This course provides a comprehensive exploration of bias and fairness issues in artificial intelligence (AI) systems. Participants will engage with modules covering topics such as the introduction to bias and fairness in AI, types of bias, fairness metrics and evaluation, debiasing techniques, transparency and explainability, ethical considerations, legal and regulatory landscape, bias in data collection and preprocessing, bias in machine learning models, human-centric approaches, bias and fairness in natural language processing (NLP), AI governance, industry-specific challenges, and continuous improvement strategies.

Who This Course Is For:

This course is intended for data scientists, AI engineers, machine learning practitioners, policymakers, ethicists, and professionals working with AI technologies. It is also suitable for researchers, academics, and students interested in understanding and addressing bias and fairness issues in AI. Whether you are involved in AI development, deployment, governance, or regulation, this course equips you with the knowledge and tools necessary to promote fairness and mitigate bias in AI systems across various applications and industries.