Category: Roles & Accountability

Roles and Responsibilities

Also known as: R&R, role definition, roles and responsibilities matrix

Simply put

Roles and responsibilities describe who does what within a team or organization, pairing a person's position (their role) with the specific tasks and duties they are accountable for (their responsibilities). Defining them clearly helps people understand who is responsible for what, which can reduce overlap, prevent conflict, and support better teamwork and decision-making. In an AI governance context, this concept is applied to clarify who owns particular oversight, development, and control activities for AI systems.

Formal definition

In organizational terms, a role establishes the framework for an individual's position within a team, while responsibilities specify the recurring tasks and duties tied to that position that keep operations running. Clearly documenting and communicating roles and responsibilities is commonly used to avoid duplicated effort, resolve accountability gaps, and improve decision-making. Applied to AI governance, roles and responsibilities typically formalize accountability for AI system oversight, development, validation, and control; note that the general-team evidence supplied here does not itself define the governance-specific allocations (for example, first, second, and third line of defense assignments) or the distinction between accountable ownership and delegated execution, which are treated as out of scope for this evidence-based definition and would require framework-specific sources to specify.

Why it matters

In AI governance, ambiguity about who owns which oversight, development, and control activities is a common source of accountability gaps. When roles and responsibilities are not clearly defined and communicated, tasks can be duplicated, overlooked entirely, or left in dispute between teams—outcomes that the general organizational evidence associates with costly overlaps, conflict, and weaker decision-making. Clear role definition is therefore a foundational governance measure that helps an organization establish who is answerable for a given AI system and its associated activities.

It is important to frame roles and responsibilities as a mechanism that reduces and manages the risk of confused accountability rather than one that eliminates it. Documenting who does what supports better coordination, but it does not by itself guarantee that the underlying oversight, validation, or control work is performed adequately. The evidence supplied here concerns general team and organizational practice; it does not establish AI-specific requirements, and it should not be read as prescribing a particular governance model.

Because the framework-specific allocation of responsibilities—such as line-of-defense assignments or the distinction between accountable ownership and delegated execution—falls outside the general evidence used here, organizations typically look to their applicable governance frameworks, policies, or regulatory guidance to specify those details. Where such allocations are needed, they should be drawn from framework-specific sources rather than inferred from general role-definition practice.

Who it's relevant to

AI Governance and Compliance Officers

Those responsible for organizational oversight of AI systems use clear role definitions to establish who is accountable for specific governance, oversight, and control activities. Note that the general evidence here supports the value of clarifying roles but does not prescribe a particular governance-specific allocation, which would draw on applicable frameworks or policies.

Model Risk and Validation Teams

Teams involved in developing, validating, or controlling models rely on defined responsibilities to distinguish who performs which recurring tasks and to avoid gaps or duplication. The specific separation of development, validation, and control duties is framework-dependent and is not defined by the general organizational evidence used here.

Team Leaders and People Managers

Managers use role definition, including workshops and responsibility matrices, to communicate positions and duties across a team, which is commonly associated with reduced conflict, less overlapping effort, and improved decision-making.

Auditors and Second-Line Reviewers

Those assessing an organization's governance may examine whether roles and responsibilities are documented and communicated as a foundational control. They should treat clear role definition as a measure that helps manage accountability risk rather than one that guarantees adequate performance of the underlying activities.

Inside Roles and Responsibilities

Accountability Assignment

The allocation of specific decision-making authority and answerability for AI systems to named individuals or functions, typically distinguishing who owns a model, who approves its use, and who is answerable for outcomes. In many governance frameworks this is documented so that responsibility does not remain diffuse.

Three Lines of Defense

A commonly used organizational model separating risk-taking functions (first line, e.g., model developers and business owners), independent risk oversight and challenge (second line, e.g., model risk management and compliance), and independent assurance (third line, e.g., internal audit). These lines are distinct and should not be collapsed; the second line challenges the first, and the third line provides independent assurance over both.

Model Owner and Developer Roles

First-line roles responsible for the design, implementation, appropriate use, and ongoing performance of a model. As commonly defined, the model owner is accountable for the model serving its intended purpose, while developers are responsible for its construction and documentation.

Independent Validation and Review Roles

Roles, typically situated in the second line, that provide effective challenge and independent assessment of models. Note that validation (assessing whether a model is conceptually sound and fit for purpose) is distinct from verification (confirming a model was implemented as specified); role descriptions should reflect that distinction.

Senior Management and Board Oversight

Governance-level responsibilities for setting risk appetite, approving policies, and overseeing the aggregate use and risk of AI systems. This is an AI governance function—organizational oversight and accountability—rather than a model-level risk measurement activity, though the two overlap.

