following subsections, we will therefore discuss several typical scenarios that recur in practice and show how their resolution would look in a chaotic, uncoordinated environment, and how a systematic approach through a platform like Anywhere.legal can help.

Hallucinated citations in court filings

The first situation, which has become a symbol of the risks of generative AI in law, is the submission of court filings with fabricated citations.

Since 2023, several hundred cases have been documented worldwide where lawyers or pro se litigants relied on an open AI tool for legal research and inserted decisions into filings that never existed.

Courts responded differently: in some cases, they imposed financial sanctions, in others, they ordered mandatory training on AI use or issued more general guidelines for lawyers to declare whether they used AI in preparing filings.

The typical course of the problem is as follows: a lawyer under time pressure submits a query to an AI tool to support a specific legal thesis, receives an answer with several citations that look convincing, and instead of verifying each citation in the primary source, inserts them directly into the filing.

When it later turns out that the decisions do not exist or do not say what the AI claimed, the lawyer stands before the court in a situation where they cannot explain how such an error could have passed their scrutiny.

The reference to "AI error" is not accepted by courts as an excuse: responsibility according to procedural rules rests with the person who signed the filing.

In an Anywhere.legal environment, such a situation would look different for several reasons.

Firstly, the lawyer would have already defined at the time of assignment for which jurisdiction and court the filing was to be prepared, which would direct the AI to relevant sources and reduce the risk of "mixing" legal regulations.

Secondly, legal research tools would be built on curated databases where the existence and correct citation of every decision are guaranteed, and AI would serve to select and summarize them, not to invent new judgments.

Thirdly, the workflow would include a mandatory step of verifying citations in primary sources – either directly through links to databases or an internal checklist that the lawyer must complete before submitting the filing.

Finally, the audit trail in the platform would allow for documentation of the steps taken to verify the output, which can be important for compliance or potential complaints.

Working with sensitive data and loss of privilege

The second scenario concerns working with sensitive information and protecting attorney-client privilege. Imagine a client facing an investigation who tries to "prepare" for a meeting with a lawyer by uploading internal emails, contracts, and their notes to an open AI tool and asking the system for "risk analysis" or proposed arguments.

Later, they provide these documents to the lawyer, stating that "AI has already prepared it." When the question arises in criminal proceedings to what extent these documents are protected by attorney-client privilege or work product doctrine, the court may conclude that they were voluntarily disclosed to a third party already at the time of their creation and that their confidentiality cannot be reasonably expected.

Exactly this situation was addressed by federal judge Jed Rakoff in a case that drew wide attention from the legal community.

The court concluded that documents generated by a client through a public AI platform are not protected by either attorney-client privilege or work product doctrine because they were not created under the guidance of a lawyer, AI was not a necessary intermediary for understanding communication, and the terms of service precluded a reasonable expectation of confidentiality.

In other words: it is not possible to first share sensitive information with an unknown AI provider and only then try to "clothe" this communication with attorney-client privilege.

Anywhere.legal offers a different model. Both client and lawyer work from the outset in a closed environment designed to meet confidentiality requirements and allow communication to be considered part of legal advice.

If AI processes materials or helps structure them, it is done under the guidance of the lawyer and within a system where data is encrypted, access is controlled, and the provider contractually guarantees that inputs will not be used to train models or made accessible to third parties.

This significantly strengthens the argument that communication and documents generated in such an environment may fall under the legal protection of attorney-client privilege or work product – provided, of course, that they also meet other conditions of these doctrines.

Bias and algorithmic decision-making in HR and compliance

The third situation, which appears more and more frequently in practice, concerns the use of AI in areas where decisions directly affect people: for example, in recruiting employees, assessing risks in criminal proceedings or financial services, or in compliance screening processes.

Companies may be tempted to use AI to automate candidate selection, assess the suitability of business partners, or evaluate the risk of regulatory violations, but if they are not careful, they may find themselves in a situation where their decision-making reproduces historical discriminatory patterns or violates principles of equal treatment.

Because models reflect the data on which they were trained, they can statistically disadvantage certain groups even without explicit use of protected characteristics, for example, through proxy variables.

Legal systems in many jurisdictions are responding to this problem with requirements for auditing algorithmic systems, transparency of criteria, and the possibility of human review of decisions.

In some cases, regulations require organizations to conduct impact assessments on personal data protection or non-discrimination before deploying an AI system and to ensure the possibility of redress if results prove unfair.

In legal practice, this means that lawyers often advise companies not only on selecting appropriate tools but also on setting governance, documenting the decision-making process, and communicating with affected individuals.

Anywhere.legal can serve as a platform in such situations where these aspects are processed in a structured manner.

The case assignment can include not only a description of the technical tool but also legal questions related to personal data protection, anti-discrimination regulation, and data subject rights.

