Ethical AI Governance and Compliance#401224

• Analyse the impact of future trends on the development and regulation of AI, including emerging ethical challenges and potential societal disruptions.
• Identify key principles and frameworks for promoting ethical AI development and deployment, considering accountability, transparency, and fairness.
• Understand the potential risks and societal impacts of AI, including bias, algorithmic discrimination, and job displacement.
• Explore specific ethical concerns related to data privacy, security, and ownership in the context of AI applications.
• Develop strategies for mitigating bias in AI algorithms and datasets, ensuring fair and non-discriminatory decision-making.
• Analyse existing and emerging legal frameworks and regulations governing AI, including national and international initiatives.
• Develop and implement an ethical AI governance framework within their organizations, considering industry best practices and compliance measures.
• Build a personalized action plan outlining steps to champion ethical AI practices, advocate for sound regulations, and ensure ongoing compliance within their roles or organizations.

• Policymakers and government officials responsible for developing regulations and frameworks governing AI development and deployment.
• Business leaders, executives, and technologists interested in ensuring responsible AI integration within their organizations.
• Data scientists, engineers, and AI developers seeking to understand and implement ethical considerations in their work.
• Legal professionals and compliance specialists navigating the legal landscape surrounding AI technology.
• Anyone interested in understanding the challenges and opportunities of ethical AI governance and future trends in compliance.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Future of AI & Ethical Considerations
• Welcome and program overview.
• The Ethics of AI: A Call for Responsible Development: Exploring the potential benefits and risks of AI, and the need for ethical considerations in design, development, and use.
• Future Studies for Ethical AI: Learning how to incorporate future studies methodologies like scenario planning to anticipate potential ethical dilemmas and societal impacts of advanced AI.
• The Global Landscape of AI Regulation: Discussing existing and proposed international regulations for AI governance, highlighting emerging trends and challenges.
• Guest Speaker: An AI ethics expert, policymaker, or leader who champions responsible AI practices can be invited to share their insights and answer participant questions.
Day 2: Bias, Fairness, and Transparency in AI
• Understanding Bias in AI Algorithms & Datasets: Exploring how bias can be introduced into AI systems through data collection, model training, and design choices.
• Mitigating Bias in AI Development: Discussing strategies for identifying and mitigating bias in AI algorithms and datasets, promoting fairness in decision-making.
• The Importance of Transparency and Explainability: Learning about the importance of transparency and explainability in AI processes, fostering trust and accountability.
• Hands-on Workshop: analysing Bias in AI Datasets (Optional): Participants can engage in a practical session using open-source tools to analyse sample datasets and identify potential biases that could impact AI algorithms.
Day 3: AI & Data Privacy, Security, and Ownership
• The Right to Privacy in the Age of AI: Discussing the implications of AI on data privacy rights, considering issues like data collection, surveillance, and user consent.
• Data Security Challenges and Solutions in AI: Exploring the importance of robust data security measures to protect sensitive information used in AI systems.
• Data Ownership and Control in AI Applications: Addressing questions of data ownership and control in the context of AI development and deployment, considering individual and organizational rights.
• Case Study: analysing a real-world example of an AI application that encountered ethical challenges related to data privacy or security, discussing lessons learned and best practices.
Day 4: Legal Frameworks & Governance of AI
• Existing Legal Frameworks for AI: Delving into national and international legal frameworks governing AI development, deployment, and use.
• Emerging Regulations and Policy Initiatives: Discussing proposed regulations and policy initiatives on AI governance, focusing on key areas like algorithmic transparency and accountability.
• The Role of Standards and Best Practices: Exploring the importance of industry standards and best practices in ethical AI development and compliance.
• Developing an Ethical AI Governance Framework: Participants work in groups to develop a sample ethical AI governance framework for a hypothetical organization, considering key principles and implementation strategies.
Day 5: The Future of AI Compliance & Action Planning
• The Future of Work & AI: Implications for Ethical Governance: Discussing the impact of AI on employment and the need for ethical considerations related to job displacement and skill development.
• Fostering a Culture of Responsible AI: Learning strategies for promoting ethical AI practices within organizations and advocating for responsible development and use.
• Building Your AI Compliance Action Plan: Participants create personalized action plans outlining steps to champion ethical AI practices, advocate for sound regulations, and ensure ongoing compliance within their roles or organizations. This may include:
o Identifying key ethical considerations and potential risks within their specific work area related to AI.
o Researching existing regulations and best practices for ethical AI development and compliance.
o Advocating for the implementation of an ethical AI governance framework within their organization.
o Developing training programs and awareness initiatives to educate colleagues on ethical AI principles.
o Monitoring regulatory developments and adapting compliance strategies as needed.
• Course Wrap-Up & Ongoing Learning: Reviewing key takeaways from the program, addressing any remaining questions, and discussing ongoing resources for staying informed about advancements in AI, ethical considerations, and the evolving regulatory landscape.
• Networking & Collaboration: Participants engage in a facilitated discussion to share their AI compliance action plans and explore collaborative efforts to promote responsible AI development and effective governance across different sectors.