Artificial intelligence has rapidly evolved into a core enterprise capability that influences lending decisions, hiring, marketing, healthcare, cybersecurity, supply chains, customer service, fraud detection, product recommendations, autonomous systems, and executive decision-making. As organizations increasingly depend on AI-driven systems, executive leaders must ensure these technologies operate responsibly, transparently, securely, legally, and in alignment with organizational values. AI governance has therefore become a strategic business function that extends far beyond technical implementation, encompassing corporate governance, enterprise risk management, regulatory compliance, ethics, cybersecurity, operational oversight, and stakeholder trust.
This advanced course provides a practical, executive-oriented study of Responsible AI and Algorithmic Governance. Rather than focusing on programming algorithms or machine learning development, the course examines how executives design governance frameworks, oversee AI deployment, manage organizational risks, establish accountability structures, evaluate third-party AI systems, and ensure that AI creates sustainable business value while protecting customers, employees, investors, regulators, and society. Students will learn how to build enterprise AI governance programs that balance innovation with compliance, operational performance, security, transparency, and long-term resilience.
Throughout the course, emphasis is placed on practical governance frameworks used by Chief Executive Officers (CEOs), Chief Artificial Intelligence Officers (CAIOs), Chief Information Officers (CIOs), Chief Data Officers (CDOs), Chief Risk Officers (CROs), Chief Compliance Officers (CCOs), Chief Information Security Officers (CISOs), boards of directors, regulators, consulting firms, financial institutions, healthcare organizations, manufacturers, technology companies, retailers, multinational corporations, and public-sector organizations. Students will develop executive-level capabilities required to govern AI responsibly while supporting innovation, organizational trust, regulatory readiness, and sustainable enterprise growth.
Course Objectives
By the end of this course, students will be able to:
• Design enterprise AI governance frameworks aligned with corporate strategy.
• Evaluate organizational risks associated with AI systems throughout their lifecycle.
• Establish governance structures for responsible AI development, procurement, deployment, monitoring, and retirement.
• Assess AI systems for transparency, explainability, accountability, fairness, reliability, privacy, and security.
• Integrate AI governance into enterprise risk management, compliance, cybersecurity, and corporate governance.
• Evaluate third-party AI vendors using governance and operational criteria.
• Measure organizational performance and value generated through responsible AI initiatives.
• Lead cross-functional AI governance programs across global enterprises.
• Build organizational cultures that support ethical, transparent, and accountable AI use.
• Apply executive leadership principles to enterprise-wide algorithmic governance.
