In the rapidly evolving computing technology landscape, ethical issues arise at every stage of the data lifecycle, from collection to downstream predictive analytics, including but not limited to pre-existing bias, privacy, fairness, and accountability of machine learning and artificial intelligence algorithms. Data ethics education for students in Computer Science and related STEM programs has become a focal point of discussion and innovation. This lightning talk describes the current effort to build a system, DEEILS, that allows students to learn about ethical issues at each stage of the data lifecycle through multi-media interactive modules and real-world scenario simulations. DEEILS also supports instructors with customized components for different levels of courses.

DEEILS consists of three phases: Collection, Preparation, and Analytics, which represent the stages of how data is created and evolves through its lifecycle. Within each module, DEEILS integrates components to simulate the steps that data practitioners take in real-world scenarios, such as cleaning, de-identification, and feature engineering in the Preparation phase. It then guides students through the ethical issues that can arise in each component, using examples of real datasets and commonly used computational techniques for that component. Through interactive content with real-world applications, DEEILS aims to provide an adaptive, immersive learning environment to facilitate education on ethical issues in data-driven science.