ISBE

Responsible AI and Governance:

Navigating Risk, Compliance, and Innovation

Program Duration: 2 - 3 Days
Can be delivered on-site, remotely via Zoom, or in a hybrid mode

As artificial intelligence (AI) technologies rapidly evolve and integrate into business operations, the imperative for robust AI governance, risk management, and compliance frameworks becomes critical. This course provides an in-depth exploration of how organizations can implement responsible AI practices that not only align with regulatory standards but also foster ethical innovation and public trust.

Participants will examine the multifaceted challenges organizations face in governing AI systems, including issues related to bias, accountability, transparency, and data privacy. By analyzing case studies from industry leaders such as Google, Mastercard, OpenAI, and Alibaba, learners will gain insights into best practices for mitigating risks associated with AI deployment while ensuring compliance with emerging regulations, including GDPR, FDA, HIPAA, SOX, AML, CCPA, NIST RMF, ISO 42001, and the EU AI Act.

Utilizing a risk-based approach, this course equips participants with the tools and frameworks necessary to develop and implement effective AI governance strategies. The curriculum will cover key topics such as ethical AI principles, risk assessment methodologies, compliance strategies, and stakeholder engagement. Through engaging discussions, practical exercises, and real-world case studies, participants will learn how to navigate the complexities of AI governance and position their organizations for long-term success.

In addition, the course will emphasize the importance of fostering a culture of responsibility within organizations by instilling ethical considerations and risk awareness throughout the AI lifecycle. Participants will learn how to create and operationalize governance frameworks that promote responsible AI use while balancing innovation and compliance.

By the end of this course, leaders and decision-makers will be equipped to address the unique challenges posed by AI technologies, ensuring that their organizations not only thrive in the age of digital transformation but do so with integrity and accountability. Emphasizing practical strategies for proactive risk management and compliance, this course is essential for anyone looking to lead in a world increasingly defined by artificial intelligence and its societal implications.

Key Topics

AI Governance Frameworks: Explore the principles and frameworks that underpin effective AI governance, focusing on the roles of oversight, accountability, and stakeholder engagement in ensuring responsible AI deployment.
Navigating Regulatory Landscapes: Understand current and emerging regulations affecting AI technologies, including GDPR, CCPA, NIST RMF, ISO 42001, the EU AI Act, and more. Learn how organizations can stay compliant while fostering innovation.
Google’s Three-Tiered Internal AI Principles Ecosystem Framework: Investigate Google's approach to AI governance through its structured principles framework, understanding how it informs ethical decision-making and operational practices.
Mastercard’s Ethical Approach to Governing AI: Analyze Mastercard's model for ethical AI governance, focusing on their strategies for ensuring fairness, accountability, and transparency in AI applications.
NIST Risk Management Framework: Understand the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF), exploring its application in identifying, assessing, and managing risks associated with AI systems.
GAIX's Proprietary AI GRC Assessment Model: Delve into GAIX’s approach to governance, risk management, and compliance (GRC) in AI, examining how their proprietary assessment model can help organizations evaluate and enhance their AI governance practices.
Establishing Responsible AI Practices: Delve into best practices for developing and implementing responsible AI policies that mitigate bias, enhance transparency, and promote ethical usage across an organization.
Risk Management in AI Deployment: Identify potential risks associated with AI systems, including operational, reputational, and legal risks. Learn methodologies for assessing and mitigating these risks effectively.
Building an Ethical AI Framework: Create a robust framework for ethical AI practices tailored to your organization’s needs, emphasizing the importance of diversity, fairness, and inclusivity in AI development and deployment.
Stakeholder Engagement and Collaboration: Understand the importance of engaging stakeholders—including employees, customers, and regulators—in the governance process. Explore collaborative approaches to building trust and ensuring accountability.
Data Privacy and Security in AI: Examine the critical aspects of data governance, including privacy, security, and compliance. Learn strategies for managing data responsibly, particularly in AI training and deployment.
Monitoring and Auditing AI Systems: Discover methodologies for continuous monitoring and auditing of AI systems to ensure compliance with governance frameworks and effectiveness in achieving desired outcomes.
Crisis Management and AI: Develop strategies for managing crises related to AI failures or ethical breaches, emphasizing proactive communication and damage control to maintain stakeholder trust.
The Future of AI Governance: Explore emerging trends in AI governance, including advances in technology, evolving regulatory requirements, and the rise of ethical considerations amid rapid AI advancements.
Case Studies of Industry Giants: Analyze how companies like Google, Mastercard, OpenAI, and others effectively deploy the AI Governance models to manage risk and regulatory compliance.

