As AI continues to evolve and is part of most of our daily work tools, it brings both benefits and challenges. AI accountability is one of them—a concept that that raises a fundamental question: who should be held responsible for AI technology’s outcomes, whether good or bad.
For legal professionals and General Counsel, expectations are high. They are expected to demonstrate AI proficiency and to serve as guardians of organizational risk, alongside other departments such as IT.
Court are drawing a precise line: if the system sets defaults and humans mainly affirm outputs, oversight becomes procedural, not substantive, and that is not a shield. With the AI Act in the European Union and increasing state regulations reflecting this shift, the GC must get ready to work with AI. In this article we will uncover what the GC must know.
What is AI accountability
AI accountability is the principle that humans, not machines, bear legal and operational responsibility for AI outcomes. The main questions asked and expected to have clear, available answers are:
The term entered mainstream discourse in the early 2020’s as AI expanded its footprint across industries. Unlike transparency (disclosing how AI works) or ethics (defining what should be done), AI accountability ensures someone can be called to answer when something goes wrong. It is the control system that turns AI governance from policy to practice.
The regulatory reality: AI accountability is now mandatory
The regulatory landscape around AI-powered tech is changing and for the most part, regions across the world are seeing new rules or recommendations enforced:
Overall, courts are ready to hold deployers (the ones using the AI tech), and not only providers, accountable. When something goes wrong, responsibility flows to the humans who approved, deployed, or used the system. This includes the General Counsel within an organization.
But here is the critical insight: Findings from AuditBoard & Panterra Research claim 44% of GRC professionals cite unclear AI accountability as the number one barrier to effective governance.This is a challenge many in the legal profession may encounter. The issue is not only in the tools themselves; it lies in ownership. When accountability is unclear, AI governance becomes a compliance checkbox rather than an operational advantage. But when ownership is clearly defined, AI accountability becomes the framework that helps legal teams work with AI more effectively—turning regulatory obligation into strategic capability.
Every AI deployment should be able to answer the following five questions before go-live, not after
Five questions to guide AI accountability
There should always be an answer to the following questions once an AI system is deployed. This entails, of course, that the organization (and not only the GC) must have thought about this before deployment and do continuous monitoring throughout the tool’s lifecycle:
Who approved it?
Every system needs a named accountable executive and documented approval. An AI inventory is the prerequisite, because you cannot govern what you cannot see. The AI inventory will serve as the source of truth register of all the AI systems (tools and models) deployed across the company and their respective owners.
What data did it touch?
Best practice – and under the AI Act, a legal requriement – is to audit high-risk AI systems for bias in their training data. . Under some circumstances, such as under the AI Act, this is a requirement. For legal AI trained on historical documents, that means auditing for encoded jurisdictional, demographic, or jurisprudential biases. Data governance is not extra admin work, it is the control system for enterprise risk.
What harm could it cause?
In 2024 alone, 233 AI-related incidents were reported, representing a 56% increase. Rather than asking whether a tool could cause harm, assume it can and test before deployment. This is an ongoing monitoring commitment, not a one-time review. How is it monitored?
Whether mandatory or not, this is a common best practice, as systems drift over time. Event logging and ongoing monitoring enable teams to keep precise documentation that justifies and explains errors if they happen. Under the AI Act, it is required to retain logs for six months minimum. Audit trails designed to respect privilege are critical for legal teams.
Who answers if it fails?
Vendor liability cannot be fully transferred. In the United States, courts hold deployers accountable regardless of vendor disclaimers. Under EU jurisdiction, the AI Act creates clear liability: providers bear primary obligations, deployers carry secondary obligations under Article 26. Fines can reach €35 million or 7% of global turnover. For legal teams, the privilege paradox is real: logging requirements can turn audit trails into discoverable records unless governance is designed with privilege protections from the outset.
Failure is also where the GC must lead. While AI governance is a team effort, the GC is expected to work on AI accountability by following some rules and best practices.
What AI accountability means for the general counsel
The 2026 ACC Chief Legal Officers Survey found that 47% of CLOs say their CEOs expect them to develop AI proficiency as the top capability demand. So now that AI governance is a boardroom responsibility, the GC has become its anchor.
AI accountability is the mechanism that will assign consequences when things fail or go wrong, and to do so, organizations must now build governance structures for this principle, which has no real precedent.
The good news is that this goes to further prove how AI is not here to replace humans. It simply enforces an approach that only strengthens the very much needed human role in decision-making and accountability. As Rupali Patel Shah, Head of Legal Solutions at DiliTrust, stated in a recent piece, it “feels obvious but somehow keeps getting lost in the noise: AI cannot be held accountable—not legally, not ethically, not operationally.”
Ultimately, AI cannot make decisions alone; humans do. The harm caused by an AI system is always traceable to a human choice—what data to use, what use case to authorize, what oversight to implement.
The GC’s new responsibilities include:
AI accountability is shared across boards, IT, compliance, and business units. But where governance has not been established, the GC must lead proactively. When something goes wrong, regulators will not ask whether you predicted the failure—they will ask whether you documented decisions, maintained audit trails, and applied oversight. Defensibility depends on process, not prediction.
AI amplifies the legal function; it does not replace it. What defines GC value is what AI cannot replicate: sound judgment, courage, and trusted relationships. 63% of CLOs expect headcount to remain stable while using AI for efficiency —because the work isn’t disappearing; it is evolving.
AI accountability is not a burden. It’s an opportunity to define how legal leads in an era where technology moves faster than precedent.
