AI Ethics & Governance

This course is designed to provide a comprehensive understanding of the ethical considerations and governance frameworks associated with the development and deployment of AI systems in the Healthcare & Life Sciences industries. Participants will learn about the potential risks and challenges of AI, such as bias, transparency, accountability, and privacy, and explore strategies for ensuring the responsible and ethical use of AI technologies. The course covers best practices, industry standards, and regulatory requirements for AI governance, enabling participants to develop and implement effective policies and procedures for managing AI projects in the healthcare and life sciences domains.

• Understand the ethical implications and potential risks of AI in the Healthcare & Life Sciences industries

• Identify and mitigate bias, fairness, and transparency issues in AI systems

• Develop and implement AI governance frameworks and policies aligned with industry standards and best practices

• Apply risk assessment and management techniques for AI projects in the healthcare and life sciences domains

• Ensure compliance with relevant regulations and guidelines for responsible AI development and deployment

1. Introduction to AI Ethics and Governance

• Overview of AI ethics and governance and their importance in the Healthcare & Life Sciences industries

• Potential risks and challenges of AI (e.g., bias, transparency, accountability, privacy)

• Ethical principles and frameworks for responsible AI development and deployment

• Case studies of ethical AI failures and successes in the healthcare and life sciences domains

• Hands-on exercises: Analyzing ethical dilemmas in AI projects for healthcare and life sciences applications

2. Bias, Fairness, and Transparency in AI Systems

• Types of bias in AI systems (e.g., algorithmic bias, data bias, societal bias)

• Techniques for identifying and mitigating bias in AI models and datasets

• Fairness metrics and evaluation techniques for ensuring equitable AI outcomes

• Transparency and explainability techniques for building trust in AI systems

• Hands-on exercises: Assessing and mitigating bias in an AI model for a healthcare or life sciences use case

3. AI Governance Frameworks and Policies

• Overview of AI governance frameworks and their components

• Industry standards and best practices for AI governance (e.g., IEEE, ISO, FDA, EMA)

• Developing and implementing AI governance policies for the Healthcare & Life Sciences industries

• Roles and responsibilities of AI governance teams and stakeholders

• Hands-on exercises: Drafting an AI governance policy for a healthcare or life sciences organization

4. Risk Assessment and Management for AI Projects

• Identifying and assessing risks associated with AI projects in the Healthcare & Life Sciences industries

• Risk management strategies and mitigation techniques for AI systems

• Monitoring and auditing AI systems for ongoing risk assessment and compliance

• Incident response and crisis management planning for AI failures

• Hands-on exercises: Conducting a risk assessment for an AI project in the healthcare or life sciences domain

5. Regulations, Compliance, and Ethical AI Deployment

• Overview of relevant regulations and guidelines for AI development and deployment (e.g., GDPR, HIPAA, FDA, EMA)

• Ensuring compliance with data protection and privacy regulations in AI projects

• Ethical considerations for AI deployment in the Healthcare & Life Sciences industries (e.g., patient safety, clinical trials, drug discovery)

• Strategies for building a culture of responsible AI and ethical decision-making

• Hands-on exercises: Developing a compliance checklist for deploying an AI system in the healthcare or life sciences industry

• Basic understanding of AI concepts and technologies

• Familiarity with the Healthcare & Life Sciences industries and their operations

• Knowledge of project management and risk assessment principles is beneficial but not required