AI can help gather and structure relevant legal frameworks in different jurisdictions, but the assessment of whether a specific algorithm meets fairness and due process requirements must be performed by lawyers.

In the same platform, experts in personal data protection, HR law, and financial services regulation in different countries can be involved, their inputs linked, and a consolidated recommendation created for the client, including a proposal for internal policies and procedures.

International due diligence and team coordination

The fourth scenario concerns an area where AI brings enormous practical benefits but also requires careful control: extensive due diligence in M&A transactions or financing.

AI systems can review thousands of contracts, identify key clauses, deviations from standards, or risky elements, which previously required weeks of work by dozens of junior associates.

In an environment where a transaction involves multiple jurisdictions, multiple languages, and different types of documents, AI is able to create a structured overview of risks and facilitate management decision-making.

The risk lies in the fact that if AI outputs are adopted without sufficient control, some local nuances may remain hidden or may be misinterpreted.

For example, a clause that is standard in one country may have different legal effects in another jurisdiction, or its enforceability may be limited.

AI may classify a clause as "low risk" according to a global pattern, but a local lawyer may know that local courts interpret it differently.

Similarly, AI may underestimate regulatory risk because it does not consider the current trend of supervisory body decisions that have not yet been reflected in formal decisions.

Anywhere.legal can address these challenges by linking AI document analysis with the work of local experts on a single platform.

AI creates an initial risk map, summarizes key clauses, and identifies areas requiring deeper investigation.

Based on this material, local lawyers in individual countries can add their comments, mark clauses that are problematic in their jurisdiction, and propose mitigations or adjustments.

The lead team then sees not only the "AI score" but also human expertise from individual countries on the platform, allowing for informed decisions.

The result is not blind reliance on AI but a combination of its efficiency with local legal know-how.

Practical recommendations for companies and legal teams

From the perspective of companies and legal departments, the question is not whether to use AI, but how to use it in a way that brings benefits and simultaneously does not introduce undue risks.

Regulators and courts are increasingly making it clear that professionals are responsible for understanding the capabilities and limitations of AI, and that in some areas, the use of appropriate tools may become part of the standard of due care.

At the same time, it remains true that responsibility for AI errors lies with those who allowed them to pass unchecked, not with the technology itself.

The first practical step is to develop an internal AI policy that clearly states what tools are allowed to be used, for what purposes, and under what conditions.

Such a policy should distinguish between open consumer tools, which must not be used for sensitive or client information, and closed enterprise platforms that meet data protection and confidentiality requirements.

It should also describe mandatory review of AI outputs, for example, through checklists for verifying the scope of the assignment, jurisdiction, citations, factual correctness, and stylistic adjustments.

For some types of outputs (to be submitted to a court, opposing party, or client), a double check or approval by a senior lawyer may be required.

The second step is to invest in training and increasing AI literacy in legal teams.

Technological competence does not mean that a lawyer becomes a data scientist, but that they understand the basic nature of the tools they use, know where hallucinations and bias may occur, and can effectively integrate AI into the workflow.

Training should also include examples of failures from practice, analysis of court decisions in the AI area (e.g., cases of sanctions for fabricated citations or loss of privilege), and discussions on how to prevent these situations.

Anywhere.legal can serve as a practical laboratory in this regard: an environment where lawyers can learn AI-supported workflow within a secure framework that reflects ethical and regulatory requirements.

The third element is the integration of AI governance into the broader risk management framework of the organization.

This includes inventorying all AI systems used by the organization, assessing their impact on individual rights, setting performance monitoring, and establishing mechanisms for correcting errors.

From a legal perspective, it is important to be able to demonstrate that the organization acted with due care: it tested tools before deployment, monitored their performance, responded to identified problems, and ensured human oversight of critical decisions.

Platforms like Anywhere.legal facilitate this documentation by recording every use of AI tools in the context of specific cases, making it possible to reconstruct how outputs were used and controlled.

Finally, it is important to remember that AI must not lead to the decline of basic legal skills.

The phenomenon of "AI dependency" described in the literature warns that if professionals rely too heavily on AI, they may gradually lose the ability to independently analyze, argue, and decide. In law, this would mean weakening the core of the profession.

The solution is a model of conscious cooperation, in which AI accelerates routine tasks, but the organization simultaneously invests in maintaining and developing human skills through regular manual exercises, dual assessment of AI outputs, and maintaining "analog" backup processes in case of technology failure.

Anywhere.legal supports this approach by always embedding AI outputs in a process that assumes human interpretation and revision, not blind adoption.

Conclusion: Why combine ethical AI and human oversight into one process

AI ethics in legal work is not an abstract academic topic but a question of daily practice, responsibility to clients, and protecting the credibility of the legal system.

