Ethical risk analysis
Apply established AI ethics frameworks covering fairness, accountability, transparency, and privacy principles systematically to all organizational AI development and deployment decisions
Bias audits
Conduct quantitative bias audits on AI systems using established fairness metrics to detect, document, and address discriminatory patterns in model outputs and decisions
Consent governance
Design ethical AI review processes that evaluate proposed AI projects rigorously against organizational values, legal requirements, and potential societal impact considerations
Transparency standards
Develop comprehensive AI ethics policies and codes of professional conduct that guide responsible AI development and deployment consistently organization-wide
Compliance reporting
Assess privacy risks embedded in AI systems including data minimization requirements, consent mechanism design, and data subject rights compliance obligations
Incident handling
Facilitate AI ethics training programs that build meaningful organizational awareness of AI risks, professional responsibilities, and ethical decision-making capabilities
Policy development
Investigate AI ethics incidents including bias complaints, harmful output reports, and unintended consequences using structured, impartial root cause analysis methodology
Staff training
Monitor deployed AI systems continuously after release to detect ethical failures, fairness drift, and unintended societal harms in live production environments
Public accountability
Engage affected communities, regulatory bodies, and advocacy groups in AI impact assessment processes to ensure inclusive and representative AI development
Continuous monitoring
Maintain current knowledge of AI regulation developments including the EU AI Act, GDPR implications for AI systems, and sector-specific AI governance frameworks