The responsibilities that come with legal AI: avoiding hallucinated citations, keeping confidentiality, disclosing AI use, and the verification discipline that keeps a human firmly accountable.
22 articles
How AI intersects with legal malpractice and discipline: the standard of care, the five risk scenarios that cause trouble, the duties of competence and supervision most in play, and a practical mitigation plan built on verification, grounded tools, governance, training, and insurer disclosure.
Why human oversight of legal AI is non-negotiable: a clear definition of human-in-the-loop, the hallucination, bias, and accountability reasons behind it, a map of where humans must stay involved, and the product design - citations, visible reasoning, fast verification - that makes oversight practical.
A clear-eyed look at bias and fairness in legal AI: where bias enters, what the COMPAS debate reveals about competing fairness definitions, how research tools differ from automated decision-makers, and the mitigations - transparency, grounded citations, diverse data, audits, and human oversight - that actually help.
Three years after fabricated citations first made headlines, hallucinations remain legal AI's defining risk; but the landscape has matured. A mid-2026 review of court responses, the persistent causes, the real improvements, and the working rules that keep firms safe.
A practical security checklist for vetting any legal AI vendor - encryption, no-training guarantees, hosting and residency, access controls, audit logging, retention, sub-processors, and certifications - mapped to the ABA Rule 1.6 duty of confidentiality.
A practical prevention guide to fake AI citations: why fabrication happens, how grounded retrieval tools differ from ungrounded chatbots, the verification workflow that keeps hallucinated authority out of filings, and what judges now expect.
A step-by-step checklist a lawyer can run on any AI legal research before relying on it: confirm the source exists, read the cited passage in context, check it supports the proposition and is still good law, and verify quotes - in seconds, not an afternoon.
Using AI in legal practice is ethical when done competently and with oversight. This guide explains ABA Model Rule 1.1 and its Comment 8 duty of technology competence, walks through ABA Formal Opinion 512 on generative AI, and turns the duties into practical habits.
AI hallucinations - fabricated cases, fake citations, false quotes - are the defining risk of generative AI in law. Using the Mata v. Avianca sanctions as a lesson, this guide explains why models invent, how common it really is, and how grounded, citation-first tools plus human review keep you safe.
Separate work performed, review time, agreed fee arrangements, expenses, and client requirements when documenting AI-assisted legal services.
Review matter restrictions, client instructions, service terms, data handling, and permitted use before uploading legal documents to an AI tool.
Record potential privilege issues, participants, document purpose, context, and counsel’s review without treating automated labels as final decisions.
Check purpose, recipients, confidentiality, source support, language, and local qualifications before circulating a regional legal AI output.
Turn a general human-in-the-loop policy into practical review criteria for legal research, contract extraction, and drafting in an EU team.
A practical supervision record for UK law firms and in-house teams using AI: task limits, source checks, escalation, and final review ownership.
Most firms now use AI; far fewer have written down the rules. This walkthrough builds a practical AI use policy section by section - scope, approved tools, data rules, verification duties, client disclosure, and review - short enough that lawyers actually read it.
Regulators worldwide have converged on a clear position: lawyers may use AI, and the old duties govern how. A mid-2026 tour of what competence, confidentiality, supervision, and candor now demand: and the policy skeleton that keeps a firm on the right side.
A source-led comparison of selected US bar guidance on confidentiality, verification, supervision, communication and fees, including California’s May 2026 update.
The same email from a general counsel can be privileged in New York, unprotected in an EU competition dawn raid, and - since the Indian Supreme Court's October 2025 ruling - outside advocate-client privilege in India too. A four-regime map of where in-house privilege holds and where it breaks, for the lawyers who live inside it.
What unauthorized practice of law (UPL) means for AI tools: the line between legal information and legal advice, why lawyer-supervised AI is safe ground, and where direct-to-consumer tools raise UPL questions.
A practical framework for law-firm AI governance: an acceptable-use policy, an approved-tool list, data-handling rules, a verification mandate, supervision under the ethics rules, client disclosure, and incident handling, all mapped to the NIST AI Risk Management Framework.
A clear guide to attorney-client privilege and AI: how privilege differs from the duty of confidentiality, whether sending client data to a vendor risks waiver, what the reasonable-efforts standard requires, and the diligence questions to ask before you upload.
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