> For the complete documentation index, see [llms.txt](https://policies.aic.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://policies.aic.io/ethical-technology-and-responsible-ai-policy.md).

# Ethical Technology and Responsible AI Policy

### Purpose

AIC develops and delivers technology in environments where trust, security, fairness and accountability matter.

This policy defines the minimum ethical standard for technology and artificial intelligence work.

### Policy Statement

AIC will seek to design, build and use technology responsibly.

Technology should be lawful, secure, proportionate, explainable where appropriate, and aligned to the intended purpose.

### Responsible Technology Principles

AIC technology work should consider:

* legality
* security
* privacy
* accessibility
* fairness
* explainability
* misuse risk
* human oversight
* operational resilience
* auditability
* proportionality

### Responsible AI

Where AI is used, teams should consider:

* purpose and intended use
* data quality
* bias risk
* privacy impact
* security risk
* explainability
* human oversight
* confidence and limitations
* audit logs
* misuse potential
* customer approval requirements

### Prohibited or Restricted Use

AIC will not knowingly support technology use that is unlawful, abusive or intended to cause unjustified harm.

Restricted use cases require senior review and may require customer, legal, security or ethics approval.

### Human Oversight

AI-generated outputs should not be treated as automatically correct.

Where AI affects important decisions, there should be appropriate human oversight.

### Data Protection

AI and technology delivery must respect applicable data protection requirements.

Teams should avoid unnecessary collection, retention or exposure of personal data.

### Security

Systems must be designed with appropriate security controls.

This includes:

* access control
* logging
* monitoring
* secure configuration
* vulnerability management
* data protection
* resilience
* supplier assurance

### Bias and Fairness

Where systems may affect people, teams should consider whether outputs could create unfair or discriminatory outcomes.

Where risk exists, controls should be defined and reviewed.

### Evidence

Projects should record relevant decisions, risks and controls.

Evidence may include:

* architecture decisions
* AI use case assessment
* data protection assessment
* threat model
* bias or fairness review
* security testing
* approval records

### Review

Responsible technology controls should be reviewed as systems, risks and laws evolve.


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