AI Ethics and Responsible AI Development
Description
Comprehensive study of ethical considerations, bias mitigation, fairness, transparency, and responsible practices in AI development and deployment.
Learning Objectives
Learners will understand fundamental principles of AI ethics and moral philosophy, identify and mitigate bias in AI systems and datasets, implement fairness and transparency measures in AI applications, evaluate societal impacts and implications of AI deployment, develop governance frameworks for responsible AI development, and apply ethical guidelines and regulatory compliance in AI projects.
Topics (8)
Societal impact assessment methodologies, AI for social good applications including healthcare accessibility, educational equity, environmental monitoring, and sustainable development goals through AI.
Philosophical foundations including utilitarian ethics, deontological ethics, virtue ethics, and their applications to AI systems, exploring moral agency, responsibility, and ethical decision-making in AI development.
Comprehensive study of bias types in AI including historical bias, representation bias, measurement bias, and algorithmic bias, with techniques for bias detection, measurement, and mitigation strategies.
Fairness concepts including individual fairness, group fairness, equalized odds, and demographic parity, along with explainable AI techniques and transparency measures for algorithmic accountability.
Privacy-preserving AI techniques including differential privacy, federated learning, homomorphic encryption, secure multi-party computation, and data protection strategies for AI applications.
AI governance including regulatory frameworks (GDPR, AI Act), industry standards, compliance requirements, risk assessment methodologies, and governance structures for responsible AI deployment.
Algorithmic auditing methodologies, accountability frameworks, continuous monitoring systems, impact assessment tools, and stakeholder engagement processes for responsible AI governance.
Human-centered AI design principles, inclusive design methodologies, accessibility considerations, user experience design for AI systems, and participatory design approaches for diverse user communities.