Segregation of Duties

The principle that those who develop or use a model should not be the sole parties assessing or approving it, so that independent challenge is preserved. This supports, but is not identical to, the independence expected of second- and third-line functions.

Common questions

Answers to the questions practitioners most commonly ask about Roles and Responsibilities.

Does assigning roles and responsibilities for an AI system eliminate the associated model risk?

No. Clearly assigned roles and responsibilities are a governance control that helps allocate accountability and reduce the likelihood that risks go unmanaged, but they do not eliminate model risk. Risk typically remains even with well-defined roles, and residual risk should still be identified, monitored, and controlled. Assigning ownership clarifies who is accountable for managing risk; it does not remove the underlying risk itself.

Are roles and responsibilities the same thing across AI governance and model risk management?

Not necessarily. Roles and responsibilities appear in both AI governance (which focuses on organizational structures, policies, accountability, and oversight for AI systems) and model risk management (which focuses on identifying, measuring, monitoring, and controlling risks arising from model use). While the two domains overlap and may share personnel or titles, they are distinct: governance roles often address broader oversight and policy, whereas model risk management roles are commonly framed around functions such as development, validation, and independent review. Treating them as interchangeable can blur accountability lines that experts intentionally keep separate.

How do roles and responsibilities typically map to a three-lines-of-defense structure?

In many frameworks, roles are allocated across three lines: the first line (for example, model owners and developers) typically owns and manages risk in day-to-day activities; the second line (for example, model risk management or compliance functions) typically provides independent oversight, challenge, and policy; and the third line (for example, internal audit) typically provides independent assurance over the effectiveness of the first two. The specific titles and boundaries vary by organization and sector, so the mapping should be documented rather than assumed.

What should a roles and responsibilities definition include to be useful for accountability?

As commonly defined in practice, a useful specification identifies who is accountable, who is responsible for execution, who provides independent review or challenge, and who provides assurance. It typically also clarifies decision rights, escalation paths, and the boundary between owning risk and overseeing it. Documenting these elements helps avoid gaps or overlaps in accountability, though the appropriate level of granularity depends on the organization and the risk profile of the AI system.

How should organizations avoid conflicts of interest when assigning roles?

A common approach is to maintain independence between those who develop or own a model and those who validate, challenge, or provide assurance over it. Separating these functions helps preserve the objectivity of independent review. Where full separation is not feasible—for example, in smaller organizations—compensating controls and clear documentation of the limitation are often used. The adequacy of any such arrangement typically depends on the risk level and applicable expectations, which vary by sector and jurisdiction.

How can roles and responsibilities be kept current as AI systems and teams change?

Because AI systems, personnel, and organizational structures evolve, role assignments are typically reviewed and updated on a defined cadence and after significant changes—such as a material model change, a reorganization, or a shift in the system's use or risk profile. Maintaining a documented, current record of who holds each role supports auditability and helps ensure that accountability does not lapse when staff or systems change.

Common misconceptions

Roles and responsibilities for AI governance and for model risk management are the same thing and can be assigned to a single function.

AI governance concerns organizational structures, policies, accountability, and oversight for AI systems, while model risk management concerns identifying, measuring, monitoring, and controlling risks arising from model use. They overlap but are distinct, and collapsing them into one role can undermine the independent challenge that risk management typically requires.

Assigning clear roles and responsibilities eliminates model risk.

Well-defined roles are a control that reduces and manages risk by ensuring accountability and independent oversight; they do not eliminate risk. Residual risk typically remains even where responsibilities are clearly allocated.

The three lines of defense are a strict hierarchy where higher lines simply supervise lower ones.

The lines are functionally distinct rather than a simple chain of command: the first line owns and takes the risk, the second line provides independent challenge and oversight, and the third line provides independent assurance. Treating them as a single reporting hierarchy erodes the independence each line is meant to preserve.

Best practices

Document accountability at the level of named roles or functions rather than leaving responsibility diffuse, distinguishing who owns, who challenges, and who assures each AI system.

Preserve independence between the first line (development and use), the second line (oversight and effective challenge), and the third line (assurance), and avoid assigning conflicting responsibilities to a single party.

Distinguish AI governance responsibilities (policy, oversight, accountability) from model risk management responsibilities (validation, monitoring, control), and map roles to each without conflating them.

Ensure role descriptions reflect the distinction between validation and verification, and between model risk and model performance degradation, so responsibilities are scoped precisely.

Align role definitions with the specific frameworks that apply to your jurisdiction and sector rather than assuming a single set of requirements applies universally, and use qualified language where obligations are guidance or voluntary standards rather than binding law.

Review and update role assignments as AI systems, use cases, and regulatory expectations evolve, treating role definitions as living controls rather than one-time allocations.