Fairness and Bias in AI
Bias in AI algorithms is a significant concern, as it can perpetuate unfairness and discrimination. In this subheading, students delve into the concepts of fairness, bias, and algorithmic accountability. They explore techniques for detecting and mitigating bias in AI models, including fairness metrics, bias-aware training, and explainable AI. Students discuss the importance of diverse and representative training data to ensure fairness and discuss ongoing research and initiatives aimed at addressing bias in AI.
Societal Impact and Regulation
This subheading focuses on the potential societal impact of AI and the need for appropriate regulation. Students explore the implications of AI technologies on employment, privacy, and social inequality. They discuss the role of policymakers, industry leaders, and researchers in shaping the responsible development and use of AI. Students gain insights into ongoing debates and discussions surrounding AI ethics and the challenges of creating regulatory frameworks that balance innovation and societal well-being.
AI Project Management
Successfully implementing AI projects requires effective project management. In this session, students learn about the key principles and methodologies for managing AI projects. They explore project planning, risk management, team collaboration, and stakeholder engagement.
