Requirements

• Technical Foundation: A basic understanding of Machine Learning concepts and the AI development lifecycle is recommended to grasp advanced technical modules.,Programming Literacy: Familiarity with Python and common data science libraries like Scikit-Learn or TensorFlow will help when exploring mitigation tools.,Professional Context: Experience in software engineering
• data science
• or product management is helpful but not mandatory for understanding ethical theory.,Tools & Software: Access to a computer with an internet connection; we will explore open-source frameworks like AIF360
• LIME
• and SHAP.,Analytical Mindset: A willingness to think critically about the societal
• legal
• and moral consequences of automated decision-making systems.,Open to All: No prior ethics degree is required—I simplify complex philosophical frameworks into actionable engineering constraints for you.

Description

Who this course is for:

• Tech Leads & Architects: For those responsible for conducting high-level risk assessments and implementing "Ethics-by-Design" in enterprise systems.,Senior Developers: For engineers wanting to move beyond coding to master XAI tools like SHAP and lead "Privacy-by-Design" workflows.,AI & Data Scientists: For professionals needing to mathematically define fairness and mitigate bias within their training and evaluation pipelines.,Product Managers: For leaders who must balance performance with legal compliance (GDPR/EU AI Act) and maintain user trust in high-stakes fields.,Compliance Officers: For professionals seeking a deep dive into the technical realities of AI governance
• accountability
• and emerging global statutes.,Future-Minded Students: For anyone looking to gain "Ethical Literacy" and prepare for the shift from technical "doing" to responsible AI "directing."