It goes beyond mere legal compliance, it addresses bias, fairness, and the ethical duty to ensure decisions are just and explainable.

The rise of artificial intelligence in the legal and tax professions is accelerating processes, reducing costs, and transforming professional practice. But this transformation poses a crucial ethical challenge: how can we ensure that AI tools are not only efficient but also fair, accountable, explainable, and aligned with the values of law?

For lawyers and advisors, it is not enough to simply “use AI properly.” It is about integrating AI within an ethical responsibility framework, because client trust, justice, professional oversight, and fairness of outcomes all depend on it.

From regulation to professional ethics

So far, much of the writing on AI has focused on compliance, for instance, data protection, professional secrecy, or emerging regulation. But for legal professionals, ethics goes further: it involves competence, transparency, fairness, and social impact.

In the United States, for example, the American Bar Association (ABA) addresses this in its formal guidance on AI: lawyers must uphold their duties of competence, confidentiality, communication, and billing when using AI.

In the judicial context, it is also noted that both judges and lawyers must have “technological competence” to use AI ethically.

Thus, legal professionals should ask themselves:

• Am I using AI with sufficient understanding of its risks and limitations?
• Can I explain to my client what role AI played in forming my legal reasoning?
• Am I ensuring that AI promotes justice, not just efficiency?

This mindset shift is essential: ethics is not an added cost, it is part of professional diligence.

Bias, fairness, and algorithmic discrimination

A well-known but increasingly important issue: AI systems can replicate or amplify historical inequalities, and in the legal field, this has direct implications for rights and safeguards.

Sources of bias

• Training data reflecting outdated practices, prejudiced rulings, or overrepresentation of certain groups.
• Algorithmic designs that prioritize efficiency or volume over justice or explainability.
• Lack of effective human oversight: accepting outputs without verification.

How this affects legal professionals

An AI system might suggest strategies, draft arguments, or analyze case law with unnoticed bias against certain groups, potentially breaching principles of equal treatment.

If not critically reviewed, the result may show formal similarity to a good answer, yet lack real justice. In legal terms, appearance is not enough.

In the EU, the legal notion of “non-discrimination” intersects with data science concepts of algorithmic fairness, but the two do not fully overlap, creating additional risks.

Good practices to avoid bias

While it is not feasible for every professional to oversee or train models directly, two steps are within reach:

• Foster a culture of critical review among professionals.
• Document human oversight and decision-making criteria whenever AI is used.

Advanced explainability: reasoning, transparency, and contestability

Previous discussions have stressed the need for AI to “show where information comes from.”

Here we go further: in law, explanations must be legally relevant, meaning clients or third parties (e.g., courts) should understand how and why a conclusion was reached.

Key points for sound legal AI explainability

• Traceability: each suggestion should link to a specific statute, precedent, or doctrine.
• Functional transparency: the professional must know what model, data, and limitations were involved.
• Contestability: the professional must be able to modify or reject an AI output and offer an alternative rationale to the client or other parties.
• Context of use: jurisdiction, update date, and scope limits.

For lawyers and advisors, this means an ethical AI tool should not just provide a “result” but show the reasoning path, something deeply familiar in legal work.

Privacy, confidentiality, and data sovereignty

Privacy and confidentiality have often been discussed, especially “zero data retention” and European servers, but there are deeper implications for professionals:

• Data leakage risk: with generative AI or large models, entering client-sensitive data into uncontrolled systems can expose information.
• Infrastructure: data sovereignty matters. If AI operates on non-EU servers or mixes data across jurisdictions, it may indirectly breach professional secrecy or GDPR.
• Informed client consent: when using AI, it is good practice to inform clients that AI was used, how, within what limits, what data was involved, and what safeguards apply. This is part of ethical transparency.

Thus, confidentiality is not just “not leaking data.” It is ensuring that the architecture, policy, provider, and oversight all meet standards aligned with professional responsibility.

Human oversight, technological competence, and ai culture

For AI to be ethical, the human factor is essential, but not just in signing off at the end. Oversight must be structured, trained, and integrated into the firm’s culture.

Required technological competence

Professionals must understand the capabilities and limits of AI, stay up to date, and undergo regular training. Current guidelines recognize technological competence as part of ethical duty.

Internal protocols are key: when to use AI, when not to, how to review outputs, and who signs off.

Ethical AI culture

• Establish internal AI-use policies: from provider selection to output audits.
• Provide ongoing staff training on bias, confidentiality, and explainability.
• Ensure management or partners provide oversight: AI ethics cannot be left to individual discretion alone.

Emerging regulation and standards every professional should know

Ethical AI is not confined to internal firm policies. The regulatory landscape is evolving rapidly, and lawyers must stay ahead.

In the European Union, the Artificial Intelligence Act (AI Act) imposes obligations for “high-risk” AI systems (including aspects of legal services) to ensure explainability, risk assessment, human oversight, data quality, and technical documentation.

In the United Kingdom, The Law Society’s 2025 updated guidance on generative AI covers the risks of “hallucinations,” “incompatible jurisdictions,” and the necessity of supervision.

For lawyers and advisors, this means that ethical AI is not optional, it is a condition for competitiveness and future compliance. Failing to prepare for these standards is a professional risk.

Dilemma: should you tell the client you used AI?

From an ethics-of-transparency perspective, yes: explain what part of the work was AI-assisted, what reviews were done, what data was used, and what limitations apply.

Not doing so may erode trust and even violate communication duties in service provision.

Conclusion: ethics as a strategic advantage

Adopting AI ethically does not mean rejecting innovation, it means integrating it with professional purpose.

For lawyers and advisors, it means combining speed, quality, trust, and fairness.

An ethical AI, properly implemented, enables professionals to:

• deliver services more efficiently without sacrificing rigor.
• strengthen client trust.
• anticipate future regulations.
• protect professional reputation.
• ultimately, contribute to justice and the proper functioning of the law.

Remember: law is not just about rules, it’s about values.

And when you use AI, you must ensure those values continue to guide every step. Because, in the end, technology assists, but professional integrity signs the work.