Artificial Intelligence Models & Processes
Effective Date: Jan 2025
Review Cycle: Annually or as required
1. Purpose and Scope
This policy outlines Karbon Digital Limited’s commitment to the responsible design, development, deployment, and management of Artificial Intelligence (AI) technologies. It ensures AI use aligns with ethical standards, data privacy laws, and corporate responsibility goals. The policy applies to all employees, contractors, vendors, and partners working with or developing AI systems for or with Karbon Digital Limited.
2. Ethical Use and Purpose Limitation
AI must be used in ways that are consistent with the company’s mission and values.
Prohibited applications include:
Generating legally binding contracts or advice (medical, legal, or financial).
Political messaging, voter manipulation, or election interference.
Creation of harmful content (spam, malware, deepfakes, hate speech).
Any form of surveillance without lawful basis or consent.
All AI use cases must undergo a formal approval and risk assessment process before implementation.
3. Data Privacy and Protection
AI systems must comply with applicable data protection regulations such as GDPR, PIPEDA (Canada), and similar laws in jurisdictions where Karbon Digital operates.
All personal data must be anonymized or pseudonymized unless explicit consent is provided.
A “Privacy-by-Design” principle must be applied to all AI projects from conception to deployment.
Encryption, access controls, and audit trails are mandatory components of AI data management.
4. Fairness, Inclusivity, and Non-Discrimination
Regular audits must be conducted to identify and eliminate algorithmic bias.
Training datasets should be diverse and representative of intended user populations.
Use of synthetic data should be carefully monitored to avoid replicating real-world inequalities.
AI must not discriminate based on age, gender, ethnicity, nationality, religion, disability, or sexual orientation.
5. Transparency and Explainability
Users must be clearly informed when they are interacting with AI systems (e.g., chatbots, decision engines).
All AI models deployed in critical functions (e.g., hiring, finance, healthcare, security) must be explainable.
Tools like LIME, SHAP, or DiCE should be used to interpret model predictions.
Where feasible, publish plain-language documentation of AI system capabilities and limitations.
6. Human Oversight and Accountability
AI systems shall not make final decisions in high-stakes scenarios (e.g., job applications, loan approvals) without human validation.
An AI Ethics Committee will:
Review high-risk AI proposals.
Approve model deployment for sensitive use cases.
Provide periodic compliance reports to the executive team.
Clear accountability must be established for every AI decision-making system and its outputs.
7. Compliance and Legal Framework
AI development must adhere to:
Canada’s Artificial Intelligence and Data Act (AIDA).
ISO/IEC 42001 (AI Management Systems Standard).
Industry codes (e.g., OECD AI Principles, IEEE Ethically Aligned Design).
Legal and compliance teams must be involved in early stages of AI development.
AI vendors and partners must be vetted for ethical compliance and data handling practices.
8. Environmental Sustainability
AI projects must account for their carbon footprint during planning.
Prefer cloud infrastructure that uses renewable energy or carbon-offset commitments.
Incorporate energy-efficient model architectures (e.g., quantized or distilled models).
Include environmental impact assessments in AI project proposals.
9. Training, Literacy, and Awareness
All employees involved in AI must complete annual training on:
Responsible AI use
Algorithmic bias
Privacy and security
Environmental impact
AI literacy sessions will be offered to broader teams to promote informed usage and awareness.
Managers must ensure their teams are familiar with relevant parts of this policy.
10. Feedback, Grievances, and Continuous Improvement
End-users must have a clear way to submit feedback or flag concerns about AI system behavior.
User feedback must be logged, analyzed, and acted upon within a defined SLA.
A continuous improvement loop should be implemented for all deployed AI models.
The policy will be reviewed annually and revised as necessary to reflect technological, legal, and societal changes.
For questions or feedback about this policy, contact the Compliance Team at info@karbondigital.com