Key Benefits and Expected Outcomes

After attending this program, participants will be able to:

Enhanced Understanding of AI Governance: Participants will gain a foundational understanding of AI governance principles, frameworks, and best practices, empowering them to lead AI initiatives responsibly within their organizations.
Comprehensive Regulatory Knowledge: Learners will become familiar with current and emerging regulations affecting AI, equipping them to navigate compliance landscapes confidently and avoid potential legal pitfalls.
Development of Responsible AI Practices: Participants will learn how to create and implement ethical AI practices that mitigate bias and foster transparency, ensuring AI systems are used responsibly and fairly.
Effective Risk Management Skills: Attendees will acquire skills to identify, assess, and mitigate risks associated with AI deployment, increasing their ability to safeguard their organizations against operational and reputational threats.
Creation of Ethical AI Frameworks: Participants will have the tools to develop tailored ethical AI frameworks that promote diversity, fairness, and inclusivity in AI operations, enhancing overall corporate social responsibility.
Improved Stakeholder Engagement: Learners will understand how to effectively engage stakeholders in the governance process, fostering collaboration and trust, which are crucial for successful AI implementation.
Proficiency in Data Governance: Participants will gain insights into data privacy and security best practices, ensuring responsible data management in AI applications that comply with regulations and enhance trust.
Monitoring and Auditing Capabilities: Learners will develop the ability to monitor and audit AI systems effectively, ensuring compliance with established governance frameworks and enhancing the integrity of AI outputs.
Crisis Management Preparedness: Participants will leave with strategies for managing potential crises related to AI failures or ethical breaches, enabling proactive responses to build and maintain stakeholder confidence.
Awareness of Future Trends: Attendees will explore emerging trends in AI governance, preparing them to adapt to evolving technologies and regulatory changes, thereby positioning themselves and their organizations for long-term success.
Industry-Specific Insights: Participants will benefit from case studies and frameworks from industry leaders like Google, Mastercard, and GAIX, providing practical insights and models that can be applied to their own organizations.
Practical Applications of Theoretical Concepts: By engaging in discussions and exercises, learners will have the opportunity to apply theoretical concepts to real-world scenarios, enhancing their problem-solving and critical-thinking skills.

PROGRAM FACULTY

Dr. Rizwan Amin Sheikh (“Dr. Riz”)

Founder GAIX and Professor of AI Strategy, Leadership, and Governance

Dr. Riz is the Founder & CEO of Global AI Excellence (GAIX). He is an AI Strategist, AI governance leader, technologist, professor, author, and serial entrepreneur with over 30 years of experience specializing in business and AI strategy, digital transformations, AI governance, risk management, compliance, and AI/ML solutions. His expertise extends to project management, leadership development, and executive education at prestigious institutions, including Harvard, MIT, and the University of Cambridge. As a former Deloitte executive and management consultant, Dr. Riz has empowered the public sector and Fortune 500 companies—including PepsiCo, AT&T, Merrill Lynch, Progressive Insurance, the German Hospitals Group, STERIS Corporation (life sciences and biotech), PARCO (Oil & Gas), and Eaton Corporation—to transform their visions into reality. Moreover, he is an AWS Certified AI Practitioner and AI Early Adopter.

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Target Audience for Program

Executives and Senior Leaders: C-suite executives and senior leadership teams responsible for strategy and governance, including Chief Technology Officers (CTOs), Chief Information Officers (CIOs), Chief Compliance Officers (CCOs), and Chief Risk Officers (CROs).
AI and Data Governance Professionals: Individuals working in roles focused on AI governance, data privacy, risk management, and compliance, such as Data Protection Officers and Governance, Risk, and Compliance (GRC) specialists.
Regulatory Compliance Officers: Professionals tasked with ensuring compliance with laws and regulations governing AI use, such as privacy laws, data protection regulations, and industry-specific compliance frameworks.
AI and Machine Learning Practitioners: Data scientists, AI engineers, and machine learning practitioners interested in understanding the governance, ethical implications, and risk management of AI technologies in their work.
Policy Makers and Regulators: Individuals involved in developing, implementing, and enforcing policies and regulations related to AI technology, including government officials, regulatory agency personnel, and industry advocates.
Risk Management Professionals: Risk analysts, auditors, and compliance managers focused on identifying, assessing, and mitigating risks associated with AI technologies and practices.
Ethics and Corporate Social Responsibility (CSR) Leaders: Professionals tasked with ensuring ethical practices and fostering corporate responsibility, particularly within the context of emerging technologies and AI.
Legal Professionals: Lawyers and legal advisors specializing in technology law, data privacy, and intellectual property, who need to understand the implications of AI on legal compliance and ethical standards.
Consultants and Advisors: Management consultants, technology advisors, and external auditors who provide guidance on AI governance, risk management, and compliance strategies to their clients.
Academics and Researchers: Scholars and researchers studying AI ethics, governance, and compliance frameworks who seek practical insights and applications that inform their work.
Business Strategists: Professionals involved in creating and executing business strategies that involve AI implementation and require an understanding of governance and compliance factors.

Teaching Methodology

Group discussions
Interactive Classroom Sessions
GAIX’s Hands-on AI GRC Assessment Model to calculate your organization’s AI Risk Score.
Real-life case studies from Harvard Business School
Role-play exercises
Video-conferencing with faculty from the University of Cambridge, Imperial College London, RMIT (Australia), and others
Harvard Business School and/or INSEAD Business School Simulation