Introduction to the course

The AI and Ethics, Governance, and Regulation course provides a practical and policy-focused understanding of how ethical principles, governance frameworks, and global regulations shape the development and deployment of artificial intelligence. This course explores how AI ethics and governance are applied across industries to ensure fairness, transparency, accountability, and compliance with evolving laws. Designed for professionals, students, researchers, and career switchers, it delivers real-world relevance by preparing learners to build, manage, and govern responsible AI systems in today’s regulatory landscape.

Course Objectives

• Understand the core principles of AI ethics and responsible AI development
• Learn global AI governance frameworks and regulatory standards
• Gain practical knowledge of ethical risk assessment in AI systems
• Master compliance strategies for AI laws and policies
• Explore real-world case studies involving AI misuse and bias
• Apply ethical and governance best practices to AI projects

What Will You Learn Modules

Module 1: Introduction to AI and Ethics, Governance, and Regulation

• Overview of AI concepts relevant to pharmacy and healthcare.
• Understanding pharmacy data sources and digital workflows.
• Real-world examples of AI adoption in pharmacy settings.

Module 2: Foundations of AI Ethics

• Core ethical principles: fairness, accountability, transparency, and privacy.
• Understanding bias and discrimination in AI systems.
• Human-centered and value-driven AI design concepts.

Module 3: Bias, Fairness, and Explainability in AI

• Sources of bias in data, models, and decision-making processes.
• Techniques for fairness assessment and bias mitigation.
• Explainable AI (XAI) concepts for improving transparency and trust.

Module 4: AI in Formulation and Manufacturing

• Using AI to optimize drug formulations.
• Quality monitoring and process optimization concepts.
• Predictive maintenance in pharmaceutical manufacturing.

Module 5: AI in Clinical Trials and Evidence Generation

• Patient recruitment and trial optimization using AI.
• Analyzing trial data and real-world evidence.
• Post-marketing data insights for decision-making.

Module 6: Pharmacovigilance and Drug Safety Analytics

• AI-supported adverse event detection workflows.
• Text analysis for safety reports and literature review.
• Signal detection and risk prioritization concepts.

Module 7: AI in Clinical Pharmacy and Patient Support

• Medication adherence monitoring and support tools.
• Clinical decision support concepts for pharmacists.
• Patient engagement and education through AI systems.

Module 8: AI in Pharmacy Operations and Supply Chain

• Inventory optimization and demand forecasting.
• Reducing wastage and improving supply reliability.
• Monitoring logistics and distribution efficiency.

Module 9: Ethics, Privacy, and Responsible AI

• Patient data protection and regulatory compliance.
• Bias, fairness, and transparency in healthcare AI.
• Safe deployment and validation practices.

Final Project

• Design a responsible AI solution addressing ethical, governance, or regulatory challenges.
• Define risk factors, compliance requirements, and mitigation strategies.
• Example projects include AI bias assessment frameworks, governance policy design, or AI compliance checklists.

Who Should take this Course

• Pharmacy students and practicing pharmacists.
• Researchers in pharmaceutical sciences and healthcare analytics.
• Industry professionals in clinical trials, safety, and pharma operations.
• Healthcare and AI learners interested in pharmacy applications.

Job Opportunities

• Hospitals and healthcare systems.
• Pharmaceutical and biotechnology companies.
• Healthcare technology startups.
• Retail and hospital pharmacy chains.
• Contract research and safety organizations.

Why Learn With Nano School

• Expert-led training from AI ethics and policy professionals
• Practical, hands-on learning with real-world case studies
• Industry-relevant curriculum aligned with global AI regulations
• Career support to help you transition into AI governance roles

Key Outcomes of the Course

• Ability to identify and apply AI solutions in pharmacy workflows.
• Understanding of data-driven decision-making in pharmaceutical settings.
• Awareness of ethical and regulatory requirements for healthcare AI.
• Practical readiness to contribute to AI-enabled pharmacy initiatives.