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Exploring AI Governance and Enterprise Risk Management:
AI governance refers to the frameworks, policies, and procedures that organizations implement to ensure the ethical, transparent, and accountable use of AI technologies. It encompasses a range of practices aimed at aligning AI development and deployment with societal values and regulatory requirements. Key components of AI governance include:
Ethical Guidelines
Regulatory Compliance
Stakeholder Engagement
Oversight Mechanisms
AI Risk Management
These guidelines can establish principles that guide the ethical use of AI. Appropriate compliance can aid in adhering to laws and regulations that govern AI technologies. Furthermore, adequate stakeholder engagement can help to involve diverse stakeholders, including employees, customers, and regulators, in the AI decision-making process. Oversight mechanisms can help implement monitoring and auditing processes to ensure AI systems operate as intended and do not cause harm. Overall, AI Risk Management will be key in identifying, assessing, and mitigating the risks associated with AI technologies.
AI Risks
As our understanding of AI technologies and related risks evolves, new risks will continue to emerge as practices evolve and AI use cases continue to expand. Some common types of AI risks include:
Operational Risks
Ethical Risks
Data Privacy Risks
Security Risks
Malware Generation Risks
Misinformation and Disinformation Risks
Hallucination Risks
Compliance Risks
Intellectual Property Risks
Environmental Risks
Lack of Accountability
Algorithmic Biases
Lack of Transparency and Explain-ability
Model Dependency Risks
Training Data
Adversarial Attacks
Acquiring and retaining talent
Regulatory Compliance
AI Costs
Partnerships and Alliances
Sources include DelCreo’s Newsletter, Responsible AI, NIST Generative AI Risk Framework, IBM AI Risk Management blog, Robust Intelligence’s resources on AI Risk Management, and Splunk’s articles on AI Risk Management.
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