Guidance Note on Consumer Protection and the Responsible Adoption and Use of Artificial Intelligence and Machine Learning by Licensed Financial Institutions in the U.A.E.
This guidance note from the Central Bank of the UAE outlines principles and guidelines for licensed financial institutions regarding the consumer protection and responsible adoption of artificial intelligence and machine learning. It covers governance, fairness, transparency, data quality, continuous monitoring, and human oversight. The document aims to promote ethical AI use, mitigate risks, and ensure fair treatment of consumers in financial services.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This document is for licensed financial institutions in the UAE to guide their responsible development and deployment of AI technologies.
What the document contains
The document covers principles and guidelines for licensed financial institutions adopting artificial intelligence and machine learning, focusing on consumer protection, data quality, transparency, model monitoring, human oversight, and third-party risk management.
Specific statutory penalty amounts or fines for non-compliance are not detailed.
- Licensed financial institutions in the UAE must adopt a documented governance framework for AI commensurate with their operations.
- AI and ML systems must not result in discriminatory or manipulative outcomes against individuals or groups.
- Institutions should maintain transparency with customers regarding AI usage, especially for high-impact decisions, and offer plain-language disclosures.
- AI systems must operate under meaningful human oversight, categorized through human-in-the-loop, human-on-the-loop, or human-out-of-the-loop models.
- Institutions remain fully responsible for outsourced AI functions and must maintain robust third-party risk management.
1 Definitions 2 Governance and Accountability 3 Fairness/Non-Discrimination and Ethics 4 Transparency and Explainability 5 Data Quality, Privacy and Security 6 Continuous Monitoring and Review 7 Human Oversight and Consumer Protection 8 Integration with Existing Frameworks 9 Outsourcing and Third-Party Risk 10 Ethical Collaboration and Innovation
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
Senior management and the Board of Directors of LFIs should be responsible and accountable for AI and ML systems and outcomes, model selection/development, deployment, accountability
Section 2
screening score · 2026-10-23
Fairness
4/5Non-discrimination and equitable treatment
LFIs are expected to ensure that AI and ML systems do not result in discriminatory or manipulative outcomes against individuals or groups.
Section 3
screening score · 2026-10-23
Human Dignity
2/5Respect for the inherent worth of persons
AI and ML systems do not result in discriminatory or manipulative outcomes against individuals or groups.
Section 3
screening score · 2026-10-23
Human Oversight
4/5Humans remain in control of consequential decisions
LFIs should ensure that AI and ML systems operate under meaningful human oversight and judgement, particularly for decisions that have significant implications for consumers
Section 7
screening score · 2026-10-23
Privacy
4/5Protection of personal data and private life
LFIs should incorporate privacy‑by‑design and security‑by‑design principles into AI systems and maintain safeguards to protect data from unauthorised access or misuse.
Section 5
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
LFIs should be transparent with customers and relevant stakeholders about the use of AI, particularly in respect of high ‑impact decisions, and if they are communicating or interacting with an AI application.
Section 4
screening score · 2026-10-23
Enforcement
4/5Consequences and compliance mechanisms with real force
No AI system or application should be deployed or used if it is discriminatory or manipulative or develops as such post-deployment.
Section 3
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
AI deployed should be subject to periodic testing, i.e. once a year or each time a model is upgraded, materially changed or a new one is introduced, to identify and remediate undue/unintended embedded biases or discriminatory outcomes.
Section 3
screening score · 2026-10-23