Generative AI offers lawyers unprecedented opportunities: it can review thousands of documents in seconds, propose filing structures, summarize case law, or help coordinate multi-jurisdictional projects.

At the same time, it functions as a probabilistic system that lacks an internal concept of truth, justice, or professional responsibility, and therefore tends to hallucinate, reproduce data biases, and generate outputs that "sound correct" but may not be legally accurate.

In the legal environment, where a minor inaccuracy can mean a significant difference in liability or litigation outcome, it is therefore essential that AI is always embedded within a framework of human oversight and clear ethical rules.

Bar associations, regulators, and courts are gradually confirming several basic principles.

Firstly, AI is permissible to use, but must never replace independent legal judgment and a lawyer's responsibility; its outputs must always be verified similarly to the work of junior colleagues.

Secondly, working with AI must respect confidentiality and the protection of sensitive information, which excludes uncontrolled use of public tools for client data and at the same time requires closed, contractually secured environments for secure processing.

Thirdly, organizations have a duty to understand and manage the risks associated with bias, discrimination, and non-transparent decision-making, through AI governance frameworks, audits, and human review mechanisms.

The Anywhere.legal platform is an example of how these principles can be transformed into a practical, everyday process.

By connecting assignment structuring, document centralization, AI for initial information processing, and the involvement of local experts in a single secure environment, it allows typical cross-border legal and tax situations to be resolved faster, more systematically, and with better risk control.

AI accelerates the understanding of materials and the generation of proposals, while decision-making, legal interpretation, and final responsibility remain firmly in the hands of lawyers and other experts.

Need international legal help? Get in touch with us via Anywhere.legal.

FAQ: Frequently asked questions on AI ethics in legal work

1. What is the main difference between using an open AI chatbot and a platform like Anywhere.legal when dealing with legal matters?
   An open AI chatbot typically functions as a consumer service that collects user data, uses it for further model training, and does not provide guarantees corresponding to the requirements of attorney-client privilege or work product protection. A platform like Anywhere.legal, on the other hand, is conceived as a closed, contractually secured environment for working with legal and tax cases, where data is processed only for the purposes of specific cases, is encrypted, and its use is subject to a clear audit trail. In addition, it integrates AI into a structured workflow that assumes human review of outputs and the involvement of local experts, so the risk of errors and ethical failures is significantly lower.

2. Can I, as a lawyer, use AI for legal research, or does that violate ethical rules?
   Most expert opinions and ethical codes today permit the use of AI for legal research and related tasks, provided the lawyer maintains responsibility for the outcome, protects confidentiality of information, and verifies the accuracy and completeness of outputs. This means that you can use AI for quick searches of relevant case law, summaries of judgments, or proposed arguments, but you must not adopt citations, legal conclusions, or factual claims without verification. It is advisable to use specialized legal AI tools or platforms like Anywhere.legal that draw from authoritative sources and support citation verification, instead of generally available chatbots.

3. How can I explain to a client that we use AI without undermining their trust?
   Transparent communication emphasizing that AI serves as a tool to increase efficiency and quality, not as a replacement for the lawyer, usually strengthens rather than weakens trust. You can explain to the client that AI helps with rapid understanding of materials, first drafts of texts, and identification of risks, allowing more time to be dedicated to strategy and individual advice, while all outputs are verified by an experienced team. If you use a platform like Anywhere.legal, you can add that it is a closed, secure environment with clear rules for maintaining confidentiality and an audit trail, so the client has better control over how their data is handled.

4. What are the biggest mistakes lawyers make when using AI?
   The most common mistakes include uncritical adoption of AI outputs without verification, especially concerning case law citations or legal conclusions, using open AI tools for sensitive or client information, and the absence of an internal policy that would set rules and limits for AI use. Another mistake is relying on AI in areas where the model lacks sufficient data or where there is a high risk of bias, for example, in decision-making about people or in newly regulated industries. The solution is a combination of appropriately chosen tools (ideally closed and legally oriented platforms), internal rules for verifying outputs, and regular training of the team in AI literacy.

5. Is it possible to regulate AI in law to ensure fairness and transparency, or is the technology too much of a "black box"?
   Some aspects of the functioning of deep neural networks remain difficult to explain, but that does not mean that AI cannot be legally and ethically regulated. Instead of attempting a complete "disassembly" of the model, many experts favor an approach that emphasizes procedural transparency, auditability, communication of uncertainty, and ensuring human oversight of decisions with significant impacts. Regulations such as the EU AI Act, American proposals for algorithmic accountability, and professional standards in law are moving precisely in this direction: requiring risk assessments, explainability of decision-making processes at the system level, protection against discrimination, and the possibility of effective redress. Platforms like Anywhere.legal can help companies practically fulfill these requirements by embedding AI in a structured process with clear roles, controls, and documentation.