Ethical Development and Responsible Deployment of AI and ML Systems
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we live, work, and interact with technology. With these advances, however, come significant risks: adversarial attacks, data misuse, privacy violations, and ethical concerns that can undermine trust and security. This course equips learners with the knowledge and skills to design, build, and deploy AI and ML systems that are secure, responsible, and aligned with evolving privacy, legal, and ethical standards.
You will explore the cutting edge of AI/ML security, gain practical knowledge to defend against AI-powered threats, and learn how to integrate ethical and privacy-first design into real-world applications. Through examples using tools such as ChatGPT, GitHub Copilot, DALL·E, MidJourney, and Stable Diffusion, you’ll see how these issues impact daily practice.
With expert guidance, this course blends foundational theory with modern applications to ensure you’re prepared to build AI systems that are not only innovative but also secure, trustworthy, and ethically responsible.
Course Requirements
None. A general familiarity with computing and an interest in AI are helpful.
Course Content
Introduction
The importance of security, ethics, and privacy in modern AI/ML development.
Understanding responsible AI frameworks and why they matter.
Module 1: Fundamentals of AI and ML
Module introduction: Laying the groundwork for secure and ethical AI development.
Lesson 1: Overview of AI and ML Implementations
Supervised, unsupervised, and reinforcement learning.
Practical applications and use cases across industries.
Preprocessing, feature engineering, and model preparation.
Key ML algorithms and their strengths/limitations.
Model evaluation, validation, and benchmarking.
Lesson 2: Generative AI and Large Language Models (LLMs)
Introduction to generative AI concepts.
Understanding LLMs and their transformative capabilities.
Common AI tools in everyday life (ChatGPT, MidJourney, LLaMA).
Beyond text: image, audio, and multimodal applications.
Ecosystems: Hugging Face, LangChain Hub, dataset/model-sharing platforms.
Modern model training environments.
LangChain frameworks: chains, templates, and agents.
Fine-tuning models with LoRA and QLoRA.
Retrieval-Augmented Generation (RAG) pipelines.
Module 2: AI and ML Security
Module introduction: Understanding AI as both a target and tool for cyber threats.
Lesson 3: Fundamentals of AI and ML Security
Why AI security is critical to modern organizations.
OWASP Top 10 risks for LLM applications.
Prompt injection and data poisoning attacks.
Risks of insecure output handling and plugin design.
Excessive autonomy and overreliance on AI systems.
Model theft and intellectual property risks.
Lesson 4: How Attackers Use AI to Perform Attacks
MITRE ATLAS framework and adversarial tactics.
AI supply chain risks and dependencies.
AI-driven vulnerability discovery and exploit automation.
OSINT, phishing, and social engineering powered by AI.
Deepfakes, synthetic media, and disinformation threats.
Dynamic obfuscation of attack vectors.
Lesson 5: AI System and Infrastructure Security
Secure AI/ML development best practices.
Monitoring, auditing, and logging AI operations.
Software Bill of Materials (SBOMs) and emerging AI BOMs.
Using CSAF and VEX for vulnerability management.
Module 3: Privacy and Ethical Considerations
Module introduction: Building trust by embedding ethics and privacy into AI systems.
Lesson 6: Privacy and AI Fundamentals
Privacy challenges unique to AI.
Bias, fairness, and inclusivity in AI/ML.
Transparency and accountability practices.
Differential privacy techniques.
Secure multi-party computation (SMPC).
Homomorphic encryption for secure computation.
AI data lifecycle management.
Federated learning and decentralized privacy-preserving AI.
Lesson 7: AI Ethics
Defining ethical AI in practice.
Responsible AI frameworks from industry and academia.
Policy frameworks guiding AI development.
Mitigating bias through design and governance.
Lesson 8: Legal and Regulatory Compliance
Overview of global AI regulations (EU AI Act, NIST AI RMF, etc.).
Strategies for compliance in AI/ML projects.
Case studies and industry best practices.
Summary
Recap of AI security threats and defense strategies.
Ethical and privacy-first design principles for responsible AI.
Preparing to build and deploy AI systems that are secure, compliant, and trustworthy.
👉 By the end of this course, you’ll have the knowledge, tools, and ethical foundation to ensure your AI and ML systems can withstand adversarial threats while meeting the highest standards of transparency, fairness, and responsibility.