Human authority

Responsible AI in Practice

Responsible AI is not a disclaimer attached after deployment. It is expressed through choices about purpose, information, models, authority, evaluation, transparency and what intelligent systems are permitted to do.

Principles

Responsibility is expressed through design decisions

AFA’s existing responsible-AI principles remain the foundation, now extended to the full intelligence environment.

01

Purpose and necessity

Use AI where it produces a meaningful benefit and define the decision or workflow it supports.

02

Human accountability

Assign clear ownership for decisions, approvals, exceptions and escalation.

03

Information protection

Match data, knowledge and model access to the sensitivity and purpose of the workload.

04

Proportional risk

Apply stronger controls as the consequences, autonomy and information sensitivity increase.

05

Transparency and maturity

Describe systems and product maturity accurately; do not imply certainty or availability that does not exist.

06

Evaluation and monitoring

Test behaviour before use and monitor material changes in models, sources, prompts and workflows.

07

Inclusion and accessibility

Design experiences that can be understood and used by the people they are intended to serve.

08

Lifecycle responsibility

Govern the system through design, deployment, operation, change and retirement.

Authority

Define what the person is actually responsible for

Human oversight is not meaningful when a person is nominally “in the loop” but lacks the time, information or authority to intervene.

For agents and Physical AI, Operational Sovereignty adds a direct question: what is the system permitted to do without approval, and what must remain under human authority?

Operating framework

Turn principles into an operating system for responsibility

Document responsibilities that can survive beyond a pilot.

Purpose

Define the outcome and why AI is appropriate.

Boundaries

Define information, tools, models and actions that are allowed.

Evidence

Define evaluation, logging, provenance and documentation requirements.

Accountability

Define approval, escalation, change control and who can stop the system.

Turn principles into an operating framework

Discuss responsible AI