AI : Ethics & Biases

Welcome to the Sixth Track titled "AI : Ethics & Biases". This track emphasizes the importance of addressing ethics and bias in data and AI. It highlights various types of biases, such as sample, exclusion, measurement, recall, observer, racial, and association biases, with real-world examples. The track highlights the need for governance frameworks, diversity, and ethical practices in AI development. It includes case studies like YouTube's issue with left-handed users and Target's predictive analytics to illustrate the impact of bias and unethical data practices.